Data Mining

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Data Mining

  1. 1. KENNESAW STATE UNIVERSITY UNDERGRADUATE PROPOSAL New Course (NOT General Education) I. Proposed Information Course Prefix and Number: _____________IS4540_______ Course Title: ___________Data Mining___________________________ Credit Hours (format should be # - # - #): ____3-0-3_____ Prerequisites: ______________IS3580: Business Intelligence and Data Warehouse_______________ (Prerequisites are courses or requirements that non-negotiable and must be successfully completed by any student before enrolling in the course or program under consideration. Corequisites are courses that can be taken before or in the same semester as the course under consideration. Courses at the upper-division level will require lower-division competencies or prerequisites.) Course Description for the Catalog: Data mining aims at finding useful regularities and patterns in large data sets. In this course students will learn how this interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. This course covers major data mining techniques including association mining, classification, clustering, trend analysis, prediction, fraud detection, and their applications in e-commerce, CRM, health care and others. Tools like SQL Server 2005 business intelligence toolkit will be used to create analytical applications. II. Justification for Course A. Explain assessment findings which led to course development. Data Mining is one of the key members of the Business Intelligence product family. As organizations amass vast amount of data, as part of their day-to-day operation, fining useful patterns in these large data sets have become critical for competitiveness. For example, the large bar code reader data collected at a supermarket can reveal previously unknown, yet useful information about the shopping behavior of customers. Nowadays, Data Mining techniques are intensively used by organizations to increase their competitiveness, finding new opportunities, avoiding fraud, enhance security, just listing a few. The job market demand for data mining is very high. Therefore, it is very beneficial for IS majors to study data mining. B. Explain for Prerequisites: 1. What is the substance of content in each prerequisite that commands its inclusion as a prerequisite to the proposed course? Data mining techniques are typically applied on data from a data warehouse. Data warehouse stores prepossessed data in a multi-dimensional way. Therefore, making the Business Intelligence and Data Warehouse course a pre-requisite helps students build on their knowledge.
  2. 2. 2. What is the desired sequence of prerequisites? IS 2080: Data Management à IS 3580: Business Intelligence and Data Warehouse à IS 4540: Data Mining 3. What is the rationale for requiring the above sequence of prerequisites? Database course lays the foundations for Structured Query Language, Entity Relation model and other basic concepts on databases. Then Business Intelligence and Data Warehouse course discuss a special type of databases which adopt dimensional model to re-organize accumulated data in order to support business decision making. The Data Mining course further covers the technology of discovering patterns and knowledge from data stored in data warehouse. 4. How often are the required prerequisites offered? The pre-requisites will be offered twice a year. C. Give any other justification for the course. III. Additional Information A. Where does this course fit sequentially and philosophically within the program of study. It fits in the upper major elective courses. B. What efforts have been made to ensure that this course does not duplicate the content of other college courses with similar titles, purposes, or content? Search in the KSU catalog was done. No duplication was found. C. Where will the course be located in the program (elective, required in Area F, required or elective for the major)? Indicate and justify its placement in the curriculum. Major Elective. D. How often will this course be offered? This course will be offered twice a year. E. All sections of the course will be taught with the understanding that the following apply: 1. Purpose of the Course The purpose of this course is to introduce the-state-of-the-art data mining techniques to the IS majors. 2. Objectives of the Course o Have a clear understanding of the significant roles that data mining plays to improve business competitiveness. o Have a clear understanding of the purposes, techniques, processes, and results of different data mining techniques.
  3. 3. o Be familiar with using SQL Server 2005 to conduct different data mining tasks. 3. Course Content The following content will be covered by this course: 1. Introduction to Data Mining 2. OLE DB for Data Mining 3. Using SQL Server for Data Mining 4. Naïve Bayes for Classification 5. Decision Tree for Classification 6. Time Series Analysis 7. Clustering Analysis 8. Sequence Clustering Analysis 9. Association Mining 10. Data Mining with SQL Server Integration Service 11. SQL Server Data Mining Architecture 12. Implementing a Web-Cross Selling Application F. What instructional methodologies will be incorporated into the course to stimulate group process, writing skills, multiculturalism, and educational outcomes? Group project will be used to stimulate group process. Students are required to prepare presentation slides and make presentations on the course project. Complete project report is required to promote students’ writing skills. G. Outline the plan for continuous course assessment. What are the department, school, college, or professional standards which will be used for the assessment? How will it be determined that the course is current, meeting the educational needs of students and responsive to educational standards? How often will the course assessment be done by the department? Student evaluation at end of semester, regular evaluation by program curriculum committee, and annual Assurance of Learning evaluation. H. Enclose a course syllabus (optional format described at the end of this document) IV. Resources and Funding Required A. What resources will be redirected to accommodate this course? N/A B. Explain what items will cause additional cost to the department/school/college Personnel Computer Technology Library resources Equipment Space
  4. 4. IS 4540: Data Mining Instructor: Solomon Negash Office Number: CL 3013 Office Hours: TBD Phone: 770-420-4312 Email snegash@kennesaw.edu Course Description: Data mining aims at finding useful regularities and patterns in large data sets. In this course students will learn how this interdisciplinary field brings together techniques from databases, statistics, machine learning, and information retrieval. This course covers major data mining techniques including association mining, classification, clustering, trend analysis, prediction, fraud detection, and their applications in e-commerce, CRM, health care and others. Tools like SQL Server 2005 business intelligence toolkit will be used to create analytical applications. Prerequisites: IS3580: Business Intelligence and Data Warehouse, must acquire a grade of “C” or higher Textbooks: Zhaohui Tang and Jamie MacLennan, Data Mining with SQL Server 2005, Wiley Publishing, 2005, ISBN: 0-471-46261-6. Learning Objectives: o Have a clear understanding of the significant roles that data mining plays to improve business competitiveness. o Have a clear understanding of the purposes, techniques, processes, and results of different data mining techniques. o Be familiar with using SQL Server 2005 to conduct different data mining tasks. Learning Outcomes: As a result of completing this course, students will be able to: o Comprehend the roles that data mining plays in Business Intelligence. o Manipulate different data mining techniques. o Use SQL Server to conduct different data mining tasks. o Analyze business data o Select and apply proper data mining algorithms to build analytical applications
  5. 5. o Evaluate and explain the results of different data mining algorithms Course Project Students are required to work on a group course project that conducts different data mining tasks on a real business data by using SQL Server. Assignments Assignments will be questions related to each major topic covered in this course. Assessment and Grade Evaluation: Attendance and Quizzes 10% A 90% - 100% Assignments 30% B 80% - 89% Midterm Test 30% C 70% - 79% Final Project Presentation 30% D 60% - 69% Course Schedule (Tentative, subject to change): Date Topic Slides Introduction to Data Mining OLE DB for Data Mining Using SQL Server Data Mining Naïve Bayes for Classification Decision Tree for Classification Time Series Analysis Clustering Analysis Sequence Clustering Analysis Association Mining Data Mining with SQL Server Integration Service SQL Server Data Mining Architecture Implementing a Web-Cross Selling Application 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
  6. 6. 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 class assignments and lab exercises; however, the provisions of the STUDENT CONDUCT REGULATIONS, II. Academic Honesty, KSC Undergraduate Catalog will be strictly enforced in this class. Frequently students will be provided with “take-home” exams or exercises. It is the student’s responsibility to ensure they fully understand to what extent they may collaborate or discuss content with other students. No exam work may be performed with the assistance of others or outside material unless specifically instructed as permissible. If an exam or assignment is designated “no outside assistance” this includes, but is not limited to, peers, books, publications, the Internet and the WWW. If a student is instructed to provide citations for sources, proper use of citation support is expected. Additional information can be found at the American Psychology Association (APA) website: http://www.apa.org/journals/webref.html
  7. 7. Acknowledgment and Acceptance of Academic Integrity Statement: In any academic community, certain standards and ethical behavior are required to ensure the unhindered pursuit of knowledge and the free exchange of ideas. Academic honesty means that you respect the right of other individuals to express their views and opinions, and that you, as a student, not engage in plagiarism, cheating, illegal access, misuse or destruction of college property, or falsification of college records or academic work. As a member of the Kennesaw State University academic community you are expected to adhere to these ethical standards. You are expected to read, understand and follow the code of conduct as outlined in the KSU graduate and undergraduate catalogs. You need to be aware that if you are found guilty of violating these standards you will be subject to certain penalties as outlined in the college judiciary procedures. These penalties include permanent expulsion from KSU. Read the Academic Integrity Statement and then sign and date in the space below. You are required to abide by these ethical standards while you are a student at KSU. Your signature indicates that you understand the ethical standards expected of you in this academic community, and that you understand the consequences of violating these standards. ________________________________ ________________________________ Course Name Instructor Name Print Name Student ID Number Signature Date ________________________________ email
  8. 8. Attendance Policy Class attendance: Regular attendance is strongly recommended. If an emergency arises, contact professor prior to class time via email or phone. If you are absent for excusable emergency you have to bring proof to avoid academic penalty. For example, paper work indicating doctor’s visit. Make-up work*: Assignments; late assignments accrue 20% penalty for each late day. No late quizzes. No late project presentations. Exams; no make-up exams are given. If an emergency arises, and an absence is excused, then the student may take a cumulative final exam in its place. *Assignments, projects, and quizzes can be submitted anytime before the due date. Therefore, no emergency excuses are accepted. Reference/Bibliography N/A
  9. 9. V. 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: _Information Systems_____________________ COURSE NUMBER: ___IS 4540______________________ COURSE TITLE FOR LABEL: __Data Mining___________________ (Note: Limit 30 spaces) CLASS-LAB-CREDIT HOURS: __3-0-3__________ Approval, Effective Semester: __Summer 2008__________ Grades Allowed (Regular or S/U): __Regular___________ If course used to satisfy CPC, what areas? ___Major Elective_____________________________ Learning Support Programs courses which are required as prerequisites: ______IS 3580: Business Intelligence and Data Warehouse_____ APPROVED: _______________________________________________________________________ Vice President for Academic Affairs or Designee
  10. 10. KENNESAW STATE UNIVERSITY UNDERGRADUATE PROPOSAL New Course (NOT General Education) Course Prefix and Number: __IS 4540___________________________ Responsible Department: ___Computer Science and Information Systems______ Proposed Effective Date: __Summer 2008__________________________ Signature Page Submitted by: ___Solomon Negash___ Date: Name 12/03/2007 ___ Approved ___ Not Approved _____________________________ Department Curriculum Committee, Date ___ Approved ___ Not Approved _____________________________ General Education Council*, Date ___ Approved ___ Not Approved _____________________________ Professional Teacher Education Unit Program Area*, Date ___ Approved ___ Not Approved _____________________________ Department Chair, Date ___ Approved ___ Not Approved _____________________________ College/School Curriculum Committee AND/OR Teacher Education Council*, Date ___ Approved ___ Not Approved _____________________________ College/School Dean, Date ___ Approved ___ Not Approved _____________________________ Undergraduate Policies and Curriculum Committee, Date ___ Approved ___ Not Approved _____________________________ Dean of Undergraduate & University Studies, Date *For curriculum proposals involving General Education courses, there should be collaboration by the Department Curriculum Committee and the General Education Council. For Teacher Preparation proposals, there should be collaboration by the Department Curriculum Committee, the Professional Teacher Education Unit (PTEU) Program Area Committee, the Teacher Education Council, and the College/School Curriculum Committee. Form updated December 2, 2004.

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