Programming for Analytics and AI

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-.

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:

For the face-to-face section/ s of this course, regular attendance and active participation are expected. F2F class sessions of this Programming for Analytics and AI course will include important demonstrations , coding exercises, problem -solving activities, case discussions, and clarification of assignments.

Students are responsible for all materials , announcements , and activities covered during class, whether present or absent. Students may miss up to three classes without penalty. Beginning with the fourth absence, 1% of the final course grade may be deducted for each additional absence. Students who miss more than 30% of scheduled classes may face additional penalties at the instructor’s discretion. Excused absences may be considered with university-approved documentation .

Students who expect to miss more than three class periods of the F2F section to participate in university-sponsored activities should inform the instructor at the beginning of the course. In the case that the faculty member cannot make reasonable accommodations for make-up work, the student may appropriately be advised to drop the course.

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 .

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