KENNESAW STATE UNIVERSITY GRADUATE COURSE PROPOSAL OR REVISION, Cover Sheet (10/02/2002)

Transcription

1 KENNESAW STATE UNIVERSITY GRADUATE COURSE PROPOSAL OR REVISION, Cover Sheet (10/02/2002) Course Number/Program Name ACS 7420 Algorithm Design for Big Data Department Computer Science Degree Title (if applicable) Ph.D. Proposed Effective Fall 2013 Check one or more of the following and complete the appropriate sections: X New Course Proposal Course Title Change Course Number Change Course Credit Change Course Prerequisite Change Course Description Change Sections to be Completed II, III, IV, V, VII Notes: If proposed changes to an existing course are substantial (credit hours, title, and description), a new course with a new number should be proposed. A new Course Proposal (Sections II, III, IV, V, VII) is required for each new course proposed as part of a new program. Current catalog information (Section I) is required for each existing course incorporated into the program. Minor changes to a course can use the simplified E-Z Course Change Form. Submitted by: Faculty Member Department Curriculum Committee Department Chair School Curriculum Committee School Dean GPCC Chair Dean, Graduate College Vice President for Academic Affairs President

2 KENNESAW STATE UNIVERSITY GRADUATE COURSE/CONCENTRATION/PROGRAM CHANGE I. Current Information (Fill in for changes) Page Number in Current Catalog Course Prefix and Number Course Title Credit Hours Prerequisites Description (or Current Degree Requirements) II. Proposed Information (Fill in for changes and new courses) Course Prefix and Number _ ACS 7420 Course Title Algorithm Design for Big Data Credit Hours 3 Prerequisites ACS 7410 Description (or Proposed Degree Requirements) This course covers advanced algorithms and data structures that are scalable to big data in a distributed computing environment. Topics include MapReduce algorithm design principles, algorithms for processing big text data, algorithms for analyzing big graph, and large-scale machine learning and data mining algorithms. III. Justification This course is part of the core requirements of the new Ph.D. in Analytics and Data Science program. Algorithm design for big data is a core knowledge area for data science.

3 IV. Additional Information (for New Courses only) Instructor: Dr. Ying Xie Text: TBD Prerequisites: ACS 7420 Objectives: Upon the completion of the course, students will be able to explain MapReduce distributed algorithm design principles design scalable algorithms for processing big text data design scalable algorithms for analyzing big graph data design scalable algorithms for large scale data mining implement advanced algorithms Instructional Method Lectures will be given weekly. Homework on algorithm design and implementation will be assigned to students for practicing what is learned in class. Course project will be required for large-scale and creative problem solving. Method of Evaluation -Homework, projects and exams V. Resources and Funding Required (New Courses only) Resource Amount Faculty Other Personnel Equipment Supplies Travel New Books New Journals Other (Specify) TOTAL Funding Required Beyond Normal Departmental Growth

4 VI. COURSE MASTER FORM This form will be completed by the requesting department and will be sent to the Office of the Registrar once the course has been approved by the Office of the President. The form is required for all new courses. DISCIPLINE ACS COURSE NUMBER ACS_7420 COURSE TITLE FOR LABEL Algorithm Design for Big Data (Note: Limit 16 spaces) CLASS-LAB-CREDIT HOURS Approval, Effective Term Spring 2016 Grades Allowed (Regular or S/U) Regular If course used to satisfy CPC, what areas? Learning Support Programs courses which are required as prerequisites APPROVED: Vice President for Academic Affairs or Designee

5 VII MS-CS Course Syllabus Template ACS 7420 Algorithm Design for Big Data 3 Class Hours, 0 Laboratory Hours, 3 Credit Hours Course Description: This course covers advanced algorithms and data structures that are scalable to big data in a distributed computing environment. Topics include MapReduce algorithm design principles, algorithms for processing big text data, algorithms for analyzing big graph, and largescale machine learning and data mining algorithms. Instructor: Dr. Ying Xie CL 3033 [email protected] Learning Objectives: Upon the completion of the course, students will be able to explain MapReduce distributed algorithm design principles design scalable algorithms for processing big text data design scalable algorithms for analyzing big graph data design scalable algorithms for large scale data mining implement advanced algorithms Textbook: TBD Online Content: N/A Instructional Methods and Attendance Policy: Lectures will be given weekly. Homework on algorithm design and implementation will be assigned to students for practicing what is learned in class. Course project will be required for largescale and creative problem solving. Course Requirements and Assignments: Homework, projects will be assigned to the students. There will be midterm and final exams.

6 Evaluation and Grading: Evaluation will be through exams, quizzes, attendance, homework assignments and projects. Evaluation will consist of: Quizzes and attendance: 10% Midterm Exam: 20% Final Exam: 25% Homework: 20% Projects: 25% 100% Grading Scale: 90%+ A B C D < 60 F Academic Honesty: 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 lab assignments as per the instructor s specifications for each assignment; however, the provisions of the STUDENT CONDUCT REGULATIONS, II. Academic Honesty, KSC Undergraduate Catalog will be strictly enforced in this class.