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CS7260 Advanced Database Systems College Department Program Course Prefix and Number Course Title Credit Hours Prerequisite(s) Area Course Description Learning Objectives for this course Contribution of the course to the program outcomes Assessment plan and process College of Computing and Software Engineering Computer Science Master Science in Computer Science CS7260 Advanced Database Systems 3 CS5060 or equivalent or Admission to PhD in Analytics and Data Science program __Area F __ Major Required _x_ Major Elective This course will cover advanced concepts and techniques in database systems. Topics include advanced concepts in relational databases, data warehousing and mining, and NoSQL distributed database technology for big data analytics. Upon the completion of this course, students should be able to 1. write advanced SQL queries 2. optimize relational database queries 3. develop data warehousing systems 4. conduct OLAP and data mining 5. explain NoSQL concepts 6. develop distributed systems for big data analytics 7. Research and critique computing literature, and utilize it for proposing solutions 1. Demonstrate an advanced understanding of the capabilities and limits of computation, hardware and software systems, and software development 2. Analyze complex problems in the computing discipline and design solutions that integrate hardware and software, and that are technologically appropriate and theoretically sound Evaluation will be through exams, homework assignments and course projects. Evaluation will consist of: Midterm Exam: 25% Final Exam: 25% Homework assignments: 25% Course project Instruction Delivery Method _x_ Traditional On Campus _x_ Fully Online __ Quality Matters Approved __ Hybrid (describe) Proposal Lead Author Funding Required Lab Fees or special tuition Ying Xie NA NA 25% 100% CS 7260 Advanced Database Systems Syllabus 3 Class Hours, 0 Laboratory Hours, 3 Credit Hours W6:30-9:45 Course Description: This course will cover advanced concepts and techniques in database systems. Topics include advanced concepts in relational databases, data warehousing and mining, and NoSQL distributed database technology for big data analytics. Instructor: Dr. Ying Xie Office: J360 Email: [email protected] Office Hours: MW12:45-2:45 (In Office); Thursday 2-4pm Learning Objectives: Upon the completion of this course, students should be able to write advanced SQL queries optimize relational database queries develop data warehousing systems conduct OLAP and data mining explain NoSQL concepts develop distributed systems for big data analytics Research and critique computing literature, and utilize it for proposing solutions Textbook and Learning Materials: 1. Carlos Coronel, Steven Morris, Database Systems: Design, Implementation, & Management, 2016 2. Abraham Silberschatz, Henry Korth and S. Sudarshan, Database System Concepts McGraw-Hill Science/Engineering/Math; 6/e, ISBN 0073523321, 2010 3. Online Materials Instructional Delivery Methods and Attendance Policy: This course will have in-classroom lectures and also provide both synchronous and asynchronous distance learning options. Course Requirements and Assignments: Students will be expected to attend all classes in classroom or through distance learning delivery, work on homework assignments and course project, and take all exams. Evaluation and Grading: Evaluation will be through exams, homework assignments and course projects. Evaluation will consist of: Midterm Exam: Final Exam: Homework assignments: Course project 25% 25% 25% 25% 100% Academic Honesty Statement: Every KSU student is responsible for upholding the provisions of the Student Code of Conduct, as published in the Undergraduate and Graduate Catalogs. Section II of the Student Code of Conduct addresses the University's policy on academic honesty, including provisions regarding plagiarism and cheating, unauthorized access to University materials, misrepresentation/falsification of University records or academic work, malicious removal, retention, or destruction of library materials, malicious/intentional misuse of computer facilities and/or services, and misuse of student identification cards. Incidents of alleged academic misconduct will be handled through the established procedures of the University Judiciary Program, which includes either an "informal" resolution by a faculty member, resulting in a grade adjustment, or a formal hearing procedure, which may subject a student to the Code of Conduct's minimum one semester suspension requirement. Students are encouraged to study together and to work together on course projects as per the instructor’s specifications; however, the provisions of the STUDENT CONDUCT REGULATIONS, II. Academic Honesty, KSC Undergraduate Catalog will be strictly enforced in this class. Students are required to work INDEPENDANTLY on homework assignments and online exams. Schedule and Topic Coverage: Week 1 2 3 4 5 6 7 8 9 10 Lecture Topic Reference Introduction Query Processing Query Optimization Transaction Management 1 Chapter 12 Chapter 13 Chapter 14 Transaction Management 2 Data Warehousing 1 Data Warehousing 2 Midterm Exam Data Mining 1 Data Mining 2 Chapter 14 Chapter 20 Chapter 20 Exam Chapter 20 Online Material 10 11 12 13 14 Intro to NoSQL Databases MongoDB 1 MongoDB 2 Cassandra 1 Cassandra 2 16 Final Exam Online Material Online Material Online Material Online Material Online Material As per Semester Schedule