[{"data":1,"prerenderedAt":3844},["ShallowReactive",2],{"computer-science-cybersecurity-mcs-grad-data":3,"header-nav":215,"rfi-data":347,"footer-nav-items":3725},{"data":4},{"programGraduate":5},{"id":6,"detailPage":7,"code":8,"programCode":9,"title":10,"alternateTitle":11,"shortDescription":12,"longDescriptionTitle":13,"longDescription":14,"weeksPerClass":15,"totalClasses":16,"totalCreditHours":17,"nextStartDate":18,"applyNowUrl":19,"metaTagTitle":20,"metaTagDescription":21,"prestigiousFacultyTitle":22,"prestigiousFacultyDescription":23,"facultyImage":24,"videoTitle":25,"videoUrl":26,"degreeImage":27,"degreeImageAlt":28,"category":29,"campusLink":32,"featuredCoursesTitle":33,"featuredCoursesDescription":34,"cmsCourseItems":35,"relatedCareersTitle":82,"relatedCareersDescription":83,"relatedCareersImage":84,"relatedCareersImageAlt":85,"cmsCareerExamples":86,"admissionRequirementTitle":97,"admissionRequirementDescription":98,"admissionRequirementItems":99,"accoladesTitle":112,"accoladesDescription":113,"accoladeItems":114,"faqTitle":136,"faqDescription":32,"faqItems":137,"customBlockTitle":110,"customBlockDescription":180,"customBlockImage":181,"customBlockImageAlt":182,"costOverride":183,"costAcademicYear":184,"latestAcademicYear":184,"resProgramFeeCode":185,"noresProgramFeeCode":185,"costCollection":186,"interestedPrograms":187,"diplomaTitle":213,"diplomaDescription":214,"webinar":32},"2c5fde24-8dfa-413e-a3dc-0224a0bf2063","/online-degree-programs/graduate/computer-science-cybersecurity-mcs/","GRES-ESCSEIMCS","GRES","Computer Science – Cybersecurity (MCS)","Online Master of Computer Science – Cybersecurity","\u003Cp>Arizona State University’s online Master of Computer Science with a concentration in cybersecurity program helps you develop the skills needed to understand information assurance and security problems, as well as come up with potential solutions. Upon graduation, you’ll be prepared to work in a variety of in-demand positions, including cybersecurity engineer, cybersecurity risk analyst, software development engineer, security engineer and security analyst.\u003C/p>\n","Prepare for in-demand jobs in cybersecurity","\u003Cp>Cybersecurity is not only an important industry, but it’s also a lucrative one. This program prepares you for career advancement and increased pay, which is projected to continue growing for years to come. According to an estimate by the U.S. Bureau of Labor Statistics, the global cybersecurity market is projected to grow 86%, from around $145 billion in 2018 to $270 billion by 2026.\u003C/p>\u003Ch2 class=\"h2-small mb-space-xs\">What you’ll learn in this cybersecurity master’s concentration\u003C/h2>\u003Cp>In this program, you’ll learn how to protect and defend information and information systems by ensuring their availability, integrity, authentication, confidentiality and non-repudiation through protection, detection and reaction practices. You’ll also learn how to analyze and apply key theories, algorithms and software modules used in the field of computer science. Upon graduation, you’ll feel comfortable evaluating and advancing existing theories, algorithms and software modules in the field of computer science.\u003C/p>\u003Ch2 class=\"h2-small mb-space-xs\">What’s a concentration and how is this different from a master’s in cybersecurity?\u003C/h2>\u003Cp>This cybersecurity master’s concentration is designed for graduate students looking to pursue a thorough education in the area of cybersecurity through the lens of computer systems and algorithms. While a master’s in cybersecurity focuses all studies on cybersecurity topics, this concentration provides a holistic education of computer science with nine credit hours of cybersecurity-specific courses.\u003C/p>\u003Cp>Upon graduation from this master’s concentration, you’ll be equipped with knowledge, skills and advanced development capability in science and engineering for cybersecurity, including:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e173218ec24c8e8ffc517da6fbdef7ac7\">Applied cryptography.\u003C/li>\u003Cli data-list-item-id=\"eb43d603ed7990cb430e653e42bd9946a\">Computer forensics.\u003C/li>\u003Cli data-list-item-id=\"e9905e4c9c23675a5f6f27113a2ef956a\">Computer and network security.\u003C/li>\u003Cli data-list-item-id=\"e7d4b680b2ffe2449f571126c8dfc2cef\">Data and information security.\u003C/li>\u003Cli data-list-item-id=\"ecfc6f7d94911b435b005d0bdf36525c9\">Software security. \u003C/li>\u003C/ul>","7.5",10,30,"01/11/2027","https://webapp4.asu.edu/dgsadmissions/Index.jsp?program=GRES&plan=ESCSEIMCS&campus=ONLNE&term=2271&session=REG","Master's in Cybersecurity – CS Concentration | ASU Online","The online master’s in cybersecurity concentration provides you with knowledge in network security, software security, data and information security.","Study with award winning faculty members in the field of computer science","\u003Cp>Of the 350+ faculty members in Ira A. Fulton Schools of Engineering, the majority have been honored with the highest awards in their fields. Faculty milestones include:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8ff5508ec350afa118c8fac959831522\">National Academy of Engineering members.\u003C/li>\u003Cli data-list-item-id=\"e62570f010c6eb54fd45a8b391f60c894\">National Academy of Sciences member.\u003C/li>\u003Cli data-list-item-id=\"efc1c89014e72c78d1f1bd7c4830f9cd0\">National Academy of Inventors members.\u003C/li>\u003Cli data-list-item-id=\"e50fbd3067734c9740bc26d900be241b5\">National Academy of Construction members.\u003C/li>\u003C/ul>","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/2020-11/ECEE-Jennifer-Kitchen-2019-EG-4495a_7.jpg","Advance your knowledge of computer science with a concentration in cybersecurity.","https://www.youtube.com/embed/7fQdMriKAvY","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/program-images/CIDSE-Stephanie-Forrest-2018-EG-2958-EG-a.jpg","Cybersecurity Professor Stephanie Forrest explains the importance of cyber defense over a large diagram on a table.",{"title":30,"slug":31},"Graduate","graduate",null,"Computer science and cybersecurity master’s courses","\u003Cp>In this program, you’ll take one course each in foundations, systems and applications. You’ll also fulfill your cybersecurity concentration requirement of nine credit hours by taking three of the following cybersecurity courses:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e04591a204ce9d852b840b3a418b562d8\">Advanced Computer Network Security.\u003C/li>\u003Cli data-list-item-id=\"e27b296b8e12dce2d9d66f2496a028450\">Applied Cryptography.\u003C/li>\u003Cli data-list-item-id=\"e1fe620dcfda75209b71b87fd14bf1164\">Information Assurance and Security.\u003C/li>\u003Cli data-list-item-id=\"e84fbb2dbdaeacc7ea2bfef34d3e92f02\">Software Security.\u003C/li>\u003C/ul>",[36,40,43,47,50,53,56,60,63,66,69,72,76,79],{"title":37,"category":38,"description":39,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Foundations of Algorithms","Foundations","\u003Ch3 class=\"h3-large\">\u003Cstrong>About this course\u003C/strong>\u003C/h3>\u003Cp>Algorithms, which are step-by-step processes to efficiently reach desired goals, have been a part of human history since the 1200s. Additionally, they are a fundamental component of any computerized system. In this foundational course, you’ll learn several different algorithms and be able to explain how they work and when they’re considered to be good. This knowledge will enable you to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eddd7c756003ed3e3ca8160d465389c30\">Develop sound knowledge of algorithms that’ll allow you to navigate the field’s literature beyond the context of this class.\u003C/li>\u003Cli data-list-item-id=\"edd5a5889f685bf4682e1e8ff5b0dfa6b\">Evaluate appropriate algorithmic techniques that can lead to more efficient solutions for problems instead of just coding the first idea that comes to mind.\u003C/li>\u003C/ul>\u003Cp>To gain these skills, you’ll learn how to work through and understand several algorithmic techniques. You’ll also understand the mathematics needed to analyze the properties of these techniques and the algorithms based on them.\u003C/p>\u003Cp>\u003Cstrong>Specific topics include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e447bc8344949df513210ed77a62f2115\">Amortized analysis.\u003C/li>\u003Cli data-list-item-id=\"edc3db91643b5478d8c1a107c9000e9b7\">Divide-and-conquer.\u003C/li>\u003Cli data-list-item-id=\"e5afad01d9579679ccef14bd4eaa63f30\">Dynamic programming.\u003C/li>\u003Cli data-list-item-id=\"ee2e8b6bb5b15e73c2934ad66c4dfcfbc\">Greedy algorithms.\u003C/li>\u003Cli data-list-item-id=\"e5bd66939434f273f1ee03aea59ad9d98\">Introduction to randomized and approximation algorithms.\u003C/li>\u003Cli data-list-item-id=\"e58085c57bc6895d6f5091f4489c7e2ae\">Network flows.\u003C/li>\u003Cli data-list-item-id=\"e246c1f2749fa66ab258f7c6f2d294793\">NP-completeness.\u003C/li>\u003Cli data-list-item-id=\"e29791e3804c453c0502417b2c4cda2e8\">Stable matching.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Required prior knowledge and skills\u003C/strong>\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ea815dca35c9b5b534f0197e99f2bcc92\">Asymptotic notation (Big-Oh).\u003C/li>\u003Cli data-list-item-id=\"e1a6557fed560865d365af6a2d5374814\">Basic discrete math (e.g., sets, functions, logic and graphs).\u003C/li>\u003Cli data-list-item-id=\"ebd4ad1baa3627cfd3af95dd36e7edd59\">Mathematical proofs.\u003C/li>\u003Cli data-list-item-id=\"edd66843901516a79a36ee68a9047c639\">Recurrence relations.\u003C/li>\u003Cli data-list-item-id=\"e66d7da9db07116adb93ab8b2ba35bc90\">Recursion.\u003C/li>\u003Cli data-list-item-id=\"e1c71423a2bf909d7ac12dbe268a6b944\">Worst-case analysis.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cp>Proficiency in programming in at least one of the following programming languages:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1f1a022e748478b229565f59ee905635\">C.\u003C/li>\u003Cli data-list-item-id=\"e50cf4093fb53a5ccb7e495a7e551a8da\">C++.\u003C/li>\u003Cli data-list-item-id=\"ede2ec17af4b82561b74516e0ddb02dd6\">Java.\u003C/li>\u003Cli data-list-item-id=\"ea1d6834ba29729ad1e6ab500d9bbb0ab\">Python.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1b6d2b27a7d4e447c80b0786138c1bcf\">Data structures and algorithms such as sorting algorithms, hash tables, binary search trees, heaps and red-black trees.\u003C/li>\u003Cli data-list-item-id=\"e4d82c2f796b2b630cbc54cb42f9f7741\">Greedy algorithms, divide-and-conquer and dynamic programming.\u003C/li>\u003Cli data-list-item-id=\"eeb42349997ad70e16eeaa006a89b3273\">Graph algorithms, such as depth-first search, breadth-first search, minimum spanning trees (Kruskal’s and Prim’s algorithms) and shortest-paths (Dijkstra’s algorithm).\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Learning Outcomes\u003C/strong>\u003C/h3>\u003Cp>Upon completing this course, you’ll be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eaf30f45e25de4b619a881978bd49aa22\">Apply knowledge of algorithms in multiple contexts using programming languages.\u003C/li>\u003Cli data-list-item-id=\"eef887fb00899fdb7c9f49605595d59b6\">Evaluate the correctness and efficiencies of algorithms.\u003C/li>\u003Cli data-list-item-id=\"ed20699ead474fbac61379efe1722c941\">Identify and apply algorithmic techniques to solve problems.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Estimated workload/time commitment per week\u003C/strong>\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">\u003Cstrong>Technology Requirements\u003C/strong>\u003C/h3>\u003Cul>\u003Cli data-list-item-id=\"e7b5501fa4d5626502b9b1387fa2e31a9\">Hardware\u003C/li>\u003Cli data-list-item-id=\"ece069272072c7fc0bc68494e94339ecd\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e9e32f83e690653636b78f8632a506894\">Personal computer with a major operating system.\u003C/li>\u003Cli data-list-item-id=\"edf9beb3039068a3966222697766ed772\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"ee833ce989fcda5f498cdeab15ab1bd92\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/Other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e04df8abe6dfa1285f90ce21c6a4e5ff5\">Current web browser.\u003C/li>\u003Cli data-list-item-id=\"e4e9be7e8c64a2916a8b123a62f1c9494\">No specialized software or other technology is needed to successfully complete this course.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Creator\u003C/strong>\u003C/h3>\u003Cp>\u003Cstrong>Professor Andréa Richa\u003C/strong>\u003C/p>\u003Cp>Andréa Richa, Ph.D. joined Arizona State University in 1998. She’s currently affiliated with the Biomimicry Center and the Biosocial Complexity Initiative in general. Dr. Richa’s main areas of expertise are in distributed/network algorithms and computing. More recently she’s focused on developing the algorithmic foundations on what has been coined as programmable matter through her work on self-organizing particle systems (SOPS).\u003C/p>\u003Cp>In addition to SOPS, Dr. Richa’s work has been widely cited, including work on bio-inspired distributed algorithms, distributed load balancing, packet routing, wireless network modeling and topology control, wireless jamming, data mule networks, underwater optical networking and distributed hash tables (DHTs). Dr. Richa received the 2017 Best Senior Researcher award from the School of Computing, Informatics and Decision Systems Engineering (CIDSE). She was also the recipient of an NSF CAREER Award in 1999, an associate editor of IEEE transactions on mobile computing and the keynote speaker and program\\general chair of several prestigious conferences. In particular, Dr. Richa was the program committee chair of the 31st International Symposium on Distributed Computing (DISC) in 2017, one of the top two conferences in distributed computing. Dr. Richa has also delivered several invited talks both nationally and internationally.\u003C/p>",{"title":41,"category":38,"description":42,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Knowledge Representation and Reasoning","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Knowledge representation and reasoning (KRR) is one of the fundamental areas in artificial intelligence. It is concerned with how knowledge can be represented in formal languages and manipulated in an automated way so computers can make intelligent decisions based on the encoded knowledge. KRR techniques are key drivers of innovation in computer science, and have led to significant advances in practical applications in a wide range of areas from artificial intelligence to software engineering. In recent years, KRR has also derived challenges from new and emerging fields including the semantic web, computational biology and the development of software agents. This is a graduate-level course that introduces fundamental concepts as well as surveys recent research and developments in the field of knowledge representation and reasoning.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e58f9cce4e354b64a0d4bf9c56efeb567\">Classical logic and knowledge representation.\u003C/li>\u003Cli data-list-item-id=\"eba2ae09b561cc64f6dde44822fc956bf\">Answer set programming.\u003C/li>\u003Cli data-list-item-id=\"e2cd7112c182b6a5a3b821a1dd6764d93\">Reasoning about actions and planning.\u003C/li>\u003Cli data-list-item-id=\"e171603d1fd33a3e2913314402efa7d03\">Ontology, semantic web languages and knowledge graph.\u003C/li>\u003Cli data-list-item-id=\"ef81bf6b186060b34f59e38942b730c54\">Combining logic and probability.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8cf4bbdf9cc712b96ebffe68b4cb1b04\">Answer set programming.\u003C/li>\u003Cli data-list-item-id=\"eff8dcd1f36db670d101d7e9ef2d5276e\">Clingo.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e9a38106de88a0609404a71575be220c1\">Algebra.\u003C/li>\u003Cli data-list-item-id=\"eab07761177283b01b03e33d09c9a8ae4\">Classical logic.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e380566d91c854e0056380e5efb58003a\">Programming.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e89ff8b5ccab99c00a3d679d15e4726fb\">Ability to effectively read code.\u003C/li>\u003Cli data-list-item-id=\"e5fea4c0352073c3c4ef1d09046ff5801\">Confidence executing at least one programming language, such as Python, Java, C#, C++ or C.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec45eaad084568199ada179609119b2c8\">Discuss the foundations of KRR.\u003C/li>\u003Cli data-list-item-id=\"ed3ce6f18549828c65d0462f0f7390d48\">Explain different categories of representation and reasoning tasks.\u003C/li>\u003Cli data-list-item-id=\"e9a7a61709ffd92c32c95387c46de4ef3\">Assess the tradeoff between representation and reasoning.\u003C/li>\u003Cli data-list-item-id=\"e300a4b14b4e195f336adc11c7ed09990\">Identify which knowledge-based techniques are appropriate for which tasks.\u003C/li>\u003Cli data-list-item-id=\"eacd7d2639fa1bdebfe7c63afb9611447\">Apply KRR systems to challenging real-world problems.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8a5d60421f80a617c0a91cf8a369a661\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e2c8e1d9f4c80ab555382f304839e900f\">Personal computer with a major operating system.\u003C/li>\u003Cli data-list-item-id=\"e70e6542f0f5b47473a1e52d8c4ecd870\">Reliable, strong Internet connection.\u003C/li>\u003Cli data-list-item-id=\"e16346ef634cc86c9274e6dceecfcb5a7\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e085afe915912550bb741c860924b697b\">Clingo.\u003C/li>\u003Cli data-list-item-id=\"e3843ac5e03ac6bc70d3c9bb3df6cd8d2\">Protégé.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>Joohyung Lee\u003C/strong>\u003C/p>\u003Cp>Joohyung Lee is a tenured associate professor in the School of Computing and Augmented Intelligence at Arizona State University, where he has led the Automated Reasoning Group since 2005. He received his PhD from the University of Texas at Austin. His interests include designing and building intelligent systems, which can perform automated reasoning based on the knowledge represented in a formal language, thereby intelligently handling open-ended tasks by \"thinking.\" He has been working on knowledge representation, logic programming, commonsense reasoning, reasoning under uncertainty, cognitive robotics, computational logics, security and question answering. His research has been supported by the National Science Foundation, Department of Defense, IARPA, Siemens, Bosch and ETRI. He is a recipient of the Outstanding Paper Honorable Mention Award from AAAI 2004.\u003C/p>",{"title":44,"category":45,"description":46,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Distributed and Multiprocessor Operating Systems ","Systems","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>This course will teach both the fundamental concepts and principles of distributed systems and the practical skills for developing distributed systems. To begin, this course provides an initial exploration of distributed systems and their design goals and types of systems. We address the architectures of distributed systems, including centralized and decentralized architectures and important real-world examples of distributed systems that are built following these architectures. With interprocess communication at the heart of all distributed systems, a concentrated focus will be on a strong series of communication and coordination. To conclude, we take a detailed look at what consistency of replicated data actually means and the various ways that consistency can be achieved.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e5cebc39011236ea906510d822fa94fab\">Architecture.\u003C/li>\u003Cli data-list-item-id=\"e58790f5ead311ff1e4de4b06b17e6fa8\">Coordination.\u003C/li>\u003Cli data-list-item-id=\"e2e08ab51b27b521f3f876e3e0c18b514\">Communication.\u003C/li>\u003Cli data-list-item-id=\"e5ce910072fcc3d7e1de60f3fc51b83dd\">Consistency and replication.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e86961b65968a85bbfdfcec965a413b87\">gRPC.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ede912d4e6b944c5770b61d6fd348e642\">Algebra.\u003C/li>\u003Cli data-list-item-id=\"efdb68af240daf28ce6069f7339ada706\">Linear algebra.\u003C/li>\u003Cli data-list-item-id=\"e786ad09f882860f040733170afbb7482\">Data structures.\u003C/li>\u003Cli data-list-item-id=\"e4f0c61f235ab51c304f884c510d6a157\">Computer organization and architecture.\u003C/li>\u003Cli data-list-item-id=\"e48ab8a1271ed70d20cc00c7e53b7d1b4\">Operating systems.\u003C/li>\u003Cli data-list-item-id=\"e29d98e63f9482b884d82be067c1d02c7\">Computer networking.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef855c03d547ad41dfce4c92872845641\">Proficiency in programming in at least one of the following programming languages:\u003Cul>\u003Cli data-list-item-id=\"e872f333291608b39039cedd97207bbb9\">Python.\u003C/li>\u003Cli data-list-item-id=\"e9c0bc8381c4d365ca53d29bf57f8e3eb\">Java.\u003C/li>\u003Cli data-list-item-id=\"ec95ea9e5eefb32b9a1843eacddc95b1c\">C#.\u003C/li>\u003Cli data-list-item-id=\"e2311b1c0908667f4980054e4b16f2aa2\">C++.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eabe33647572d1b9caaedce4c32b91a84\">Experience reading technical specifications and documentation.\u003C/li>\u003Cli data-list-item-id=\"eb16135db8221852fec660ae67a7596de\">Experience with virtual machine software: creating, configuration and running virtual machines.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eac73ec442ec0b5fabf823864a383adf3\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e136244e133a7eae54cd57bc503ad3eef\">Personal computer with a major operating system and the following:\u003Cul>\u003Cli data-list-item-id=\"e3e65223abf58ef3160e2c532773e61cb\">8 GB RAM or higher.\u003C/li>\u003Cli data-list-item-id=\"e2e6817774843e765d6a04f28f9ca69be\">An x86-64 CPU.\u003C/li>\u003Cli data-list-item-id=\"efacf00d1a3eadd8241d1d5b0d5c431ec\">Ability to install virtual machines on this computer.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e9428a79d582da2fd871ac13e55a7b335\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e635e1ba46b18b8c7d83b16c43e3cc416\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e44eb3236a14f2ac65e3d806f48fa7179\">Virtual machine software such as VirtualBox.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>Ming Zhao, Ph.D.\u003C/strong>\u003C/p>\u003Cp>Ming Zhao, Ph.D. is an associate professor at ASU in the School of Computing and Augmented Intelligence. Before joining ASU, he was an associate professor of the School of Computing and Information Sciences at Florida International University. He directs the Research Laboratory for Virtualized Infrastructure, Systems, and Applications. His research is in the areas of experimental computer systems, including cloud/edge, big data/AI and high-performance systems, as well as operating systems and storage in general. Dr. Zhao is a recipient of NSF CAREER award, Air Force Summer Faculty Fellowship, VMware Faculty Award, AWS Machine Learning Research Award and the Best Paper Award of the IEEE International Conference on Autonomic Computing. His research outcomes have been adopted by several production systems.\u003C/p>",{"title":48,"category":45,"description":49,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Mobile Computing","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>The goal of this course is to provide an in-depth understanding of the fundamental concepts and challenges in mobile computing, as well as and study the existing and proposed solutions for these challenges from research and development perspectives. Topics covered in this course include mobile app development, wireless communication, mobile technology management, mobility tracking, context awareness and programming applications on mobile systems. Coursework will involve programming assignments, discussions, quizzes and a project.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec7e509f191efa89ecd5724c9ca62fd36\">Mobile programming.\u003C/li>\u003Cli data-list-item-id=\"e5d03c719a3d8723a3d324dba2c9c9140\">Internet of Things (IoT).\u003C/li>\u003Cli data-list-item-id=\"e2a330b0392ea8a255f72c704278b7da7\">Edge and cloud computing.\u003C/li>\u003Cli data-list-item-id=\"ead88e937a0fe36b10ae8781e19bf1f86\">Mobile networking.\u003C/li>\u003Cli data-list-item-id=\"e47667d87e3908255c62e9a1250572c8a\">Mobile information access.\u003C/li>\u003Cli data-list-item-id=\"e4d0dbc69d94b76ad79b743cf51db8b16\">Adaptive applications enabled by machine learning and AI.\u003C/li>\u003Cli data-list-item-id=\"eb106fe62506389e0f479d6e5da03e8f3\">Energy-aware systems.\u003C/li>\u003Cli data-list-item-id=\"e24cc2f1d93324275237473f7735c283f\">Location-aware computing.\u003C/li>\u003Cli data-list-item-id=\"e2f350977a3f0c18e59fe0e4ac5e1430d\">Mobile security and privacy.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ebec38e9a081b0c1044d5c27398a539ed\">Android Application Development.\u003C/li>\u003Cli data-list-item-id=\"e7411d828cd46d110b8cadc0e1c935310\">Java.\u003C/li>\u003Cli data-list-item-id=\"ef6ce86cef2ab320e4cab47cb884b5ccd\">Python.\u003C/li>\u003Cli data-list-item-id=\"e8df8f40bea9e53ba56f3267bcb5b450a\">Tensorflow.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e5b3098331fbfbd7986e4e78dc6984483\">Networking.\u003C/li>\u003Cli data-list-item-id=\"ea396aab746e8a5b702dc8bebd6693159\">Operating systems.\u003C/li>\u003Cli data-list-item-id=\"eb10b4428b593fefaccb599009a585898\">Security.\u003C/li>\u003Cli data-list-item-id=\"e28f59724226e851c61a0ddb7c55344e7\">Probability.\u003C/li>\u003Cli data-list-item-id=\"eebe8b691ac99f0775ceb6a98c21e3d93\">Statistics.\u003C/li>\u003Cli data-list-item-id=\"e9b9f07d17919dec62567e3e180550d24\">Algorithms.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef8e5a42a83a5a8e8c97b94c482aaf909\">Ability to read Python code.\u003C/li>\u003Cli data-list-item-id=\"e1dfd3ef13d61fb4cce3cc8b78404e11e\">Ability to write Java and Python code.\u003C/li>\u003C/ul>\u003Cp>Note: The course projects will be completed using the Android application development environment and Python.\u003C/p>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"edc102d6035f5fafce78c3314a2b79151\">Understanding of mobile computing system models.\u003C/li>\u003Cli data-list-item-id=\"e7ffe8323b704f2240b525ef9b5375b91\">Mobile networking: Mobile device routing, Handoff, ARP.\u003C/li>\u003Cli data-list-item-id=\"e1cb43c942834cc27e39e16b21ea91f88\">Experience reading research papers.\u003C/li>\u003Cli data-list-item-id=\"e91053229795cb4ad1b7df77e2d57de82\">Understanding of context-aware applications and the role of device usage, mobility patterns in context-aware applications.\u003C/li>\u003Cli data-list-item-id=\"eee688607c89f6bcc4dc7af63dadee213\">Mobile application security.\u003C/li>\u003Cli data-list-item-id=\"eaaf97d20c8a32a7b827e6523067d560e\">Understanding energy and power consumption of a mobile application.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>\u003Cbr>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed8351be59d02caa395b06db56b89fff4\">Design a context-aware application.\u003C/li>\u003Cli data-list-item-id=\"ecdaeb7126907d3a2584b1fa6de88afbb\">Identify the advantages of using context in applications.\u003C/li>\u003Cli data-list-item-id=\"efdee876d603ef5c92e198efbba578bf8\">Explore the challenges arising due to changes in the environment in which computation is performed.\u003C/li>\u003Cli data-list-item-id=\"ea5e7c50b0d372bfd02e2d42c7b584d7e\">Identify relevant environment changes and analyze their causes, such as mobility, availability of data, and resource constraints.\u003C/li>\u003Cli data-list-item-id=\"ed33df88e58cd7c03836091bcd6457897\">Define smartness and identify salient features that distinguish smart applications from traditional ones in the context of smart city, smart grid, smart transportation, smart mobile applications, and autonomous systems, such as autonomous cars.\u003C/li>\u003Cli data-list-item-id=\"e8c460938ec2b372689e91566bc07949d\">Describe key features of the Internet of Things (IoT) and design a distributed smart application using IoT.\u003C/li>\u003Cli data-list-item-id=\"e0888ef423b6f86737a2537b221c06844\">Define cloud computing, crowdsourcing, volunteer computing and other novel variants of pervasive computing.\u003C/li>\u003Cli data-list-item-id=\"e3f95a6fa6efe2344dc8ad5bd019ea8ad\">Analyze nonfunctional requirements of smart mobile applications, such as safety, security and sustainability.\u003C/li>\u003Cli data-list-item-id=\"e186e4457ff974de9028c0022a3892788\">Apply popular tools, such as machine learning, security protocols, AI and software testing, to validate safety security and sustainability of smart mobile applications.\u003C/li>\u003Cli data-list-item-id=\"e67846663997a5d47b3280458a3c8f42c\">Acquire programming skills on popular mobile platforms, such as Android.\u003C/li>\u003Cli data-list-item-id=\"ed105e335a9ffb41609905458958dd395\">Develop, end-to-end, a sensor-enabled smart autonomous practical application.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1bc41b358016a7519a1091149f2fde06\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e88a6377ef7a41913cd06895682579c94\">Personal computer with the following:\u003Cul>\u003Cli data-list-item-id=\"eedf6b9ca837fb623393ff815196673a0\">At least 2GB RAM (4GB RAM recommended).\u003C/li>\u003Cli data-list-item-id=\"e1b507515d43e4c9b24210e69396a67b8\">At least 1GHz (2GHz or more recommended)\u003C/li>\u003Cli data-list-item-id=\"edd30bd02312fe9379a7fc716c8d88329\">Windows 7 or higher / OS X v10.7 or higher.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e4958765cdc4123a2ef134eeae513ab79\">Reliable, strong Internet connection.\u003C/li>\u003Cli data-list-item-id=\"e2f7a2afdd52871d7e746e7010827c926\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8377c09fd8ffee310bd913b29da8a078\">Android Studio Integrated Development Environment (IDE) with the software development kit (SDK) bundle (check the system required for Android Studio on their website).\u003C/li>\u003Cli data-list-item-id=\"efc347b178a9e82d6cb79a5db04a3ec16\">Gradescope (for submission).\u003C/li>\u003Cli data-list-item-id=\"eda3708924aba529fa1c5e9b34676820c\">Python3 (recommended 3.10).\u003Cul>\u003Cli data-list-item-id=\"e68bac862556bc8746550195a07cfdf67\">Pandas 2.1.4.\u003C/li>\u003Cli data-list-item-id=\"efa8e1decf0922f8f26ba12015d0b5aca\">Numpy 1.26.3.\u003C/li>\u003Cli data-list-item-id=\"e2af64ce46b8bbb5dfd4813f693881b0f\">Scikit-learn 1.3.2.\u003C/li>\u003Cli data-list-item-id=\"e13c19d6c97eb283221ae4841d5ce78a7\">OpenCv 4.9.0.80.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e32c280a8081098ed1560479369f56c99\">Tensorflow 2.15.0.\u003C/li>\u003Cli data-list-item-id=\"eab4462b75ee0df56c885fcb46be29669\">Flask for your local server.\u003C/li>\u003Cli data-list-item-id=\"e7fcfcc8d4ef282540dbb365cdf35668f\">YouTube account.\u003C/li>\u003Cli data-list-item-id=\"e0d0ba82e437b440655ccca0fb47f30ee\">GitHub account.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>Dr. Ayan Banerjee\u003C/strong>\u003C/p>\u003Cp>Dr. Banerjee is an assistant research professor in the School of Computing and Augmented Intelligence at Arizona State University. His research interests include pervasive computing in healthcare and analysis and safety verification of embedded system software. Dr. Banerjee currently focuses on data-driven analysis and modeling in many different domains including diet monitoring, gesture recognition and biological process modeling. He works closely with government agencies, such as the Food and Drug Administration, and medical agencies, such as Mayo Clinic. Dr. Banerjee is also interested in hybrid system-based modeling and safety verification of closed-loop control systems that interact with the physical environment, also known as Cyber-Physical Systems. In addition, his work includes developing management algorithms for sustainable data centers using renewable sources of energy.\u003C/p>",{"title":51,"category":45,"description":52,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Software Verification, Validation and Testing","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Software as a stand-alone product or embedded within a system plays an integral role in our world today. As a consequence, it's essential that software works as expected. This requires software testing which entails answering the verification question, “Are we building the product right?” and the validation question, “Are we building the right product?” Understanding these questions is crucial for developing good test cases. This course is for anyone involved in testing software at any level from code modules to system testing. Strategies and techniques are presented for testing software and planning and tracking testing efforts.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e277ce87b8b0e365e61b1caf6b6abcb2d\">Testing background.\u003C/li>\u003Cli data-list-item-id=\"ef245831856a2b22a51780418d3656999\">Testing process activities.\u003C/li>\u003Cli data-list-item-id=\"ebf8bfd669fe79aba2bca7d64e0fbcf96\">Requirements-based testing techniques.\u003C/li>\u003Cli data-list-item-id=\"e689d1bdb2f7f61c59c5f5930b4b664cb\">Structure-based testing techniques.\u003C/li>\u003Cli data-list-item-id=\"eea158f1410e09b9d2dfc5b1192497bcd\">System testing.\u003C/li>\u003Cli data-list-item-id=\"e1cd0e02c2951478eed32ac3f67d7dbfe\">Testing tools.\u003C/li>\u003Cli data-list-item-id=\"e64fb72b1504319e0bb1e76557f57c855\">Reliability models.\u003C/li>\u003Cli data-list-item-id=\"e39f87f7eef469d73cea3d1d087b322ac\">Statistical testing.\u003C/li>\u003Cli data-list-item-id=\"e0bacfd743d0d58717fc1a144370f4206\">Test planning.\u003C/li>\u003Cli data-list-item-id=\"e6decf57dbe2d528f48c846168b7d0a71\">Tracking testing progress.\u003C/li>\u003Cli data-list-item-id=\"e6be1d0f72d2ee92756ab67b8775f4791\">Test documentation.\u003C/li>\u003Cli data-list-item-id=\"e2e788b3d833e70c02463392eaba67b04\">Test process improvement.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e854bca96449f8261f28a7f45eabbd575\">Unit testing frameworks.\u003C/li>\u003Cli data-list-item-id=\"ed63033b1d35991c3685e6afabedb0e10\">Code coverage tools.\u003C/li>\u003Cli data-list-item-id=\"e624f1da285ab7da622753c869f6508aa\">Static analyzers.\u003C/li>\u003Cli data-list-item-id=\"e30550832f8dd18e8ab132f53eb814fe2\">Reliability models.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and the necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef1d643e0be2ced139d3d1b775b23470a\">Discrete math.\u003C/li>\u003Cli data-list-item-id=\"e4ddc3e97ac58ae777366f031b9a2dea8\">Probability and statistics.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4461b346bfb86de08ab3dea3926e8230\">Programming in a high-level language — Java (recommended).\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed955899f169b90ce42cfbaf5b19578f1\">Experience with software development life cycle models.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ea21905fe6e15460154b50774e84888fe\">Explain how testing activities fit within leading software development process models.\u003C/li>\u003Cli data-list-item-id=\"e2be4a5fa394e3395ce35e2cd70c3a2b1\">Understand and apply best practices for software testing.\u003C/li>\u003Cli data-list-item-id=\"ec702ed3512eac77d1c02617afb835172\">Create test cases based on commonly used requirements-based testing techniques.\u003C/li>\u003Cli data-list-item-id=\"effe9d3fd8b99324b938b9b223032e393\">Create test cases to achieve control and data flow structure-based coverage.\u003C/li>\u003Cli data-list-item-id=\"e8ad4f3eef54ed0d36b262df0340b6e05\">Apply static analysis techniques to identify code anomalies.\u003C/li>\u003Cli data-list-item-id=\"e361fb1ebe6d8cc1fcf042993e1536b47\">Create test cases that demonstrate system-level quality requirements are being met.\u003C/li>\u003Cli data-list-item-id=\"e258a6feba62df325ed41f63365d506c1\">Identify appropriate testing tools for applications.\u003C/li>\u003Cli data-list-item-id=\"ee694076aa28d666d81e8434a92bcf2b5\">Predict software reliability based on operational profile testing and reliability models.\u003C/li>\u003Cli data-list-item-id=\"e7909301e1eacbb90533a7ae0be60313f\">Describe activities to perform for improving testing processes.\u003C/li>\u003Cli data-list-item-id=\"e92a815440bee4ed4ef1672565a5373df\">Analyze testing needs to create a plan to achieve test objectives.\u003C/li>\u003Cli data-list-item-id=\"e339dae0f8e996191b3332fe448797a41\">Track testing progress against a plan.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e76e87c2d281fb5b6d6ccb56f24fdae76\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e053c44ffda220c43c695d4e7330079dd\">Personal computer with a major operating system.\u003C/li>\u003Cli data-list-item-id=\"e09e356b8ed1e4d38dad85c1673476ece\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e41b8b50dd6e2a3b4cd0fedbdf974b579\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eea2812f3a0b03457ae88f048e89329e1\">Java.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>James Collofello\u003C/strong>\u003C/p>\u003Cp>James Collofello, Ph.D. serves as vice dean of academic and student affairs and has held this position since 2006. In this capacity, he leads the school’s student recruitment and retention, career development and placement, K-12 programming, new curriculum development, accreditation and oversight of Fulton Difference programming. The Fulton Difference consists of innovative programs operated at scale to provide students with opportunities to develop and enhance their research, leadership, project development and entrepreneurship skills. Major Fulton Difference programs include engineering student organizations, Fulton Undergraduate Research Initiative, Grand Challenge Scholars program, undergraduate teaching assistant program and engineering projects in community service. He is also a professor of computer science and software engineering. His teaching and research interests lie in the software engineering area with an emphasis on software quality assurance, software process improvement and software project management. He has also been active in developing and improving computer science curriculum and working to improve undergraduate retention. In addition to his academic activities, he has also been involved in applied research projects, training and consulting with many large corporations over the last 25 years.\u003C/p>",{"title":54,"category":45,"description":55,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Software Project, Process and Quality Management","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>The success of a software project is dependent upon many factors and requires skillful leadership in planning and tracking the project. There are numerous software development processes and methodologies to choose from and customize based on the organization’s unique environment. This course is for anyone seeking to learn more about planning and tracking a successful project. Strategies and techniques for estimating, scheduling and tracking a project in plan-driven and agile environments are presented, along with approaches for tracking schedule, budget and quality risk. Additionally, you’ll learn about quality management planning and defining quality objectives, as well as software process maturity models and techniques for software process improvement.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e5ad6260d45030d41b4a02b84e4320f18\">Software development process models.\u003C/li>\u003Cli data-list-item-id=\"ef2cab29ff442127315360a16e95b6c5c\">Software configuration management.\u003Cul>\u003Cli data-list-item-id=\"edf1b035aa3f849155ca55caf5369d611\">Configuration identification and control.\u003C/li>\u003Cli data-list-item-id=\"ec73adb4667244deeab537616cb6d4819\">Error tracking.\u003C/li>\u003Cli data-list-item-id=\"e04755f12c8fd7bdcdfd45d1082f98a47\">Configuration management tools.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e8108ceece8387ab3d451a6bb2bf37797\">Risk management.\u003Cul>\u003Cli data-list-item-id=\"e8db66b973db7371ecb0876e16fd23fb0\">Risk identification.\u003C/li>\u003Cli data-list-item-id=\"ef608d63037d17dec35286e7a6e8025c3\">Risk prioritization.\u003C/li>\u003Cli data-list-item-id=\"ed508019ae7eab0f0d39120052cc8fdea\">Risk mitigation.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e961e2727c77295d903f41aae5f710483\">Software project management.\u003Cul>\u003Cli data-list-item-id=\"e5dd8e0bdd0a1c0166021c79daf82baeb\">Development team approaches.\u003C/li>\u003Cli data-list-item-id=\"e85411fe7a736d0a95d2b84fd387d6020\">Management strategies.\u003C/li>\u003Cli data-list-item-id=\"e3283f4e89dd941d0be0cabae1461d50e\">Development plans.\u003C/li>\u003Cli data-list-item-id=\"e1c9d1a3b58f415c02647fa8e88769dba\">Cost estimation.\u003C/li>\u003Cli data-list-item-id=\"ed333ce7a09ce4190217b64d66ab24c0d\">Scheduling.\u003C/li>\u003Cli data-list-item-id=\"e687f777f00ae9fe4352c87fe8d038f99\">Tracking progress.\u003C/li>\u003Cli data-list-item-id=\"e84967dd202b40b4aac4a5d736dd051f6\">Project management tools.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e642ad2983a164c7291703f46d44ff3d1\">Software acquisition management.\u003C/li>\u003Cli data-list-item-id=\"e66ddbc102c247cdaf4e5b98558dc3b80\">Software process management.\u003Cul>\u003Cli data-list-item-id=\"e3d1b48948374945d876d4b0402ec37c1\">SEI CMMI.\u003C/li>\u003Cli data-list-item-id=\"e7184147c60253c171b92dcdac6f01624\">Standards.\u003C/li>\u003Cli data-list-item-id=\"eff8a53ebafaebb2d4cb2f3c420ac20fc\">Software process improvement.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"ea6d86a8881655a1c5d8d2402cbb175e9\">Software quality management.\u003Cul>\u003Cli data-list-item-id=\"ee3050cb637841ca4e854a36ba4dfe152\">Quality functions.\u003C/li>\u003Cli data-list-item-id=\"ecc4ded9f182609b4c536c3f1e459662b\">Quality metrics.\u003C/li>\u003Cli data-list-item-id=\"e3d87089516ead7c00a20e42892823f30\">Software quality standards.\u003C/li>\u003Cli data-list-item-id=\"ea63d1a9dd9062cca551e62721c7d4a35\">Statistical quality control.\u003C/li>\u003Cli data-list-item-id=\"efc865b24aa76e5c57505b82dfd9008c9\">Quality assurance tools.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed156d998c5b4080bbfebff6690c6d294\">Software cost estimation models.\u003C/li>\u003Cli data-list-item-id=\"eb65d1230ff7a12b9e2ba9902ba18b9b8\">Earned value project management.\u003C/li>\u003Cli data-list-item-id=\"e7fea3558e3c51ead50518204127ee7e4\">SCRUM.\u003C/li>\u003Cli data-list-item-id=\"e95c35786bb42ef1813ded200665df2b4\">Software quality measurement tools.\u003C/li>\u003Cli data-list-item-id=\"ebd6d1e2207863694f08fa4af2673fbc9\">CMMI.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eea4c5ccb87dca3fc48b2a7a6afa1125e\">Understanding of the phases of the software development process including requirements, design, coding and testing.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1d1d436ce971bbca6e7e7667c25440de\">Ability to effectively read C code.\u003C/li>\u003Cli data-list-item-id=\"e8d2d5393aa8e143d3f56e1cf86e02099\">Ability to effectively read Python code.\u003C/li>\u003Cli data-list-item-id=\"ebc871352f7c8c36c4cafdf4d3e32d8ed\">Confidence executing at least one programming language:\u003Cul>\u003Cli data-list-item-id=\"e185864c82588ceffd0ddd09d168690f3\">Python.\u003C/li>\u003Cli data-list-item-id=\"e18c1918c726182b3b50994b451ff9c99\">Java.\u003C/li>\u003Cli data-list-item-id=\"ee9da60e36a0ff982629d8e23c6cac619\">C#.\u003C/li>\u003Cli data-list-item-id=\"e564fb6cc42f8905b96e81d3742616626\">C++.\u003C/li>\u003Cli data-list-item-id=\"e75fab0f9c6c80327de76930dffe94bce\">C.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ebf0540d9eddc266c0957561a88c02409\">Experience as a member of a software development team.\u003C/li>\u003Cli data-list-item-id=\"eac4d661964293c6e7969a77db988b72a\">Applying the phases of the software development process including requirements, design, coding and testing.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4872a2b6541608e7ff840a77e05d5359\">Differentiate between development process models.\u003C/li>\u003Cli data-list-item-id=\"e7f8c827b40c9e7b8d67679ccabf6005e\">Identify strengths and weaknesses of process models.\u003C/li>\u003Cli data-list-item-id=\"eff81271523cdbfff282a8305558588e1\">Determine software configuration management activities.\u003C/li>\u003Cli data-list-item-id=\"eb389adf4452c5f016ce6d1f47c7f4599\">Apply risk management theories and practices for software projects.\u003C/li>\u003Cli data-list-item-id=\"ea8f266d7fed098e058f37d6aa781e18d\">Manage software projects including planning and tracking.\u003C/li>\u003Cli data-list-item-id=\"e12fb72187486cf953ed1a603b665815d\">Develop a software outsourcing plan.\u003C/li>\u003Cli data-list-item-id=\"e2c22e4ee3245af338fc0f7b9280c2e56\">Utilize software process management practices including process improvement.\u003C/li>\u003Cli data-list-item-id=\"ee5f4597fd322cb8dfb5d23aa6f06eb5b\">Perform software quality management activities.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e61c1751c550593a850c14fce9c921b73\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e2ed750ebc636e7191d34620747bccacc\">Personal computer with a major operating system\u003C/li>\u003Cli data-list-item-id=\"e3f1a1358d57291b60dd46376e4c4af9c\">Reliable, strong Internet connection.\u003C/li>\u003Cli data-list-item-id=\"e8c5974bd69b2d580da430d9581ef4ead\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1045ca1adf8a32c7f2774ecf3d5fef4f\">Word processor to make professional quality documents.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>James Collofello\u003C/strong>\u003C/p>\u003Cp>James Collofello, Ph.D. serves as vice dean of academic and student affairs and has held this position since 2006. In this capacity, he leads the school’s student recruitment and retention, career development and placement, K-12 programming, new curriculum development, accreditation and oversight of Fulton Difference programming. The Fulton Difference consists of innovative programs operated at scale to provide students with opportunities to develop and enhance their research, leadership, project development and entrepreneurship skills. Major Fulton Difference programs include engineering student organizations, Fulton Undergraduate Research Initiative, Grand Challenge Scholars program, undergraduate teaching assistant program and engineering projects in community service. He is also a professor of computer science and software engineering. His teaching and research interests lie in the software engineering area with an emphasis on software quality assurance, software process improvement and software project management. He has also been active in developing and improving computer science curriculum and working to improve undergraduate retention. In addition to his academic activities, he has also been involved in applied research projects, training and consulting with many large corporations over the last 25 years.\u003C/p>",{"title":57,"category":58,"description":59,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Artificial Intelligence","Applications","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>The field of artificial intelligence (AI) develops the principles and processes for designing autonomous agents. This course addresses the core concepts of designing autonomous agents that can reason, learn and act to achieve user-given objectives and prepares students to address emerging technical and ethical challenges using a principled approach to the field. Main topics include principles and algorithms that empower modern applications and future technology development for self-driving vehicles, personal digital assistants, decision support systems, speech recognition and natural language processing, autonomous game playing agents and household robots.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"efc686a28264946c00f55ec1a09710ef6\">Neural Networks.\u003C/li>\u003Cli data-list-item-id=\"ef78a0dd0f3ea69684ee3bf81896c6b3f\">Classical Planning.\u003C/li>\u003Cli data-list-item-id=\"e81abfcdf5512be8a6aa90db7139e5225\">Modeling & Reasoning.\u003C/li>\u003Cli data-list-item-id=\"e1c06dcc1e43bb8b10f02ba9eb03060a1\">Reinforcement Learning.\u003C/li>\u003Cli data-list-item-id=\"e826170a73eda020a3e2df23a20dc2aac\">Markov Decision Processes (MDPs).\u003C/li>\u003Cli data-list-item-id=\"ed3b616230c656483aa9204e82203ea50\">Partially Observable Markov Decision Processes (POMDPs).\u003C/li>\u003Cli data-list-item-id=\"efcd1cac42f0ae1e23b5db3b9d3349a8c\">Bayesian Networks.\u003C/li>\u003Cli data-list-item-id=\"ec071118f29323b58bbfaf55a95550113\">Sensors for Perception.\u003C/li>\u003Cli data-list-item-id=\"e8bd5355e93a96cf50208e4d46e2c49ad\">Perception based Recognition.\u003C/li>\u003Cli data-list-item-id=\"e012d87f495396876f22e364bc8897b1d\">Real-world Applications.\u003C/li>\u003Cli data-list-item-id=\"e546c341f9b92b11d7b8fd8e670b0c5f1\">Robotics.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e82c467f28205a36e0da7c036a41e3519\">Algebra.\u003C/li>\u003Cli data-list-item-id=\"e08b4d727ee8afe564d512afb151ed66f\">Linear algebra.\u003C/li>\u003Cli data-list-item-id=\"ed9bd7eb03728b09289bafd0b706b44da\">Probability and statistics.\u003C/li>\u003Cli data-list-item-id=\"e81d781803a362a386b8e14cbe8b41458\">Algorithms.\u003C/li>\u003Cli data-list-item-id=\"e67674f9a18e65e693cc7ae71b7d205be\">Data structures.\u003C/li>\u003Cli data-list-item-id=\"e5af3cf27dab0cf97b5d7ec0817cc93a6\">Computer organization and architecture.\u003C/li>\u003Cli data-list-item-id=\"ede8a2cc6e4756d3b980602bdbb75a69f\">Operating systems.\u003C/li>\u003Cli data-list-item-id=\"edb797df570a0758aca6d12f03b246616\">Discrete math.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eda53e48359db374bc116d75f2f26bb1d\">Programming in Python.\u003C/li>\u003Cli data-list-item-id=\"e7db1b2a3d7e48309c59e6ec3d07ba4e9\">Ability to effectively read Python code.\u003C/li>\u003Cli data-list-item-id=\"ee237194d04795833e028d945c5243cda\">Confidence in executing at least one programming language:\u003Cul>\u003Cli data-list-item-id=\"e822a5e910ff39efdc77b4a771b0d1abb\">Python.\u003C/li>\u003Cli data-list-item-id=\"ef1f6d5d8387addd1da1f7db433a939b8\">Matlab.\u003C/li>\u003Cli data-list-item-id=\"e77486ae3d20c7c2becef56f88e6e1018\">R.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e1d07744cfa71085506db40d5825a06b9\">ROS.\u003C/li>\u003C/ul>\u003Cp>Note: The course project will be completed using the language that the learner chooses. However, the course team will not be able to help the learner if they choose any language that is not Python or Matlab.\u003C/p>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1fbddd24a4ce90f91300e96022c3ba07\">High-Level Programming Language (Python or Matlab).\u003C/li>\u003Cli data-list-item-id=\"eb6631eacafd8a89db5a0807aa05a5523\">Ability to implement AI Algorithms using Python.\u003C/li>\u003Cli data-list-item-id=\"e90ffc01a599edf13cd2e46d39c017263\">Digital Drawing tools (e.g. for constructing Parse Trees).\u003C/li>\u003Cli data-list-item-id=\"eed5a7d7136c72378060c25ff2833dedb\">Familiarity with any one of the following frameworks:\u003Cul>\u003Cli data-list-item-id=\"e1f579a7e69a95812daae807cde6210ee\">Jupyter Notebook.\u003C/li>\u003Cli data-list-item-id=\"e8cd60f35cacee5e044e2b5f532a41c87\">Google Colab.\u003C/li>\u003Cli data-list-item-id=\"e439145a0487f773fabb9187f30ab06ba\">PyTorch.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e922c9e5826c9aeef95a76f2f21840e07\">Familiarity with the following tools/libraries:\u003Cul>\u003Cli data-list-item-id=\"e2e6ca1cce1420bb36f6c542f0f957076\">Numpy.\u003C/li>\u003Cli data-list-item-id=\"e84865132fdafb9a01f4f403cee9ca2a5\">Pandas.\u003C/li>\u003Cli data-list-item-id=\"e492e635b80614d18500ae2dc76cc6c2a\">Tensorflow.\u003C/li>\u003Cli data-list-item-id=\"eb621e650dd41f98de8e6a9850ce92ead\">Keras.\u003C/li>\u003Cli data-list-item-id=\"e57c633fe3a56984ab474a19ec758b6e2\">Matplotlib.\u003C/li>\u003Cli data-list-item-id=\"ec23f5e7edffa9ad3792979eeed635eae\">Scikit Learn.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e797d11dd679e90fd491b6e78a17b0c14\">Microsoft Office 365.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ea1aaa5c3d61a7e13aa9ba4789df5106d\">Apply logical reasoning and programming to produce solutions for real-world problems.\u003C/li>\u003Cli data-list-item-id=\"e5d47fef0108121400b07c2e063ed0629\">Use probabilistic inference to navigate uncertain information efficiently.\u003C/li>\u003Cli data-list-item-id=\"e0b71fe4ddbceb7fe4a7c926b2c93926f\">Determine appropriate machine learning methods for a given scenario or dataset.\u003C/li>\u003Cli data-list-item-id=\"ee271962aea9a00d23ac01667cb331ec8\">Evaluate the challenges in perception systems for AI.\u003C/li>\u003Cli data-list-item-id=\"ec84b134a236a4ea8ef6cebbb16136e78\">Utilize sensors to execute perception tasks and their applications in intelligent systems.\u003C/li>\u003Cli data-list-item-id=\"ecda3c7ab067dfe02cf181ef1b909d0e5\">Apply algorithms to train an image classifier.\u003C/li>\u003Cli data-list-item-id=\"ebc979444c03dc09faa1a7c21dd3ca71d\">Design an agent that can plan and act to achieve given objectives using noisy sensors and actuators.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef487417dd6092e603303f58b65a89b7b\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"ed0f02e970c83b5037be425ff3a05cdf5\">Personal computer with a major operating system and 8 GB RAM or higher.\u003C/li>\u003Cli data-list-item-id=\"ee2fefe8408f0f74cc2dd2fa23a6b71a3\">Reliable, strong Internet connection.\u003C/li>\u003Cli data-list-item-id=\"e4d7e8e118b445dd0a2a2dfa2f699b5f2\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4dd1f1588d194a92c6733360ff035104\">Matlab.\u003C/li>\u003Cli data-list-item-id=\"e01f2887fe4f34f9619631fffd55ec676\">Ubuntu 16.04.\u003C/li>\u003Cli data-list-item-id=\"e2d2d8809caecc707ac6a946a06bb8a4c\">ROS Kinetic.\u003C/li>\u003Cli data-list-item-id=\"e4cf59f66ae6f39ce0e329b50ed1a7aab\">Turtlebot3 packages.\u003C/li>\u003Cli data-list-item-id=\"e288b2800ecf2faad15846adf85ff8bb1\">PyTorch.\u003C/li>\u003Cli data-list-item-id=\"edff1a5377c1a4ffe191ff5084a2c7825\">GProlog 1.4.5.\u003C/li>\u003Cli data-list-item-id=\"e397c5b82d8b5fba020ac31f83b7a4213\">Cygwin (windows users).\u003C/li>\u003Cli data-list-item-id=\"ed4db6c71db5fde161f989ff68f224a6a\">Pip and Pgmpy.\u003C/li>\u003Cli data-list-item-id=\"edf4092888fe08cbea92e053e73cd8ab2\">Python 3.4 or higher.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creators\u003C/h3>\u003Cp>\u003Cstrong>Dr. Yu “Tony” Zhang\u003C/strong>\u003C/p>\u003Cp>Yu (“Tony”) Zhang is an assistant professor at Arizona State University, where he directs the Cooperative Robotic Systems laboratory. He graduated with a PhD in computer science from the University of Tennessee, Knoxville in 2012. His research interests include the intersection of artificial intelligence (AI) and robotics. His focus areas are innovating and applying AI and machine learning methods to human-robot teaming, multi-agent systems, distributed robotic systems and, more generally, human-in-the-loop AI systems. His research has been funded by federal governments and agencies such as the National Science Foundation, National Aeronautics and Space Foundation and Air Force of Scientific Research. Zhang has been highlighted with “Best Paper” Awards in premier robotics conferences. He is also a member/senior member of the program committees of major AI and robotics conferences, such as AAAI, IJCAI, IROS and ICRA.\u003C/p>\u003Cp>\u003Cstrong>Dr. Heni Ben Amor\u003C/strong>\u003C/p>\u003Cp>Heni Ben Amor is an assistant professor at Arizona State University where he leads the ASU Interactive Robotics Laboratory. He studied computer science at the University of Koblenz-Landau and earned a PhD in robotics from the Technical University Freiberg and the University of Osaka in 2010, where he worked with Hiroshi Ishiguro and Minoru Asada. He received the NSF CAREER Award, as well as the Outstanding Assistant Professor award in 2018. Prior to that, he was a research scientist at the Institute for Robotics and Intelligent Machines at Georgia Tech in Atlanta. Heni's research topics focus on artificial intelligence, machine learning, human-robot interaction, robot vision and automatic motor skill acquisition. He received the highly competitive Daimler-and-Benz Fellowship, as well as several “Best Paper” awards at major robotics and AI conferences. He is also on the program committee of various AI and robotics conferences, such as RSS, AAAI, IJCAI, IROS and ICRA.\u003C/p>\u003Cp>\u003Cstrong>Dr. Yezhou Yang\u003C/strong>\u003C/p>\u003Cp>Yezhou Yang is an assistant professor at the School of Computing and Augmented Intelligence at Arizona State University and director of the Active Perception Group. He received his MS and PhD degrees in computer science from the University of Maryland at College Park in 2013 and 2015 respectively. Prior to that, he obtained a BEng degree in computer science and engineering from Zhejiang University, China. His primary research focus is in Computer Vision and Robot Vision, especially exploring visual primitives in interpreting peoples’ actions and the scene’s geometry from visual input, grounding them by natural language as well as high-level reasoning over the primitives for intelligent systems. His research focuses on solutions to visual learning, which significantly reduces the time to program intelligent agents. He is a recipient of Qualcomm Innovation Fellowship 2011, Verisk AI faculty award and the NSF CAREER award in 2018.\u003C/p>\u003Cp>\u003Cstrong>Dr. Siddharth Srivastava\u003C/strong>\u003C/p>\u003Cp>Siddharth Srivastava is an assistant professor of computer science in the School of Computing and Augmented Intelligence at Arizona State University. Professor Srivastava was a staff scientist at the United Technologies Research Center in Berkeley. Prior to that, he was a postdoctoral researcher in the RUGS group at the University of California Berkeley. He received his PhD in computer science from the University of Massachusetts Amherst. His research interests include robotics and AI, with a focus on reasoning, planning and acting under uncertainty. His work on integrated task and motion planning for household robotics has received coverage from international news media. His dissertation work received a “Best Paper” award at the International Conference on Automated Planning and Scheduling and an Outstanding Dissertation award from the Department of Computer Science at UMass Amherst.\u003C/p>",{"title":61,"category":58,"description":62,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Data Mining","\u003Ch3 class=\"h3-large\">\u003Cstrong>About this course\u003C/strong>\u003C/h3>\u003Cp>Once called “knowledge discovery in databases,” the field of data mining continues to evolve. Advances in processing power and speed over the last decade have allowed users to move beyond manual, tedious and time-consuming practices to quick, easy data analysis that harnesses the power of machine learning and high-performance computing. This course will introduce you to the fundamentals of data mining and pattern recognition. You’ll gain a deeper understanding of data through hands-on experiences in the areas of big data analysis, classification, clustering and association rule mining. Advanced topics such as reinforcement learning, deep learning, transfer learning and Deep Mind for Google will also be covered. By the end of the course, you’ll be able to apply state-of-the-art data mining technology to real-world applications, analyze and compare competing techniques and design optimal solutions for a given set of application-driven constraints.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eaa9d58c17e0fcbddb4444b6641f77f61\">Data collection.\u003C/li>\u003Cli data-list-item-id=\"eabf7d5130fd56c0f0c0b293553940d50\">Data mining algorithms.\u003C/li>\u003Cli data-list-item-id=\"e5aa23cbe5d392af0cb0aa8b64babd284\">Data mining fundamentals.\u003C/li>\u003Cli data-list-item-id=\"e2b2fc6f28a3e244f3c3ba9c2c7a97cd1\">Data visualization.\u003C/li>\u003Cli data-list-item-id=\"eefaf5acee9f1814d00045294bc1dbd8d\">Deep learning.\u003C/li>\u003Cli data-list-item-id=\"e43776d5a1284af2fd5d9d00b80ccb75f\">Machine learning.\u003C/li>\u003Cli data-list-item-id=\"e4f9a96232f1c5d3022968a220d1ae3f1\">Reinforcement learning.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8ee5c278b6af2d53a442fd9be1bb1e2c\">Python\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Learning outcomes\u003C/strong>\u003C/h3>\u003Cp>Upon completing this course, you’ll be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eef099b7dfc2922221146e4240d54c561\">Apply common data mining algorithms to discover relationships and patterns in large datasets.\u003C/li>\u003Cli data-list-item-id=\"e3c77cfbe01e611293c4460938650eab8\">Differentiate among major data mining techniques such as classification, cluster analysis and association rule mining.\u003C/li>\u003Cli data-list-item-id=\"e0c77fb43590c94a9e765b6d581a00e59\">Implement more advanced learning algorithms, such as deep learning and reinforcement learning.\u003C/li>\u003Cli data-list-item-id=\"e3099b39b28cf48ed2ea44377418df168\">Utilize open-source tools to build a data mining project to solve a specific problem.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Estimated workload/time commitment per week\u003C/strong>\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">\u003Cstrong>Required prior knowledge and skills\u003C/strong>\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4df9e9f30a264d3de18068b552ae13a5\">Basics of statistics.\u003C/li>\u003Cli data-list-item-id=\"e739ef1a91df1e77a9c2c7cf2c82cc6c9\">Intermediate understanding of core concepts of data mining.\u003C/li>\u003Cli data-list-item-id=\"e5a0fc25ef61429aa2fbebe1a1ebbff2b\">Strong application skills\u003C/li>\u003Cli data-list-item-id=\"ec16bf5c627028d8f077ac0d5aed335ba\">Ability to effectively read C and Python code.\u003C/li>\u003Cli data-list-item-id=\"e4129bed77186322cd76e3532116122ca\">Confidence in executing at least one programming language, such as:\u003Cul>\u003Cli data-list-item-id=\"e7fb41a87817b82a0d7fde3dcc07e7007\">C.\u003C/li>\u003Cli data-list-item-id=\"e15007becd48fa41213bda65ca021d417\">C#.\u003C/li>\u003Cli data-list-item-id=\"ead31394c10cf3cac687dbe1108a12205\">C++.\u003C/li>\u003Cli data-list-item-id=\"ed2cb54029e5465b6e669ae7de21635e0\">Java.\u003C/li>\u003Cli data-list-item-id=\"ed68397c21823156f7fbca8e425dd420a\">MATLAB.\u003C/li>\u003Cli data-list-item-id=\"e3debaf3a2c83face4730f53b42c4bafd\">Python.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>The recommended language for the course projects is Python, however, you can also choose any other language to complete the projects. Please note that the course team will not be able to help you if you choose to use any language other than Python.\u003Cbr>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e53668f635b5066df59bd725f3310b4c8\">Experience reading technical specifications and documentation.\u003C/li>\u003Cli data-list-item-id=\"e645da8f87c7073f266bfee7f5e78e933\">Experience working with .csv data files.\u003C/li>\u003Cli data-list-item-id=\"e3f9dbb6aa5e44395f0e412ef987bf503\">Python programming.\u003C/li>\u003Cli data-list-item-id=\"e2396949f6ece0fcc2069ed6d09141a72\">Working experience with Python libraries: NumPy, Pandas, scikit-learn and pickle.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Technology Requirements\u003C/strong>\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e6cefec859b698e0e0744251f7a670e25\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e55d7144aa8e621193fff783177659126\">Personal computer with a major OS.\u003C/li>\u003Cli data-list-item-id=\"e5217d7a0c1b62bc94ded7545a62ffd82\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e96d018cb12e79cf8c2acf2122b1ca373\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/Other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e2bcec995148d30d2a21e45caed58ef3a\">Matplotlib 3.6.0.\u003C/li>\u003Cli data-list-item-id=\"edf856832fdcc71380f5e54fb4ec81c0b\">NumPy 1.23.1.\u003C/li>\u003Cli data-list-item-id=\"ec78732210a723240ad84bdff0286617c\">Pandas 1.4.3.\u003C/li>\u003Cli data-list-item-id=\"e38df2b5f6c1a0735bb6d6173b9655eff\">Python 3.10.9.\u003C/li>\u003Cli data-list-item-id=\"e54b77afcfeb5719ae319e6863cd62dad\">Scikit-learn 1.1.1.\u003C/li>\u003Cli data-list-item-id=\"e52aba87de5a8b60cc1fd1716bc05f2dc\">Scipy 1.8.1.\u003C/li>\u003Cli data-list-item-id=\"e2d10e0f8673cb2ef18949ee8aaf8870b\">Standard web access tools.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">\u003Cstrong>Creator\u003C/strong>\u003C/h3>\u003Cp>\u003Cstrong>Dr. Ayan Banerjee\u003C/strong>\u003C/p>\u003Cp>Dr. Banerjee is an assistant research professor in the School of Computing and Augmented Intelligence at Arizona State University. His research interests include pervasive computing in healthcare and analysis and safety verification of embedded system software. Dr. Banerjee currently focuses on data-driven analysis and modeling in many different domains including diet monitoring, gesture recognition and biological process modeling. He works closely with government agencies, such as the Food and Drug Administration, and medical agencies, such as Mayo Clinic. Dr. Banerjee is also interested in hybrid system-based modeling and safety verification of closed-loop control systems that interact with the physical environment, also known as Cyber-Physical Systems. In addition, his work includes developing management algorithms for sustainable data centers using renewable sources of energy.\u003C/p>",{"title":64,"category":58,"description":65,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Data Processing at Scale","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Database systems are used to provide convenient access to disk-resident data through efficient query processing, indexing structures, concurrency control and recovery. This course delves into new frameworks for processing and generating large-scale datasets with parallel and distributed algorithms, covering the design, deployment and use of state-of-the-art data processing systemPos, which provide scalable access to data.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e054fee0502006bc781ceed7bfcc65922\">Concurrency control in distributed parallel database systems.\u003C/li>\u003Cli data-list-item-id=\"e229de48b96e5650b2484d3945b96b27a\">Data management in cloud computing environments.\u003C/li>\u003Cli data-list-item-id=\"e1219c9fe1e0cab610104dbf5201b5b88\">Data management in Map/Reduce-based.\u003C/li>\u003Cli data-list-item-id=\"e961dd5492ef446feca1897be5defb132\">Distributed database design.\u003C/li>\u003Cli data-list-item-id=\"ea2e9a185d034bcf751c074a1cbba33b2\">Efficient query processing.\u003C/li>\u003Cli data-list-item-id=\"eed01dfeb185f91a914ac17be5597144a\">Indexing structures.\u003C/li>\u003Cli data-list-item-id=\"ec2b38998d663229a1c539f005db27749\">NoSQL database systems.\u003C/li>\u003Cli data-list-item-id=\"e6603d7f62178da4ee896295d2e2ac064\">Parallel query execution.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e965ec577ce28e0659f84dd0995e22fbf\">Amazon AWS.\u003C/li>\u003Cli data-list-item-id=\"e0686a9f72e267c22d721993b9a11e943\">Apache Spark.\u003C/li>\u003Cli data-list-item-id=\"e61301f9c278f30f50d6740ad00f07311\">Hadoop.\u003C/li>\u003Cli data-list-item-id=\"eb6f5419dae43b8850c89a18057028456\">Java.\u003C/li>\u003Cli data-list-item-id=\"e2c7df448acbbadbf01c99f5191ae948a\">MongoDB.\u003C/li>\u003Cli data-list-item-id=\"e28429ac6c95fc4325a3a0a7c95742645\">Neo4j.\u003C/li>\u003Cli data-list-item-id=\"ef3ab539243a9fce700b81e36676b7c07\">PostgreSQL.\u003C/li>\u003Cli data-list-item-id=\"e1a7599f7d15b222824869fc20e0dafd5\">Scala.\u003C/li>\u003Cli data-list-item-id=\"e056c29dcaffb357c891f99309fb0d86a\">Python.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e7d5d319dad483ee464fd40245855cb3f\">Basic statistics and computer science knowledge, such as computer organization and architecture, discrete mathematics, data structures and algorithms.\u003C/li>\u003Cli data-list-item-id=\"e28aa84e9a4a5420d34014045706804fd\">Knowledge of high-level programming languages (e.g., C++, Java) and scripting language (e.g., Python)\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec2ce982effe4cacb6fce21b39c3e95d1\">Ability to effectively read and write Java, Python and C++ code.\u003C/li>\u003Cli data-list-item-id=\"ec616cae2feb156f822b4149d5fc49565\">Ability to effectively read Python code.\u003C/li>\u003Cli data-list-item-id=\"ec290d7b86314c8d0aec41538950edc2b\">Apache Spark\u003C/li>\u003Cli data-list-item-id=\"ef80a7dff45deb1677a1b29d6cd3e1260\">Confidence executing in the following technologies:\u003Cul>\u003Cli data-list-item-id=\"ecb4337c281314aa931d400dc48137200\">PostgreSQL.\u003C/li>\u003Cli data-list-item-id=\"e4ae889d309007719e110533c227d9310\">Scala.\u003C/li>\u003Cli data-list-item-id=\"ec9d8b0ae5568165b8f3541a37f752abb\">Hadoop.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>Note: The course project will be completed using the language that the learner chooses. However, the course team will not be able to help the learner if they choose any language that is not Python or Java.\u003C/p>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eda31b66650485c7d4caa46cad9a654a2\">For the course to be effective, course participants should already have acquired basic database systems knowledge in the following:\u003Cul>\u003Cli data-list-item-id=\"e5bea0559f0205c51ea36620300753860\">SQL query language.\u003C/li>\u003Cli data-list-item-id=\"ea421c438bfa34bec1c5fad27a0ad1bdf\">Query processing and optimization.\u003C/li>\u003Cli data-list-item-id=\"ef2953ab5f6e485f480f79bd3463c00ac\">Data storage and indexing.\u003C/li>\u003Cli data-list-item-id=\"ecfe0963669558b2f464a5f0c174dcb39\">Transaction management.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e54d1438c5bd64af9e9743fe50b4937db\">Programming experience with a high level programming language (e.g., C++ and Java).\u003C/li>\u003Cli data-list-item-id=\"e8ed60deaa7f3fc56142cc2332e3626fd\">Scripting language programming (e.g., Python).\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ee082f0fa59ff3905f39c7ec28195de07\">Differentiate among major data models such as relational, spatial, and NoSQL.\u003C/li>\u003Cli data-list-item-id=\"ee979c49a888226c212da7e1ac843b6df\">Perform queries (e.g., SQL) and analytics tasks in state-of-the-art database systems.\u003C/li>\u003Cli data-list-item-id=\"ef4fb14338c01ad8f6acea34a5dd65624\">Apply leading-edge techniques to design/tune distributed and parallel database systems.\u003C/li>\u003Cli data-list-item-id=\"ec4717097dafaa15bcaa1d80ea29dc28b\">Utilize existing NoSQL database systems as appropriate for specified cases.\u003C/li>\u003Cli data-list-item-id=\"eaae1b97603349050803f7110fc51607e\">Perform database operations (e.g., selection, projection, join, and groupby) in state-of-the-art cluster computing systems such as Hadoop/Spark.\u003C/li>\u003Cli data-list-item-id=\"eebb69e909434039916900a47e544b181\">Perform scalable data processing operations (e.g., selection, projection, join, and groupby) in cloud computing environments, including Amazon AWS.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e7798069864c7fe323b86ed684221fe89\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"ebfee75cad9b808f0a0954e5ccd673a2a\">Personal computer with a major operating system and the following\u003Cul>\u003Cli data-list-item-id=\"e7630531af0c7cbe0d54a644d53f9863e\">8 GB RAM or higher\u003C/li>\u003Cli data-list-item-id=\"e4e47722d5eb6f2fe2f071def3a0e7434\">An x86-64 CPU.\u003C/li>\u003Cli data-list-item-id=\"e6a58062d4682d18b33854bcb86faee76\">Ability to install virtual machines.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"ec289789b76805b37fe20e70f88807c7b\">Reliable, strong internet connection with unrestricted access to key websites commonly used in software development activities (e.g., GitHub and StackOverflow).\u003C/li>\u003Cli data-list-item-id=\"ea7f038dd27262e98db6f6536d19d572d\">Webcam.\u003C/li>\u003C/ul>\u003Cp>Note: Computers with ARM processors (or any other architecture) will not work.\u003C/p>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cp>To complete course projects and assignments, the following will be required:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eed10ae152c5de8ab460eb9e917d96869\">Gradescope (for submission).\u003C/li>\u003Cli data-list-item-id=\"ea03ce4d57d1edb0fd4ff0284d08eaa83\">Python3 (recommended 3.10).\u003Cul>\u003Cli data-list-item-id=\"e96c56bb97ab4650a29bb9b3a35f35508\">psycopg2 == 2.9.9.\u003C/li>\u003Cli data-list-item-id=\"edbc7521821a3d992207795fa213f43aa\">Cython.\u003C/li>\u003Cli data-list-item-id=\"e0dc77d980bd089feb57a4b07398cb103\">Unqlite.\u003C/li>\u003Cli data-list-item-id=\"ea2b31fb97026a0ca54d608c6e0e21adb\">Py4j == 0.10.9.6\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e996dd99f04046c0a40c8265d809571cc\">Postgresql-16\u003C/li>\u003Cli data-list-item-id=\"e03606a9d363c8b1614019b469d727327\">Java JDK 17\u003C/li>\u003Cli data-list-item-id=\"ead37b13dfb2b7c75d254fbb0bf254f0a\">Hadoop == 3.3.6\u003C/li>\u003Cli data-list-item-id=\"e6218e8ca975bbfbad806a953ed6bf6bf\">Spark == 3.4.1\u003C/li>\u003C/ul>\u003Cp>Note: Some of the following resources may also be required: Amazon AWS, Cloud, GitHub, MongoDB, Neo4j.\u003C/p>\u003Ch3 class=\"h3-large\">Creators\u003C/h3>\u003Cp>\u003Cstrong>Mohamed Sarwat, Ph.D.\u003C/strong>\u003C/p>\u003Cp>Mohamed Sarwat is an assistant professor of computer science and the director of the data systems lab at Arizona State University. He is also an affiliate member of the Center for Assured and Scalable Data Engineering. Before joining ASU, Mohamed obtained his MSc and PhD degrees in computer science from the University of Minnesota. His research interest lies in the broad area of data management systems.\u003C/p>\u003Cp>\u003Cstrong>Ming Zhao, Ph.D.\u003C/strong>\u003C/p>\u003Cp>Ming Zhao, Ph.D. is an associate professor at Arizona State University in the School of Computing and Augmented Intelligence. Before joining ASU, he was an associate professor of the School of Computing and Information Sciences at Florida International University. He directs the Research Laboratory for Virtualized Infrastructure, Systems, and Applications. His research is in the areas of experimental computer systems, including cloud/edge, big data/AI and high-performance systems, as well as operating systems and storage in general. Dr. Zhao is a recipient of NSF CAREER award, Air Force Summer Faculty Fellowship, VMware Faculty Award, AWS Machine Learning Research Award and the Best Paper Award of the IEEE International Conference on Autonomic Computing. His research outcomes have been adopted by several production systems.\u003C/p>",{"title":67,"category":58,"description":68,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Data Visualization","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Visual representations generated by statistical models help us to make sense of large, complex datasets through interactive exploration, thereby enabling big data to realize its potential for informing decisions. This course covers techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology and cognitive science to enhance the understanding of complex data.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed2c2360f7a77b3ad286029d02a9aff0c\">Data transformations.\u003C/li>\u003Cli data-list-item-id=\"e3c51ff2edc6f2ef0461edcc855752ed8\">Time series analysis.\u003C/li>\u003Cli data-list-item-id=\"e5ec48699ae9b50d221f8b186d7411d15\">Exploratory querying.\u003C/li>\u003Cli data-list-item-id=\"ec5ef6cd8a403e2b5a573d678f1e6de9d\">Exploratory spatial data analysis.\u003C/li>\u003Cli data-list-item-id=\"eec546439be5e04b4baf09004db65159d\">Statistical graphics.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e2897684bcb7ea7a48abe3e59d57f762c\">Tableau.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and the necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e3b6907b88b32a7059bef370d036705df\">Basic statistics.\u003C/li>\u003Cli data-list-item-id=\"ebe19ddc660244b972ffb50ac953f484a\">Discrete mathematics.\u003C/li>\u003Cli data-list-item-id=\"e085a52eb52fa882c7453b458e49c83bb\">Computer organization.\u003C/li>\u003Cli data-list-item-id=\"ea6f0520d6016e1165bce7c46b7ad9798\">Architecture.\u003C/li>\u003Cli data-list-item-id=\"e6b267862793a96f9d6f86101200b202b\">Data structures.\u003C/li>\u003Cli data-list-item-id=\"e875cd6a1e169d0e579a893dc86e5a582\">Algorithms in computer science.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef5fa71cdc74b7f701ac85f25490ead40\">Knowledge of high-level programming languages (e.g., C++ and Java) and scripting languages (e.g., Python).\u003C/li>\u003C/ul>\u003Cp>Note: The course project will be completed using the language that the student chooses. However, the course team will not be able to help the student if they choose any language that is not Python, Java or C#.\u003C/p>\u003Cp>\u003Cstrong>Proficient Experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ebbfe81cd1e6ac997c14b83c879b2550f\">Python.\u003C/li>\u003Cli data-list-item-id=\"e7bef175f977e114817b553b789041543\">SQL.\u003C/li>\u003Cli data-list-item-id=\"e24a13999aa60d5a4fcc85b398d775d27\">JavaScript.\u003C/li>\u003Cli data-list-item-id=\"eef5dfa7d0feb34553b657cf0401bdc79\">HTML.\u003C/li>\u003Cli data-list-item-id=\"ed175f2d2b751af6c9cbe1cce9ec9d02f\">CSS.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e939d16a0f897e94cff2a30b2e015efcf\">Develop exploratory data analysis and visualization tools using Python and Jupyter notebooks.\u003C/li>\u003Cli data-list-item-id=\"e643122c4418f7bb7e52fe43aef115735\">Apply design principles for a variety of statistical graphics and visualizations including scatterplots, line charts, histograms, and choropleth maps.\u003C/li>\u003Cli data-list-item-id=\"e6c1b0f476f6bd8da0abb20bae9bc8b55\">Combine exploratory queries, graphics and interaction to develop functional tools for exploratory data analysis and visualization\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eb0162ad3995a987a52332d6b23e92535\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e1e322fd12f5c96b86c16821e5cad7a43\">Personal computer with the following:\u003Cul>\u003Cli data-list-item-id=\"e0426fb57a11316ab5167c46f3f564e50\">8 GB RAM or higher.\u003C/li>\u003Cli data-list-item-id=\"e7a7d6298a8bf1476032cd8eae5abbfce\">An x86-64 CPU.\u003C/li>\u003Cli data-list-item-id=\"e4ac2ca9ed25aec849fad9ef12e07d440\">Ability to install virtual machines.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e3e1627c554e90be6835ff7bfbcd2b283\">Reliable, strong internet connection with unrestricted access to key websites commonly used in software development activities (e.g., GitHub and StackOverflow).\u003C/li>\u003Cli data-list-item-id=\"e83c74c0d570da2581876a6e299370fa9\">Webcam.\u003C/li>\u003C/ul>\u003Cp>Note: Computers with ARM processors (or any other architecture) will not work.\u003C/p>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cp>To complete course projects/assignments, the following will be required:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec35fb006b160a4cc4f02c7dc20307866\">Ed Lessons (for submission).\u003C/li>\u003Cli data-list-item-id=\"ef3f8a64f122c75a409f688b0435d104d\">Jupyter Notebooks.\u003C/li>\u003Cli data-list-item-id=\"ef3ec4ab645689271eb1244f2ffdede68\">Python3 (recommended 3.11).\u003Cul>\u003Cli data-list-item-id=\"ec017056bc651b366e7f57df2d3b92ed6\">Sqlite3.\u003C/li>\u003Cli data-list-item-id=\"ecd613b4932c01c4b97e4ab73fcd75043\">Pandas 1.5.3.\u003C/li>\u003Cli data-list-item-id=\"eff4538512d1e1d5dfa30199640988a9f\">Numpy 1.26.2.\u003C/li>\u003Cli data-list-item-id=\"e34c05cba7cedc92a38a8c65dd7543fed\">Matplotlib 3.8.1.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"eb9c2f253f5e3a68c618cafef2b2e93ae\">JavaScript.\u003C/li>\u003Cli data-list-item-id=\"e66a1dfa1eb43b142d809dbb286a8f8de\">D3 JavaScript library.\u003C/li>\u003Cli data-list-item-id=\"eba77f750ef5f2d75328953bd6ed5b840\">HTML.\u003C/li>\u003Cli data-list-item-id=\"e1807070e815691bd16ee05af892389c6\">CSS.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creators\u003C/h3>\u003Cp>\u003Cstrong>Ross Maciejewski\u003C/strong>\u003C/p>\u003Cp>Dr. Ross is an associate professor at Arizona State University in the School of Computing and Augmented Intelligence and director of the Center for Accelerating Operational Efficiency, a Department of Homeland Security Center of Excellence. His primary research interests are in the areas of geographical visualization and visual analytics, focusing on public health, dietary analysis, social media, criminal incident reports and the food-energy-water nexus.\u003C/p>\u003Cp>\u003Cstrong>Professor Huan Liu\u003C/strong>\u003C/p>\u003Cp>Professor Huan Liu joined Arizona State University in 2000 after conducting research in Telecom (Telstra) Australia Research labs and teaching at the National University of Singapore. He has extensive experience in research and development. Liu’s research and teaching focuses on machine learning, data mining and real-world applications.\u003C/p>\u003Cp>\u003Cstrong>K. Selcuk Candan\u003C/strong>\u003C/p>\u003Cp>K. Selcuk Candan is a professor of computer science and engineering at Arizona State University and the director of ASU’s Center for Assured and Scalable Data Engineering. His primary research interest is in the areas of management and analysis of non-traditional, heterogeneous and imprecise data.\u003C/p>",{"title":70,"category":58,"description":71,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Statistical Machine Learning","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Deriving generalizable models from some given training data is central to statistical machine learning. Statistical machine learning has found wide applications in many fields, including artificial intelligence, computer vision, natural language processing, finance and bioinformatics. This course provides a systematic introduction to common learning paradigms in statistical machine learning, accompanied by an exploration of a set of foundational algorithms. Main topics covered include supervised learning, unsupervised learning and deep learning.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed02d252d8773867466739b5b500a3fc5\">Mathematical foundations for machine learning.\u003C/li>\u003Cli data-list-item-id=\"e0ae4cbb6f30adb81403052b17fe4c1d0\">Maximum likelihood estimation.\u003C/li>\u003Cli data-list-item-id=\"e731fcbce6bf87390cdaab50b48c069bf\">Naive Bayes classification.\u003C/li>\u003Cli data-list-item-id=\"e46e6fd51d03b72058cfa29d3dae6f7e2\">Logistic regression.\u003C/li>\u003Cli data-list-item-id=\"ed576a443143de90c04cdc2120085ad2b\">Support vector machines.\u003C/li>\u003Cli data-list-item-id=\"ef56b11413a93d62305126c5d3fa9fd65\">Probabilistic graphical models.\u003C/li>\u003Cli data-list-item-id=\"e92b307c278d427dd207895f83eb90ec3\">Mixture models.\u003C/li>\u003Cli data-list-item-id=\"e35b39699eb7b167c2d6338d49d5f2313\">K-means clustering.\u003C/li>\u003Cli data-list-item-id=\"e4ee4ad5a59dcd26ce486fca4f20f7a8f\">Spectral clustering.\u003C/li>\u003Cli data-list-item-id=\"ef8bef077f31e0c3b0cf5b364876b078f\">Dimensionality reduction.\u003C/li>\u003Cli data-list-item-id=\"eaf37bf5fc1ca187dd38c648ff8612a47\">Principal component analysis.\u003C/li>\u003Cli data-list-item-id=\"e83daf0a5dcd922ca3febc1d5848d04ac\">Neural networks and deep learning.\u003C/li>\u003Cli data-list-item-id=\"e10db020bfd0d568aacd5e4c865a69c8b\">Convolutional neural networks.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e52c0adca05f7950bc98256ff64039355\">Python.\u003C/li>\u003Cli data-list-item-id=\"e250be5a173e2740fe22b355260c6e9d3\">Matlab.\u003C/li>\u003Cli data-list-item-id=\"ea2156b390c444aa086d151eb5ee6d4c0\">Jupyter Notebooks.\u003C/li>\u003Cli data-list-item-id=\"e63827388c1fb21d81c2b45a1b1694ddd\">Google Colab.\u003C/li>\u003Cli data-list-item-id=\"ed7aca65f4843e7d5a11580d9d0cbb914\">PyTorch.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and the necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient Mathematical Skills and Theoretical Understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"efa83fabedd1c0674ba79482c268e0a53\">Basics of linear algebra.\u003C/li>\u003Cli data-list-item-id=\"ed36e4a3e11c7777722e96bde7b79c775\">Basics of probability and statistics.\u003C/li>\u003Cli data-list-item-id=\"e799726d25ac545c61a2f2b0da0086978\">Basics of calculus and set theory.\u003C/li>\u003Cli data-list-item-id=\"e9656251763c80b9ae4184cb950940c1f\">Basics of algorithm design and analysis.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong Application Skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec53d7f445b66742c87717c212355c6f9\">Programming in Python.\u003C/li>\u003Cli data-list-item-id=\"efc28bade7a08fb57c1124b4c755b30b2\">Ability to effectively read Python code.\u003C/li>\u003Cli data-list-item-id=\"e91fc8cdfcc0f170b39a5917d51780917\">Confidence in executing at least one programming language:\u003Cul>\u003Cli data-list-item-id=\"e173c3e715f4caac4b6e3c7fecd779958\">Python.\u003C/li>\u003Cli data-list-item-id=\"e8249fb1c0fea2102720d418336885b22\">Matlab.\u003C/li>\u003Cli data-list-item-id=\"eaf47060668bf5d071c08ecae9b9367a8\">R.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Cp>Note: The course project will be completed using the language that the learner chooses. However, the course team will not be able to help the learner if they choose any language that is not Python, Matlab.\u003C/p>\u003Cp>\u003Cstrong>Proficient Experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ecbb5a05a462b4ede258362e9861cfbe1\">High-level programming language (Python or Matlab).\u003C/li>\u003Cli data-list-item-id=\"eb01565ff83877375bf9a4628ce3279ed\">Ability to implement machine learning algorithms using python.\u003C/li>\u003Cli data-list-item-id=\"e296e59c04b4e75747532513615314aab\">Familiarity with any one of the following frameworks:\u003Cul>\u003Cli data-list-item-id=\"e9884c447bb51b05bfdea4d496790c9ab\">Jupyter Notebook.\u003C/li>\u003Cli data-list-item-id=\"e693694ef0b5cdf0b982a73c442b01796\">Google Colab.\u003C/li>\u003Cli data-list-item-id=\"e5f253a5aa417902a408a1fcad4cfc90a\">PyTorch.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"ebe72d7c0ce8d3c69e3642af387e2ff19\">Familiarity with the following tools/libraries:\u003Cul>\u003Cli data-list-item-id=\"e0d8ab43460022872bab37e4366160f7c\">Numpy.\u003C/li>\u003Cli data-list-item-id=\"e769109532ea9ef0704a4ed01b7c4b1bd\">Pandas.\u003C/li>\u003Cli data-list-item-id=\"e1f176245394dd3fc2132ac15ad2d3c07\">Tensorflow.\u003C/li>\u003Cli data-list-item-id=\"e7ad00e2e14d814e27a6fac9d0329a62c\">Keras.\u003C/li>\u003Cli data-list-item-id=\"e8d0d13d69facaff773aa534ee7b454a5\">Matplotlib.\u003C/li>\u003Cli data-list-item-id=\"e32c157f25a25b9e305829dcad740f4a4\">Scikit Learn.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>\u003Cbr>Learners completing this course will be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e728e363d17211450dcbe8cf50d37d46d\">Distinguish between supervised learning and unsupervised learning.\u003C/li>\u003Cli data-list-item-id=\"e3ca7d4f5647cbab36cd0886c99bbb686\">Apply common probability distributions in machine learning applications.\u003C/li>\u003Cli data-list-item-id=\"e4c118766a85a708898f91b528ce90ad8\">Use cross validation to select parameters.\u003C/li>\u003Cli data-list-item-id=\"eeaf5cd576f56fa6ed4073dc98cec332a\">Use maximum likelihood estimate (MLE) for parameter estimation.\u003C/li>\u003Cli data-list-item-id=\"e79b2e191b28ecf04c07e74424b3a50b7\">Implement fundamental learning algorithms, such as logistic regression and k-means clustering.\u003C/li>\u003Cli data-list-item-id=\"e06b93dabdc60b67c51820b029d055511\">Implement more advanced learning algorithms, such as support vector machines and convolutional neural networks.\u003C/li>\u003Cli data-list-item-id=\"e86ed32b8f5b570478decec04a681d2e7\">Design a deep network using an exemplar application to solve a specific problem.\u003C/li>\u003Cli data-list-item-id=\"e0ad9ab38e301bbd2a1cf258d995f657b\">Apply key techniques employed in building deep learning architectures\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e29fa1ea2242ffae41e8bcde60a7d2c99\">Personal computer with major OS and 8 GB RAM or higher and an x86-64 CPU.\u003C/li>\u003Cli data-list-item-id=\"edfae7dba835721bd792ec51a51307b04\">Reliable, strong Internet connection.\u003C/li>\u003Cli data-list-item-id=\"e31b437d3386c173266787479cbf98473\">Webcam.\u003C/li>\u003Cli data-list-item-id=\"edecd79a0d18d8d6c5439ec505d55501a\">Microphone.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eb7c85665005523c6a8da3f6d21d7b216\">Ed Lessons (for submission).\u003C/li>\u003Cli data-list-item-id=\"ee750d3b4e2a20ec53652e487879eb5fa\">PyTorch 2.1.1.\u003C/li>\u003Cli data-list-item-id=\"e851bf33da4f782d0b04d45f822c03712\">Matlab.\u003C/li>\u003Cli data-list-item-id=\"e7209bd55432335744a8141ad8b24bbd8\">GPU environments like Google Colab/Kaggle or personal setup on your own.\u003C/li>\u003Cli data-list-item-id=\"e536e3013a5b062c8a7cf81603603da02\">Anaconda.\u003C/li>\u003Cli data-list-item-id=\"e12a1a170cc749142cdf6b733a51fe2b0\">Jupyter Notebook.\u003C/li>\u003Cli data-list-item-id=\"e1541d24fda0304cafc275b4fe6614e8a\">Tensorflow.\u003C/li>\u003Cli data-list-item-id=\"e52cb9c088679bf8ef8205c68fec6f89d\">Python3 (recommended 3.11).\u003Cul>\u003Cli data-list-item-id=\"e0f23447a4d619f648126ec3641ef61fd\">Pandas 1.5.3.\u003C/li>\u003Cli data-list-item-id=\"e3aa8597cf91704a45be4b4fe371ecac4\">Matplotlib 3.8.1.\u003C/li>\u003Cli data-list-item-id=\"e63b078f1701f7961d42a87854792cc01\">Numpy 1.26.2.\u003C/li>\u003Cli data-list-item-id=\"e9920ad752c135c3b034d618c2c46af39\">Scipy 1.11.4.\u003C/li>\u003Cli data-list-item-id=\"ec097ddc7ba2d8c696d9ca22acaeccb76\">Scikit-learn 1.3.2.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>Baoxin Li\u003C/strong>\u003C/p>\u003Cp>Baoxin Li is currently a professor and the chair of the Computer Science and Engineering Program and a Graduate Faculty Endorsed to Chair in the Electrical Engineering and Computer Engineering programs. From 2000 to 2004, he was a Senior Researcher with SHARP Laboratories of America, where he was the technical lead in developing SHARP’s HiIMPACT Sports™ technologies. He was also an Adjunct Professor with Portland State University from 2003 to 2004. His general research interests are on visual computing and machine learning, especially their application in the context of human-centered computing.\u003C/p>",{"title":73,"category":74,"description":75,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Advanced Software Analysis and Design","Elective","\u003Ch3>About this course\u003C/h3>\u003Cp>Software analysis and design propose optimal software solutions to solve complex problems. In this hands-on course, you’ll apply methodologies, frameworks and fundamentals to techniques of design, implementation and software architecture. Additional topics include object-oriented analysis and design, software architecture and design principles, quality attributes of software architecture, stakeholder perspectives and team approaches, mobile applications, service-oriented architecture and microservices-based web applications and software engineering perspectives for robotics and autonomous systems.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e9ce058393991d685ec6e82ea22ba9ec5\">Architecting mobile applications.\u003C/li>\u003Cli data-list-item-id=\"e2f0dfafd1e2fb6b38192a03aec9bd4dd\">Architecting robotics and autonomous systems.\u003C/li>\u003Cli data-list-item-id=\"e797650a994d1ae6bd3dcace4e2892ddb\">Architecting service-oriented architecture and microservices-based web applications.\u003C/li>\u003Cli data-list-item-id=\"ef2501af9cec82d502069fa68e5843dd2\">Nature of software systems.\u003C/li>\u003Cli data-list-item-id=\"e25dbe6653d438218c657571014c6dddc\">Object-oriented analysis and design.\u003C/li>\u003Cli data-list-item-id=\"e33b3b11931e26e85bbf1ce38944c2235\">Quality attributes of software architecture.\u003C/li>\u003Cli data-list-item-id=\"ed5d36538a94545aa8d0d1da062fe78bb\">Significance of software analysis and design.\u003C/li>\u003Cli data-list-item-id=\"e4c4146fa056617f8d010b91d0471ec31\">Software architecture and design principles.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eed72d110786292b9d6a5a6f9d4ea281e\">C#.\u003C/li>\u003Cli data-list-item-id=\"e02d9d98c4a40f96d6c1519ef16c799e5\">Java.\u003C/li>\u003Cli data-list-item-id=\"ef0aadf683538ccc63563f7fc62d8ea64\">Swift.\u003C/li>\u003Cli data-list-item-id=\"e295a4107def096975ee5a64f5c7b973a\">Xcode11/Android Studio.\u003C/li>\u003C/ul>\u003Ch3>Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e35788cb2b35d0e9f8833ec052429b717\">Project management.\u003C/li>\u003Cli data-list-item-id=\"ea0f14670300425b1f6663ed12ed844b9\">Software architectures.\u003C/li>\u003Cli data-list-item-id=\"e1f633286d10697a88a748f7573de76e0\">Software life cycle models.\u003C/li>\u003Cli data-list-item-id=\"ec23fc799b20569aab378c4e6275b7840\">Team development environments and methodologies.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e18be0ca5a54065e35a856c602f0bb555\">iOS App or Android App (you need at least one to complete some coursework).\u003C/li>\u003Cli data-list-item-id=\"e58ebce27f7099d17634f3ef7e4b78330\">For Android app: Android SDK using Java.\u003C/li>\u003Cli data-list-item-id=\"e84d82097f6590d8d965d8d5de3eb4d62\">For iOS app: XCode 11 (or newer), programming language — Swift (no Objective-C) is strongly preferred.\u003C/li>\u003C/ul>\u003Cp>Note: You can complete the course project using the language that you choose. However, the course team won’t be able to help you if you choose any language that is not Java or Swift.\u003C/p>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ea6f0b8082f5dc7a989b94f9ac7a593e2\">Professional background:\u003Cul>\u003Cli data-list-item-id=\"e1fe7654a6928d391749036ba4fb8715f\">Exposure to object-oriented programming.\u003C/li>\u003Cli data-list-item-id=\"e0319962da75314245d1ba6b02b00223c\">Basic knowledge of software process modules.\u003C/li>\u003Cli data-list-item-id=\"e205f8652aa0470908b28363e5a7d2631\">Class diagrams and experience working on a software development team.\u003C/li>\u003Cli data-list-item-id=\"e6ea3be0eec1aa7d5376de71da2c57cb3\">Experience developing software following a disciplined development process.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e69e46d8d5da3601441af8e7a9a95e1c3\">Familiarity with the following tools:\u003Cul>\u003Cli data-list-item-id=\"e1067de614cc981e3ca983cdfaed23ad4\">UML Modeling tools such as Astah.\u003C/li>\u003Cli data-list-item-id=\"eb6de6b57393566a292f62822f80ff20d\">C4 Modeling tools such as draw.io.\u003C/li>\u003Cli data-list-item-id=\"e6a6b0ac90ef9d28a40ba6d759e78a008\">Visual Studio.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"ee2121398b8ba96c7b66a23ed63684b15\">Basic understanding of SOAP and REST.\u003C/li>\u003Cli data-list-item-id=\"eadac1c57e0eae9e713e6e287df46c83a\">Knowledge of developing UML class diagrams that represent the design and implementing object-oriented design in Java.\u003C/li>\u003Cli data-list-item-id=\"e1096c1fda21d563a2534234bbc2b9260\">Basic knowledge of:\u003Cul>\u003Cli data-list-item-id=\"e796130b776687e920bf39dc69218b881\">Application building.\u003C/li>\u003Cli data-list-item-id=\"ea0e198f10b78b02e5c904cb54e5cc6f0\">Service invocation.\u003C/li>\u003Cli data-list-item-id=\"e8f9c8813ff5eda0c7e6599c6f09bfb85\">Web service development.\u003C/li>\u003C/ul>\u003C/li>\u003C/ul>\u003Ch3>Learning outcomes\u003C/h3>\u003Cul>\u003Cli data-list-item-id=\"e22630f5d038fc36c4c6d67af6cd4b58b\">After completing this course, you'll be able to:\u003C/li>\u003Cli data-list-item-id=\"e07429403c8e4be155e2579fff7eb26cd\">Apply appropriate architectural styles in designing and implementing software systems in different application domains including, but not limited to: mobile, service-oriented and autonomous and robotics systems.\u003C/li>\u003Cli data-list-item-id=\"e5ec3fbec7b30c05cf27c5e89be38a927\">Apply standardized structural and behavioral modeling methods and practices for software systems.\u003C/li>\u003Cli data-list-item-id=\"e469ff4d9b5ff020be6f59e99d5cf6314\">Articulate software specification analysis and design concepts with foundations.\u003C/li>\u003Cli data-list-item-id=\"edd6a116d7ac3c55f9510fcf5da88875f\">Demonstrate engineering methodology in analysis and design of a model-based software system.\u003C/li>\u003Cli data-list-item-id=\"e3b12f71927f3b7fb8933642fedb86429\">Develop structural and behavioral specifications with advanced features using the Unified Modeling Language (UML), C4 Model for Software Architecture (C4), frameworks and tools.\u003C/li>\u003Cli data-list-item-id=\"e199cf85c0dfe0358ada8222537ca5119\">Develop, implement and test consumer/producer-style software systems using software design patterns.\u003C/li>\u003Cli data-list-item-id=\"e1f943cf3cd746b8793abeb0f5375993c\">Evaluate and apply appropriate software architecture functional and nonfunctional concepts, quality attributes and styles in designing a new software system.\u003C/li>\u003Cli data-list-item-id=\"e29deb4fdda6deeeba4bfb25cf8da2407\">Evaluate software complexity and scale traits in modern software systems.\u003C/li>\u003C/ul>\u003Ch3>Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3>Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"efb817be92bd5f934d44bd96be49d3813\">Using a Mac computer is highly recommended.\u003C/li>\u003Cli data-list-item-id=\"ef42fd7c484b56fc189976329879a4df6\">A computer that is able to run Java IDE, Visual Studio (to create C# Projects) and Android SDK.\u003C/li>\u003Cli data-list-item-id=\"e2e1b56ee1410e23bf863ea7458870c34\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"ef0c618b9f0cbe3b9454865ab2867d974\">Personal computer with 8 GB RAM or higher and an x86-64 CPU.\u003C/li>\u003Cli data-list-item-id=\"e1fbf1458201246c4afbaf1d9ca373eaa\">Must be able to install virtual machines on this computer.\u003C/li>\u003Cli data-list-item-id=\"e15efaea5f78835b9c5d428ffa9aec686\">Computers with ARM processors (or any other architecture) won’t work.\u003C/li>\u003Cli data-list-item-id=\"ebb390eadf28bcde3b40059ec6f8c4e7a\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e83a362667b83abeb5f01d96816c72d1f\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e70e23c57010330cc98af79b8695c0d37\">Java development IDE: VSCode or Eclipse.\u003C/li>\u003Cli data-list-item-id=\"e8d1e0739e5bfddd167370d9d628d7dd0\">Java 17.\u003C/li>\u003Cli data-list-item-id=\"e386cf7149f68e1d4b2282b1328799dab\">Visual Studio 2019.\u003C/li>\u003Cli data-list-item-id=\"e567762de6ca87befb68db0a767476a7b\">XCode 10 (or newer)/Android Studio.  \u003C/li>\u003Cli data-list-item-id=\"e624362adddbefae0c22fc704059a0b2e\">UML modeling tools, such as Astah.\u003C/li>\u003Cli data-list-item-id=\"e29a77230446edefa6a9b9b4f866cc2a6\">C4 modeling tools, such as draw.io.\u003C/li>\u003Cli data-list-item-id=\"e69b368872e00e6806537a537e03c3248\">iOS App or Android App (you need at least one to complete some coursework).\u003Cul>\u003Cli data-list-item-id=\"e32d4dcff11baa8d4922c495b6284af60\">For iOS app: XCode 10 (or newer), programming language Swift (no Objective-C) — \u003Cstrong>strongly preferred\u003C/strong>.\u003C/li>\u003Cli data-list-item-id=\"efbd2927d15a9207d6a5ba5373e13d927\">For Android app: Android SDK using Java.\u003C/li>\u003C/ul>\u003C/li>\u003Cli data-list-item-id=\"e2048ea52a28418546fd51c984c140d01\">A virtual desktop platform, Apporto (optional), can be used in place of a local setup.\u003C/li>\u003C/ul>\u003Cp>Note: All of these software systems are either open source, free download or can be downloaded through ASU’s My Apps free of charge.\u003C/p>\u003Ch3>Creator\u003C/h3>\u003Cp>Janaka Balasooriya\u003C/p>\u003Cp>Janaka Balasooriya joined Arizona State University in 2007. Prior to joining ASU, Balasooriya was a postdoctoral fellow at Missouri University of Science and Technology. With several years of industry experience as a software engineer, his research interests span the areas of distributed computing and software engineering, including service-oriented computing, cloud computing and software testing. Balasooriya has taught courses in distributed computing, mobile computing, software testing, algorithms and data structures, software engineering and programming languages. Balasooriya is an ASU Barrett Honors faculty and serves as a faculty honors advisor to CS and CSE students. He is also a program committee member in several premier conferences, including IEEE Service Oriented Computing and IEEE Cloud Computing Conferences since 2007.\u003C/p>",{"title":77,"category":74,"description":78,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Engineering Blockchain Applications","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>Blockchain technology is revolutionizing digitalization prospects for many industries and is emerging as a rapidly growing field. By examining the architecture of the technology, this course ensures that you’ll be well-versed in blockchain fundamentals. It’s also designed to put you on the leading edge by presenting the abstract nature of blockchain technology and emphasizing its broad applicability. Topics include the mathematical and cryptographic underpinnings of the technology, mining, consensus protocols, networking and decentralized governance.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"eaf45612ecb5e3c5aa8ca4278745ab51c\">Blockchain consensus.\u003C/li>\u003Cli data-list-item-id=\"e6999be1f822f30d5aa23f4d2adfa5518\">Blockchain technology’s abstractions and applications.\u003C/li>\u003Cli data-list-item-id=\"e64d454c2ebc06007f2ea280382f04759\">Cryptographic and mathematical foundations of blockchain technology.\u003C/li>\u003Cli data-list-item-id=\"eb575268cefec2f9b597040015205b1b7\">Governance.\u003C/li>\u003Cli data-list-item-id=\"eece641a7cd811331bcb4567266c56705\">Hash functions.\u003C/li>\u003Cli data-list-item-id=\"e8422ad001cdd35d79b1acfccc0dfa2bc\">Mining.\u003C/li>\u003Cli data-list-item-id=\"ee7e414db5b61438753442cff0079ad47\">Peer-to-peer networks.\u003C/li>\u003Cli data-list-item-id=\"efc887c0ee9456420898f34912da606bf\">Transactions on the blockchain.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e98804ed952108b3a0a62f4fe022f1dbd\">Hyperledger Fabric.\u003C/li>\u003Cli data-list-item-id=\"e27003719a9196b222a41b5152d3d4ca0\">Solidity.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and the necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e45a6d3faeb4ee7931a1578e61ca7b022\">Algebra.\u003C/li>\u003Cli data-list-item-id=\"edeec9d1d26a48fe166bb65cf15d64299\">Algorithms.\u003C/li>\u003Cli data-list-item-id=\"e8bb7ef7b4cd1b82fa6a0c2ad6d45c39e\">Computer networking.\u003C/li>\u003Cli data-list-item-id=\"e5f8058f412ca862d790895ad55d217c5\">Data structures.\u003C/li>\u003Cli data-list-item-id=\"eff05fa0ca8c491f7fa9fbd73f4b45391\">Linear algebra.\u003C/li>\u003Cli data-list-item-id=\"e20c61e075c06249e55f61fd84655310e\">Operating systems.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cp>Confidence in executing at least one programming language, such as:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed1c4b37dad97b1be183224eb14d7579a\">C.\u003C/li>\u003Cli data-list-item-id=\"e0b70990d44733e551e90d0f9200abfa5\">C++.\u003C/li>\u003Cli data-list-item-id=\"e3deb13521e03b22efd76d34e7d5f4314\">Java.\u003C/li>\u003Cli data-list-item-id=\"ec85f5378624ba1394e2d9be0aae4460a\">Python.\u003C/li>\u003C/ul>\u003Cp>Note: You’ll complete course projects using Node.js and Solidity.\u003C/p>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e0e3d09a8766e5993b3aa659f1b35316e\">Experience in programming (e.g. Node.js, Docker, Unix).\u003C/li>\u003Cli data-list-item-id=\"e7fa44f2d5b69769a0ed0ab30b6541c8f\">Familiarity with Node.js is preferred.\u003C/li>\u003Cli data-list-item-id=\"e51365902e73494ec431f26e7d2a6fc7f\">Knowledge of networking concepts.\u003C/li>\u003Cli data-list-item-id=\"e71811d450b3ec240503cff2d67af2bd3\">Understanding of Docker containers.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cp>After completing this course, you'll be able to:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4f0998bcbc5c26f2f4414bcbac338cb0\">Apply the Elliptic Curve Digital Signature Algorithm to identity management and computer security.\u003C/li>\u003Cli data-list-item-id=\"e9cef05b149f5b2d44af60b29d3b6f5f6\">Compare proof-of-work secured blockchains’ security to alternate security methods.\u003C/li>\u003Cli data-list-item-id=\"e40039a8b57d3d7b992c3a279e05b60eb\">Deploy an ERC-20 Token Standard contract on an Ethereum-based network using Solidity.\u003C/li>\u003Cli data-list-item-id=\"e91c7961bf3ebf4cb96628b025778c013\">Determine the validity of chains given general consensus rules.\u003C/li>\u003Cli data-list-item-id=\"e518d14f31e660b1a257d40a403335dd3\">Determine whether changes in consensus rules for a Nakamoto network will result in a successful protocol fork.\u003C/li>\u003Cli data-list-item-id=\"e90e07e8637549a57e16fab443b0b5428\">Evaluate an optimal mix of network design and operational parameters to ensure network scalability and throughput.\u003C/li>\u003Cli data-list-item-id=\"eba426bed9d0264c0a6ca47690759ee4b\">Evaluate the trade-off between security and computational complexity.\u003C/li>\u003Cli data-list-item-id=\"e8672edb2f1c43451dbab9ae90249e809\">Navigate relevant documentation and resources to engineer blockchain applications.\u003C/li>\u003Cli data-list-item-id=\"e91eaf778642c8e48011697f42dfc31f3\">Navigate the complexities of applying blockchain technology to an area of personal interest.\u003C/li>\u003Cli data-list-item-id=\"e943eef196487a7ddd5d70d94d75c9281\">Use Hyperledger Composer to build a business application.\u003C/li>\u003Cli data-list-item-id=\"ed9b2c76b23d6afd182c0e1d4fa281215\">Use Hyperledger Fabric to build a custom decentralized blockchain network configuration.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e7b77faa58a62cf044012ff5c5ff0f4f6\">Linux is strongly recommended.\u003C/li>\u003Cli data-list-item-id=\"e0cc0a863e36a2f9554049144e78e507f\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e1e72fe9906a945a9bfc22f4824f99c56\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e253728569670d362e8c4cf1705f5d993\">Standard personal computer with a major operating system (OS).\u003C/li>\u003Cli data-list-item-id=\"edd02b941393fa24083d14016144937fd\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e1a4ba7c5864eb6c9cdb9e7e9e270f5e4\">Linux, Windows or MacOS with the ability to install Docker containers.\u003C/li>\u003Cli data-list-item-id=\"e0e6fe5f6bdc3fdb91f0327fe9a967b2d\">Node.js supported IDE.\u003C/li>\u003Cli data-list-item-id=\"e1454306424df757604e5567271b1b79d\">Npm install.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creators\u003C/h3>\u003Cp>\u003Cstrong>Dragan Boscovic\u003C/strong>\u003C/p>\u003Cp>Dragan Boscovic is a research professor in the School of Computing and Augmented Intelligence (SCAI), the technical director of SCAI’s Center for Assured and Scalable Data Engineering and a distinguished visiting scholar, mediaX, at Stanford University. Dr. Boscovic also leads ASU’s Blockchain Research Lab, where his team’s mission is to advance the research and development of blockchain-based technologies for use in business, finance, economics, mathematics, computer science and other areas of potential impact. He also holds a PhD. in EE and CS, numerical electromagnetic modeling from University of Bath, United Kingdom (1991) and a Magistar in EE, eq. PhD, Microwave and Optoelectronics from University of Belgrade, Serbia (1988). Dr. Boscovic has 25 years of high-tech experience acquired in an international setup (e.g. UK, France, China, USA) and is uniquely positioned to contribute to data-driven technical advances in today’s global data-intensive technology arena. He’s a lateral thinker with broad exposure to a wide range of scientific methods and business practices. He has a proven track record in conceiving strategies and managing development, investment and innovation efforts as related to advanced data analysis services, user experience, mobile and IoT solutions and platforms.\u003C/p>\u003Cp>\u003Cstrong>Darren Tapp\u003C/strong>\u003C/p>\u003Cp>Darren Tapp was involved in the development of Bitcoin and is now a researcher on the digital cash (cryptocurrency) development team at dash.org, a nonprofit blockchain technology startup. He earned his doctorate in mathematics from Purdue University in 2007 and holds both a bachelor’s degree in physics and mathematics and a master’s degree in mathematics from the University of Kentucky. Most recently, he’s taught both on campus and online at schools including Southern New Hampshire University, NHTI-Concord’s Community College and Hesser College. He lives in New Hampshire, where he volunteers to promote STEM subjects to high-school-aged Big Fish Learning Community members.\u003C/p>\u003Cp>\u003Cstrong>Jeremy Liu\u003C/strong>\u003C/p>\u003Cp>Jeremy Liu has been an active contributor, researcher and practitioner in the blockchain industry since 2015. Initially focusing his efforts on cryptocurrency investments and payments, Liu was the cryptocurrency portfolio manager and payment network architect for a blockchain startup in Arizona creating a cryptocurrency payment processing network. Transitioning away from investments, Liu began focusing on education, governance and policy in 2017, working with the Blockchain Research Lab at Arizona State University in the areas of block propagation techniques, voting systems and zero-knowledge proofs. His current, active independent work includes applying network-based models of production, supported by decentralized technologies, to higher education, public goods funding and innovation inducement ecosystems. Liu also advises multiple organizations on their blockchain technology strategies and leads development teams creating various web3 projects. He holds a bachelor's degree in mathematics and a master’s degree in science and technology policy.\u003C/p>",{"title":80,"category":74,"description":81,"youtubeVideoId":32,"youtubeVideoDuration":32,"youtubeVideoUploadDate":32},"Intro to Deep Learning in Visual Computing","\u003Ch3 class=\"h3-large\">About this course\u003C/h3>\u003Cp>In recent years, deep learning has revolutionized the field of artificial intelligence. Modern deep neural networks extract patterns in large amounts of data to solve complex real-world problems. In this course, you’ll learn the basic principles of designing and training deep neural networks with a focus on computer vision. You’ll also learn the founding principles for training deep neural networks, along with techniques to train and optimize them. Additionally, you’ll gain an understanding of the principles of CNNs, generative modeling for unsupervised learning and much more.\u003C/p>\u003Cp>\u003Cstrong>Specific topics covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ed4e0e80a506e60bfa8ffeca39c19a3e2\">Introduction to visual representation and the fundamentals of machine learning.\u003C/li>\u003Cli data-list-item-id=\"e23f3af054444ee1f0747b4c92275661b\">Modern convolutional neural networks.\u003C/li>\u003Cli data-list-item-id=\"e515e34a4c4b70154127744cc32e6c166\">Neural networks and backpropagation.\u003C/li>\u003Cli data-list-item-id=\"e0f512063eda231b0e8ad500792b7328f\">Optimization techniques for neural networks.\u003C/li>\u003Cli data-list-item-id=\"ebdbbccfd4d26662c0cf0b3786f02ec66\">Transfer learning.\u003C/li>\u003Cli data-list-item-id=\"eac7c19ece257c3530659dbe8b018ce26\">Unsupervised learning and generative models.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Technologies covered include:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e4d4d52360e225f02e6c674fb25596dc4\">Design and implementation of neural networks.\u003C/li>\u003Cli data-list-item-id=\"e93e1f9fe31726d8e473a6d596294d19d\">GPU-based neural network programming.\u003C/li>\u003Cli data-list-item-id=\"eacd7daca13ad10963cd005c8408b4b95\">Jupyter notebooks.\u003C/li>\u003Cli data-list-item-id=\"e0a74bb505541c8046561acd7ca1fe46a\">Python — NumPy, PyTorch.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Required prior knowledge and skills\u003C/h3>\u003Cp>This course will be very challenging, so learners are expected to possess the needed prior knowledge and skills and/or learn it and necessary technologies on their own time.\u003C/p>\u003Cp>\u003Cstrong>Proficient mathematical skills and theoretical understanding\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ecc6da613cf36391a9344dc48f927d1f1\">Basics of machine learning.\u003C/li>\u003Cli data-list-item-id=\"e105b61cafc6953702817af6592c2366d\">Calculus.\u003C/li>\u003Cli data-list-item-id=\"e3493ad1130a8a3da6439b06407c91a5a\">Linear algebra.\u003C/li>\u003Cli data-list-item-id=\"e478cf3a7829240732e2a5dd68c2eaa42\">Probability and statistics.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Strong application skills\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e5e9bcf60a9bd8bf47395e87b80f31a81\">Python programming - Numpy, basics of PyTorch or other neural network APIs, such as TensorFlow.\u003C/li>\u003Cli data-list-item-id=\"e69b8a32a0c4f501cf0ea293d1c6a7799\">Technical paper reading, such as NeurIPS, AAAI and CVPR papers.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Proficient experience\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ea309c642174c723af843f49467ebeb4d\">Ability to implement machine learning algorithms using Python (NumPy).\u003C/li>\u003Cli data-list-item-id=\"e8b2f4f8cc17288e46a86bc3071a9e046\">Familiarity with Jupyter notebooks.\u003C/li>\u003Cli data-list-item-id=\"e6480fa9691834fd1d3061e17cdefcd27\">Familiarity with using a GPU environment for deep learning (such as Google Colab, Kaggle or your own setup).\u003C/li>\u003Cli data-list-item-id=\"e57c58a550478bf3eb14eb7f20d4c57be\">High-level programming language (Python with NumPy recommended).\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Learning outcomes\u003C/h3>\u003Cul>\u003Cli data-list-item-id=\"e837527453de88dd68e738556830353e5\">Upon completing this course, you’ll be able to:\u003C/li>\u003Cli data-list-item-id=\"e023e9d315db39b0698d91f131093f3db\">Demonstrate an understanding of the mathematics (statistics, probability, calculus, linear algebra and optimization) needed to design neural networks.\u003C/li>\u003Cli data-list-item-id=\"eb0c024f3156c29da8b0e3af466929674\">Design deep neural networks for real-world applications in computer vision.\u003C/li>\u003Cli data-list-item-id=\"e677b3cb463e679899394014731a41ba5\">Execute the transfer of knowledge across neural networks to develop models for applications with limited data.\u003C/li>\u003Cli data-list-item-id=\"ed5c030f5d59d681ff40e9a31d724a3ce\">Explain deep learning approaches for unsupervised learning, including variational autoencoders (VAE) and generative adversarial networks (GAN).\u003C/li>\u003Cli data-list-item-id=\"e95a4d9c1583222f036e69d167da794cd\">Provide examples of hands-on experience building and training neural networks from scratch.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Estimated workload/time commitment per week\u003C/h3>\u003Cp>You should expect to spend an average of 18 – 20 hours per week on this course.\u003C/p>\u003Ch3 class=\"h3-large\">Technology requirements\u003C/h3>\u003Cp>\u003Cstrong>Hardware\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ec38d55899f55f03958beea629e6c58b2\">A personal computer with sufficient memory and processing power, and a major OS.\u003C/li>\u003Cli data-list-item-id=\"e144e2f19d744d7efe096a6a8e3cd5a33\">Reliable, strong internet connection.\u003C/li>\u003Cli data-list-item-id=\"e98781dfc71a0a9e1f0dcb676cd684a70\">Microphone.\u003C/li>\u003Cli data-list-item-id=\"e9329221ae3b026b4395328882fa7b45f\">Webcam.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Software/other\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e28fa066cae25b9e4a953787090533b26\">GPU environment for deep learning, such as Google Colab (free), Kaggle (free) or your own setup.\u003C/li>\u003Cli data-list-item-id=\"e01beda4b9dd4f3fb014f17d325ba8830\">Up-to-date web browser.\u003C/li>\u003C/ul>\u003Ch3 class=\"h3-large\">Creator\u003C/h3>\u003Cp>\u003Cstrong>Hemanth Venkateswara\u003C/strong>\u003C/p>\u003Cp>Hemanth Venkateswara is an assistant research professor at the School of Computing and Augmented Intelligence (SCAI) at Arizona State University. He completed his PhD in machine learning and computer vision in 2017 from ASU. Hemanth’s research interests include transfer learning, incremental learning and generative models using deep learning. His research also explores knowledge transfer paradigms for deep neural networks that are challenging to train due to the paucity of annotated data. Hemanth holds a Bachelor’s degree in physics and master’s degrees in physics and computer science. Prior to earning his PhD, Hemanth worked as a senior software engineer at Alcatel-Lucent Technologies, India. Hemanth is also a member of the IEEE and the ACM.\u003C/p>","The jobs you could get with a master’s in cybersecurity","\u003Cp>This program’s unique coursework provides you with a competitive advantage in the workforce. Upon graduation, you’ll have the knowledge and skills needed to excel in a wide variety of careers including, but not limited to:\u003C/p>","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/2020-02/20181006Hacks_16.JPG","Computer science graduate participating in hack day.",[87,89,91,93,95],{"title":88,"description":32},"Cybersecurity engineer",{"title":90,"description":32},"Cybersecurity risk analyst",{"title":92,"description":32},"Security analyst",{"title":94,"description":32},"Security engineer",{"title":96,"description":32},"Software development engineer","How to apply","\u003Cp>This program has a highly competitive admission process.\u003C/p>",[100,103,106,109],{"title":101,"description":102},"Application deadlines","\u003Cp>Applicants with international credentials have an application deadline of six weeks prior to the selected session start date. All other applicants have an application deadline of four weeks prior to the selected session start date.\u003C/p>\u003Cp>This degree is offered in A and B sessions for fall and spring, and in C session for summer. You can view upcoming start dates in the \u003Ca class=\"non-rds-link\" href=\"https://students.asu.edu/academic-calendar\" target=\"_blank\" rel=\"noopener\">academic calendar\u003C/a>.\u003C/p>\u003Cp>\u003Cstrong>How to complete an application\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e6b498a81ce71ea882c7ac2a40f0e89cb\">Submit online application and pay application fee.\u003C/li>\u003Cli data-list-item-id=\"e79f5fa35fb816255083b13e024386e79\">Send official transcripts to ASU’s graduate admission services.\u003C/li>\u003Cli data-list-item-id=\"ea9c6c539c65a8d5081f831feb0612630\">If needed, send proof of English proficiency.\u003C/li>\u003Cli data-list-item-id=\"e544c8bdf8c14b4be486928d16570c9b1\">If you’re not a U.S. citizen but are living in the U.S., send a copy of your U.S. visa to \u003Ca href=\"mailto:gograd@asu.edu\" target=\"_blank\" rel=\"noopener\">gograd@asu.edu\u003C/a>.\u003C/li>\u003Cli data-list-item-id=\"e0a7d42337d7c9234e36ed6e9ce822cb9\">If you’re based outside of the U.S., select \"JN\" under Visa.\u003C/li>\u003C/ul>\u003Cp>For questions, call 1-844-353-7953 or email \u003Ca class=\"non-rds-link\" href=\"mailto:scai.grad.admission@asu.edu\" target=\"_blank\" rel=\"noopener\">scai.grad.admission@asu.edu\u003C/a>.\u003C/p>",{"title":104,"description":105},"Admission requirements for computer science undergraduate majors","\u003Cp>You must have completed an undergraduate degree in computer science from an accredited university. Please review the necessary CS pre-requisite courses in the section below to confirm eligibility.\u003C/p>\u003Cp>\u003Cstrong>GPA requirements\u003C/strong>\u003C/p>\u003Cp>A minimum cumulative GPA of 3.00 in the last 60 credit hours (last two years) of a four-year undergraduate degree. If your degree was completed in the U.S., it must be from a regionally accredited university.\u003C/p>\u003Cp>Applicants who have previously completed graduate-level coursework must have a minimum post-baccalaureate/graduate GPA of 3.00.\u003C/p>\u003Cp>\u003Cstrong>English proficiency\u003C/strong>\u003C/p>\u003Cp>If all college degrees are from a country outside of the U.S., you may need to demonstrate \u003Ca class=\"non-rds-link\" href=\"https://admission.asu.edu/apply/international/graduate/english-proficiency\" target=\"_blank\">English proficiency\u003C/a>.\u003C/p>\u003Cp>Note that the MCS degree requires higher English proficiency scores.\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e92dc49a1fc75aa7b338df2cca0dcbc13\">TOEFL: 575 paper-based / 90 internet-based.\u003C/li>\u003Cli data-list-item-id=\"e034a3f27017763915599d1cb0d641e9c\">IELTS (academic version): 7.\u003C/li>\u003Cli data-list-item-id=\"e453becc1e3ab2641cbb335c48170f546\">PTE: 65.\u003C/li>\u003Cli data-list-item-id=\"eeb84e0d235927f30cfa7dc1776289ac9\">Duolingo: 115.\u003C/li>\u003C/ul>\u003Cp>Don’t yet meet this requirement? If you complete the pathway with a 3.00 or higher, you’ll have demonstrated English proficiency. Click on the nondegree tab below to learn more.\u003C/p>",{"title":107,"description":108},"Admission requirements for non-computer science undergraduate majors","\u003Cp>\u003Cstrong>GPA requirements\u003C/strong>\u003C/p>\u003Cp>A minimum cumulative GPA of 3.00 in the last 60 credit hours (last two years) of a four-year undergraduate degree. If your degree was completed in the U.S., it must be from a regionally accredited university.\u003C/p>\u003Cp>Applicants who have previously completed graduate-level coursework must have a minimum post-baccalaureate/graduate GPA of 3.00.\u003C/p>\u003Cp>\u003Cstrong>Math course requirements:\u003C/strong>\u003C/p>\u003Cp>You must have two semesters of advanced math in Calculus I and Calculus II, as well as a background course in discrete math.*\u003C/p>\u003Cp>Applicants with an undergraduate degree in a STEM field have likely completed the math requirement.\u003C/p>\u003Cp>*Discrete math is not always a separate class at all universities. Taking a course that covers data structures and algorithms may also fulfill this requirement. If you’ve never done algorithms, you may not have the proper background for the program.\u003C/p>\u003Cp>\u003Cstrong>Pre-requisite course requirements:\u003C/strong>\u003C/p>\u003Cp>To be eligible for this program, you must have programming knowledge in a variety of languages, including C/C++, Java, Python and HTML, as well as courses in calculus and discrete math.\u003C/p>\u003Cp>\u003Cstrong>CS course information:\u003C/strong>\u003C/p>\u003Cp>\u003Cstrong>CSE 230: Computer Organization and Assembly Language Programming\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e7ad24e88a7e3846b6eb07315ec1760d1\">Register-level computer organization, instruction set architecture, assembly language, processor organization and design, memory organization, IO programming and exception/interrupt handling.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>CSE 310: Data Structures and Algorithms\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8eae6768ef9f80f475864565ae573870\">Advanced data structures and algorithms, including stacks, queues, trees (B, B+, AVL), graphs and searching for graphs, hashing and external sorting.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>CSE 330: Operating Systems\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8dc3968a14f143bbfa4b62822292c773\">Operating system structure and services, processor scheduling, concurrent processes, synchronization techniques, memory management, virtual memory, input/output, storage management and file systems.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Principles of Programming Languages OR Introduction to Theoretical Computer Science\u003C/strong>\u003C/p>\u003Cp>\u003Cstrong>CSE 340: Principles of Programming Languages\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e8f4b2fa20f5372b8e8aeccdac855487d\">Formal syntactic and semantic descriptions, compilation and implementation issues, and theoretical foundations for several programming paradigms.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>CSE 355: Introduction to Theoretical Computer Science\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e58b99b3b97a1d196855e3c188a8395e4\">Introduces formal language theory and automata, Turing machines, decidability/undecidability, recursive function theory and complexity theory.\u003C/li>\u003C/ul>\u003Cp>\u003Cstrong>Demonstration of Computer Science Competency\u003C/strong>\u003C/p>\u003Cp>If your bachelor’s degree isn’t in computer science, you may need to demonstrate foundational knowledge in key computer science topics before you’re admitted to the program.\u003C/p>\u003Cp>You can demonstrate this competency through approved coursework or by completing designated computer science competency examinations. These exams are still available as an option, but access is limited to current ASU students.\u003C/p>\u003Cp>If you’re interested in satisfying this requirement through examination, first apply as a \u003Ca class=\"non-rds-link\" href=\"https://webapp4.asu.edu/dgsadmissions/Index.jsp?program=GRND&plan=ESMCSNONDG&subplan=&campus=ONLNE&osm=true\" target=\"_blank\">nondegree student\u003C/a>. For step-by-step instructions on how to apply as a nondegree student to access these exams, see \u003Ca class=\"non-rds-link\" href=\"https://docs.google.com/document/d/1eI1sgfczBAgJQO3d3FWhUtnWiUYjRPLiC3-i3NIdoLo/edit?usp=sharing\" target=\"_blank\">this guide\u003C/a>.\u003C/p>\u003Cp>For more information about competency requirements and next steps, contact the program advising team at \u003Ca class=\"non-rds-link\" href=\"mailto:mcsonline@asu.edu\" target=\"_blank\">mcsonline@asu.edu\u003C/a>.\u003C/p>\u003Cp>\u003Cstrong>English proficiency\u003C/strong>\u003C/p>\u003Cp>If all college degrees are from a country outside of the U.S., you may need to demonstrate \u003Ca class=\"non-rds-link\" href=\"https://admission.asu.edu/apply/international/graduate/english-proficiency\" target=\"_blank\">English proficiency\u003C/a>.\u003C/p>\u003Cp>Note that the MCS degree requires higher English proficiency scores.\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e19e8104c519a6e30b265bb47972903bb\">TOEFL: 575 paper-based / 90 internet-based.\u003C/li>\u003Cli data-list-item-id=\"eed999ce20a06b84b0c9998872cec7411\">IELTS (academic version): 7.\u003C/li>\u003Cli data-list-item-id=\"e366c08acffbd8857afd722dd8cfd25bb\">PTE: 65.\u003C/li>\u003Cli data-list-item-id=\"ed68a80510c17fd85c5f2af025ff8d3fe\">Duolingo: 115.\u003C/li>\u003C/ul>\u003Cp>Don’t yet meet this requirement? If you complete the pathway with a 3.00 or higher, you’ll have demonstrated English proficiency. Click on the nondegree tab below to learn more.\u003C/p>",{"title":110,"description":111},"Professional certification and nondegree enrollment opportunities","\u003Cp>As a nondegree graduate student, you can begin taking graduate-level computer science courses without being admitted to the master’s program.*\u003C/p>\u003Cp>\u003Cstrong>Professional certifications\u003C/strong>\u003C/p>\u003Cp>If you’re not sure you want to pursue the full degree or only want to focus on a specific topic, you can earn a professional certification for ASU credit.\u003C/p>\u003Cp>To receive a professional certification, you must successfully complete at least three courses from that topic area with a grade of “B” or better. Available certifications include:\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"e56cf6bbbd2f9783900b6c06790050fa9\">AI and machine learning.\u003C/li>\u003Cli data-list-item-id=\"eb2ce9542e422951c60214bfe1f2a0a4a\">Big data.\u003C/li>\u003Cli data-list-item-id=\"e1c88eb573e2e39c8e67e84f85564c6f7\">Cybersecurity.\u003C/li>\u003Cli data-list-item-id=\"e8d7dae82a86932e84981976297085e16\">Software engineering.\u003C/li>\u003C/ul>\u003Cp>\u003Ca class=\"non-rds-link\" href=\"https://goto.asuonline.asu.edu/mcs-professional-certifications/\" target=\"_blank\" rel=\"noopener\">Learn more\u003C/a> about professional certifications.\u003C/p>\u003Cp>\u003Cstrong>General pathway\u003C/strong>\u003C/p>\u003Cp>This pathway provides an opportunity to take a variety of courses as a nondegree student to explore topics or meet this program’s GPA and English proficiency admission requirements. Completing nine graduate credits with a “B” or better (3.00 GPA) will demonstrate English proficiency.\u003C/p>\u003Cp>Through this pathway, you choose and complete any three 500-level courses that are part of the Master of Computer Science program. The courses you complete in the pathway with a “B” or higher will apply toward the master’s degree in computer science, if admitted within three years of completing courses as a nondegree student. Courses completed as a nondegree student up to 12 credits with a \"B\" or better will apply toward your MCS degree.\u003C/p>\u003Cp>\u003Ca href=\"https://webapp4.asu.edu/dgsadmissions/Index.jsp?program=GRND&plan=ESMCSNONDG&subplan=&campus=ONLNE&osm=true\" target=\"_blank\" rel=\"noopener\">Begin your application\u003C/a> for one of the four professional certifications or the general pathway.\u003C/p>\u003Cp>The ASU graduate admission office will review the application and notify you by email if any additional steps are required upon admission. All official emails will go to your ASU student Gmail account.\u003C/p>\u003Cp>\u003Cstrong>Please note:\u003C/strong>\u003C/p>\u003Cul>\u003Cli data-list-item-id=\"ef7e49600bf48840a2c110608b4dec14c\">If you don’t enroll in the semester in which you are admitted, you must submit a new application and pay the application fee.\u003C/li>\u003Cli data-list-item-id=\"e7df614e9cfaf5ec2741f51f962b422de\">Completing the pathway or professional certification does not waive the requirement for all applicants to the Master of Computer Science program to demonstrate mathematics and computer science topic prerequisite knowledge. See the admission requirements sections above for more information.\u003C/li>\u003C/ul>\u003Cp>*If you don't have the prerequisite knowledge outlined in the master’s admission criteria for mathematics and computer science, you should be aware that you may not have the necessary background for graduate CSE courses and are enrolling at your own risk. You are responsible for acquiring that knowledge before and during the course.\u003C/p>","Top 25% of all accredited engineering programs in the nation","\u003Cp>The Ira A. Fulton Schools of Engineering is dedicated to providing a dynamic learning environment and supporting all students on the paths to their degrees. We’ve received numerous peer-reviewed programmatic honors from U.S. News & World Report.\u003C/p>",[115,118,120,123,126,128,131,133],{"title":116,"description":117},"#2","\u003Cp>best online master’s in electrical engineering programs.\u003C/p>",{"title":116,"description":119},"\u003Cp>best online master’s in engineering management programs.\u003C/p>",{"title":121,"description":122},"Top 5","\u003Cp>for bachelor’s degrees awarded to underrepresented minorities (American Society for Engineering Education 2022).\u003C/p>",{"title":124,"description":125},"Top 10","\u003Cp>best online master’s in engineering programs for veterans.\u003C/p>",{"title":124,"description":127},"\u003Cp>best online master's in industrial engineering programs.\u003C/p>",{"title":129,"description":130},"Top 20","\u003Cp>best online master’s in engineering programs.\u003C/p>",{"title":129,"description":132},"\u003Cp>best undergraduate electrical, electronic and communications engineering programs.\u003C/p>",{"title":134,"description":135},"Top 35","\u003Cp>best undergraduate engineering programs.\u003C/p>","View the most commonly asked questions",[138,141,144,147,150,153,156,159,162,165,168,171,174,177],{"title":139,"description":140},"Do prerequisites need to be taken from ASU?","\u003Cp>We understand that many applicants have IT experience. ASU offers computer science competency exams for applicants who want to demonstrate foundational knowledge instead of completing coursework. Access to these exams is limited to current ASU students. If you’re interested in this option, apply as a nondegree student — see \u003Ca class=\"non-rds-link\" href=\"https://docs.google.com/document/d/1eI1sgfczBAgJQO3d3FWhUtnWiUYjRPLiC3-i3NIdoLo/edit?usp=sharing\" target=\"_blank\">this guide\u003C/a> for step-by-step instructions on applying and accessing the exams, or contact the program advising team.\u003C/p>",{"title":142,"description":143},"Is there any credit offered for work experience in IT?","\u003Cp>No. Academic credit cannot be given for work experience in the field.\u003C/p>",{"title":145,"description":146},"Can completing professional certification coursework satisfy prerequisites?","\u003Cp>Completing professional certification courses alone does not waive a prerequisite, but working through the coursework can be excellent preparation for the competency exams. See \u003Ca class=\"non-rds-link\" href=\"https://docs.google.com/document/d/1eI1sgfczBAgJQO3d3FWhUtnWiUYjRPLiC3-i3NIdoLo/edit?usp=sharing\" target=\"_blank\">this guide\u003C/a> for instructions on applying as a non-degree student to access the exams, or contact the program advising team.\u003C/p>",{"title":148,"description":149},"Are letters of recommendation required for admission?","\u003Cp>No, letters of recommendation are not required.\u003C/p>",{"title":151,"description":152},"What is a statement of purpose?","\u003Cp>A statement of purpose should include your reasons for undertaking the graduate work and an explanation of your academic interests, including their relation to your professional goals. There is no specific length for a statement of purpose. A resume will be accepted as a personal statement. Students are welcome to submit a resume or a statement of purpose or both.\u003C/p>",{"title":154,"description":155},"How can I check the status of my application?","\u003Cp>You'll receive email updates on the status of your application, so be sure to check your ASU email regularly for these notifications. You can also see your status by logging into my.asu.edu with your ASURITE ID and reviewing the section called ‘My Application Status.’ This section will display pending checklist items or an application decision. If you have questions on pending items, please contact us toll-free at 1-844-353-7953 or \u003Ca href=\"mailto:mcsenrollment@asu.edu\" target=\"_blank\" rel=\"noopener\">mcsenrollment@asu.edu\u003C/a>.\u003C/p>",{"title":157,"description":158},"Do the MCS courses need to be taken in a specific order?","\u003Cp>No. You can take courses in any order. However, you must take one applications course, one systems course and one foundations course as part of the graduation requirements. You must also complete seven CSE elective courses.\u003C/p>",{"title":160,"description":161},"It says the program takes 18–36 months to complete. Can it be completed earlier than that?","\u003Cp>The completion time is based on the number of courses you enroll per semester. You must complete 30 credit hours to get your master's degree.\u003C/p>",{"title":163,"description":164},"How much time can I expect to spend per week on class projects and assignments?","\u003Cp>These courses follow an accelerated format. As such, you should expect to spend 15–20 hours per week on each course.\u003C/p>",{"title":166,"description":167},"Can I transfer from online to on-campus while pursuing my Master of Computer Science degree?","\u003Cp>You’ll need to submit a new application to transfer your credits. If accepted, you can transfer up to 12 credits from an online program to the same on-campus program, as long as your grades are “B” or better.\u003C/p>",{"title":169,"description":170},"What is the total cost of the degree program?","\u003Cp>The total cost of the degree program is $15,000, or $1,500 per 3-credit course. There are no textbooks required for the courses and no additional fees. You'll only be billed for the courses you are enrolled in.\u003C/p>",{"title":172,"description":173},"Do I have to pay for the entire degree upfront? ","\u003Cp>You only have to pay for the courses you are enrolled in. ASU also offers a payment plan, which provides you with the option to budget the payment of certain eligible university charges over several months within the semester.\u003C/p>",{"title":175,"description":176},"How do I get billed and when do I pay?","\u003Cp>Your bill will display in the my.asu.edu portal. The payment deadlines for each session are also on the \u003Ca href=\"https://registrar.asu.edu/academic-calendar\" target=\"_blank\" rel=\"noopener\">academic calendar\u003C/a>.\u003C/p>",{"title":178,"description":179},"How do ASU students access the alumni network?","\u003Cp>More information on the ASU alumni network is available at \u003Ca class=\"non-rds-link\" href=\"https://alumni.asu.edu/\" target=\"_blank\" rel=\"noopener\">https://alumni.asu.edu/\u003C/a>.\u003C/p>","\u003Cp>As a nondegree-seeking graduate student, you can take master’s-level computer science courses without being admitted to the program. This provides you the opportunity to establish a high graduate GPA, try out courses, meet the English proficiency requirement or earn a professional certification credential.\u003C/p>\u003Cp>Those who complete three courses with a cumulative GPA of at least 3.00 after the first attempt can use this GPA and the credit hours earned when applying for the Master of Computer Science program. You must also meet all prerequisite requirements to be admitted.  \u003C/p>\u003Cp>\u003Ca class=\"non-rds-link\" href=\"https://goto.asuonline.asu.edu/mcs-professional-certifications/\" target=\"_blank\" rel=\"noopener\">Learn more\u003C/a>\u003C/p>","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/2021-06/200203-FY20-ASU-Q1-Alumni-at-Industrious-Tempe---CLP---Andrew-Wilcox-and-Tim-Chow-of-Wilco-Productions--1350_1.jpg","A man sits at a desk on his computer.",false,"2027","GS0001",{"amount":32,"summary":32},[188,194,200,207],{"id":189,"title":190,"nextStartDate":18,"category":191,"detailPage":192,"degreeImage":193},"ae6b1797-8b6e-42d7-8ec7-d9ee87320820","Graphic Information Technology (MS)",{"title":30},"/online-degree-programs/graduate/graphic-information-technology-ms/","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/program-images/07_POLY-GIT-Creative-Jam-JH_4046_a.jpg",{"id":195,"title":196,"nextStartDate":18,"category":197,"detailPage":198,"degreeImage":199},"4bf82365-b938-49b4-8c7e-65bb5152f6bd","Computer Science – Big Data Systems (MCS)",{"title":30},"/online-degree-programs/graduate/master-big-data/","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/program-images/FURI-CIDSE-Perry-Wang-MAC0002a.jpg",{"id":201,"title":202,"nextStartDate":18,"category":203,"detailPage":205,"degreeImage":206},"10c4e474-9471-4ca2-9ea2-b10b3c4e340a","Computer Science (BS)",{"title":204},"Undergraduate","/online-degree-programs/undergraduate/bachelor-science-computer-science/","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/program-images/BS-CS-final.jpg",{"id":208,"title":209,"nextStartDate":18,"category":210,"detailPage":211,"degreeImage":212},"2ec21116-c5f2-4233-90ba-848164bcabee","Information Technology (BS)",{"title":204},"/online-degree-programs/undergraduate/bachelor-science-information-technology/","https://cms.asuonline.asu.edu/sites/g/files/litvpz1971/files/program-images/POLY-FURI-Cecilia-Laplace2452a.jpg","Will my diploma say ‘online’?","\u003Cp>No, Arizona State University’s diplomas don’t specify whether you earn your degree online or in person. 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