Course Description
Learn fundamentals of programming with focus on analytics and AI. Explore modern programming techniques for business problems in analytics, predictive modelling, machine learning, and integrating hands-on Gen AI programming knowledge. Prerequisite: Grade of C or better in ISM 110 or equivalent.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- Demonstrate an understanding of fundamental principles of programming , data structures, and Object-Oriented Programming .
- Implement programming skills for data analytics and visualization using libraries .
- Generate business insights using text analytics and natural language processing techniques.
- Utilize programming skills to generate AI/ Large Language Models to solve business problems.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Individual Assignments | 20% |
| Quiz | 10% |
| Weekly assessment / Case Study | 40% |
| Exam 1 | 15% |
| Exam 2 (cumulative ) | 15% |
Grades are truncated , not rounded . For example, a total score of 899 will earn a B+ grade and not A-.
IMPORTANT: DO NOT procrastinate on the assignments, quizzes, and tests. You have two attempts at weekly exercises and individual assignments. Please complete the first attempt by Wednesday, or sooner in any given week, so you can take advantage of another opportunity to improve your grade . However, you have to submit each assignment by the DUE DATE.
Late/ missed assignments: Late submissions are not accepted . However, in cases of documented emergencies (e.g., medical emergencies or natural disasters—not personal reasons), late work may be considered at the instructor’s discretion and will incur a minimum 30% penalty. Students are responsible for notifying the instructor as soon as possible (preferably in advance) of any circumstances that may affect their ability to meet a deadline .
Grading Scale
| Percentage | Letter Grade |
|---|---|
| 940-1000 points | A |
| 900-939 points | A- |
| 870-899 points | B+ |
| 840-869 points | B |
| 800-839 points | B- |
| 770-799 points | C+ |
| 740-769 points | C |
| 700-739 points | C- |
| 670-699 points | D+ |
| 640-669 points | D |
| 610-639 points | D- |
| 0-609 points | F |
Attendance/Participation:
Attendance / participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
- Al Sweigart, Automate the Boring Stuff with Python, 2015, No Starch Press, ISBN: 9781593279936 Edition: 2ND 19
- Data Mining for Business Analytics: Concepts, Techniques and Applications in Python by Galit Shmueli, Peter C Bruce, Peter Gedec, and Nitin R Patel ISBN: 9781119549864 Edition: 20
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.