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SZABIST Karachi Campus Course Syllabus Course Name: Course Code: Credit Hours: Course Prerequisites: Quarter/Year: Instructor: Contact No: Consultation Hours: E-mail: Introduction to Data Mining 3 hours of class per week Statistical Modeling (MBI) Bilal Farooq Use Email Please Email [email protected] 1. Course Description: This course will provide comprehensive introduction to the data mining process; build conceptual and practical foundations of key data mining techniques such as association rule mining, logistic regression and clustering. Emphasis will be placed on the design and implementation of various statistical techniques to perform effective analysis on the large datasets. The students will get hands on experience through the implementation of various statistical techniques via several assignments and projects. 2. Course Objectives: The objective is to learn basic to advance data mining techniques using a statistical tool R. Focus will be on learning various statistical methods to effectively analyze and predict. 3. Learning Outcomes: Become expert data miners. 4. Textbook: Applied Data Mining (Statistical Methods for Business & Industry) by Paolo Giudici. (Wiley Student Edition is available locally). 5. Reference Book(s): Predictive data mining: a practical guide By Sholom M. Weiss, Nitin Indurkhya 6. Teaching and Learning Methodology: I will teach using primarily power point presentation and white board. From time to time I will also conduct labs to demonstrate data mining techniques using R. 7. Materials and Supplies: Scientific calculators and R is required. Course Name here Page 1 of 4 SZABIST Karachi Campus 8. Grading Policy/Student Assessment: Assessment Instruments* Percentage** Quizzes Midterm Exam Project Final Exam 10 % 30 % 20 % 40 % 9. Attendance Policy: Prompt arrival and regular attendance are extremely important. Refer to student handbook for policies on late entry, maximum absences allowed, leave application etc. 10. Expected Class Conduct: I would expect my students to avoid eating in class, no usage of mobile phones and late arrivals are unacceptable. I also expect my students to follow strict timelines for submission of assignments and quizzes etc. 11. Weekly Course Outline: Session Session Topic 1 Introduction to Data Mining Process 2 Introduction to Data Mining Process 3 Overview of the Data Mining Process 4 Overview of the Data Mining Process 5 6 7 Data Exploration and Dimension Reduction Assessments* Percentage** Instructor Evaluation Data Exploration and Dimension Reduction Evaluating Classification and Predictive Performance 8 Mid Term 9 Multi Linear Regression 10 Multi Linear Regression 11 Logistic Regression Course Name here Mid-Term Page 2 of 4 30% SZABIST Karachi Campus 12 Logistic Regression 13 Survival Analysis 14 Survival Analysis 15 Cluster Analysis 16 Final Exam Final Exam 12. Students with Physical or Educational Challenges: Students with educational and/or physical challenges are entitled to extra attention and time from the instructor. Therefore students are advised to notify the course instructor at the beginning of the course. Special arrangement may also be made on prior request based on specific challenges. 13. Academic Integrity This course seeks to empower students for independent learning, resourcefulness, clear thinking, and perception. All submitted work and activities should be genuine reflections of individual achievement from which the student should derive personal satisfaction and a sense of accomplishment. Plagiarism and cheating subvert these goals and will be treated according to the policy stated in the Student Handbook. The instructor reserves the right to utilize electronic means to help prevent plagiarism. 14. Instructor’s Course Portfolio: The Instructor’s Course Portfolio (ICP) folder must be carried and maintained weekly (it must include all work pertaining each respective week) by the course instructor over the period of the semester. This folder must be submitted to the faculty coordinator at the end of the course and should include the following items: a. Course Syllabus b. Presentation Slides c. Handouts d. Lecture Notes e. Reading Material/assignments f. Homework g. Projects h. Exams i. Quizzes j. Final Exam k. Model answers for all assessments l. Any other material pertaining to the course should also be included into the portfolio folder. 15. Comments and/or Suggestions: Course Name here Page 3 of 4 40% SZABIST Karachi Campus Students and Instructors may contact the Institutional Research Department if there is a need to make suggestions or comments that can help further improve the course. A link is also provided on your ZABDESK account for frequent and trouble-free feedback. The Institutional Research Department would like to hear your feedback about the following: Students Course Content/ thoroughness Lecture Delivery/Supplementary Material Facilities/Labs/Software/Hardware Support Course alignment with learning outcomes Any other comments/feedback Course Name here Instructors Availability of teaching material Facilities/Internet/Administrative Support Labs: Software/Hardware/Technical support Availability and quality of Teaching Instruments Any other comments/feedback Page 4 of 4