Laboratory of Statistical Artificial Intelligence and Machine Learning

Business Analytics DS5610 Spring 2024

Course Objective

Learn the various types of business problems faced by the managers in different functions of the organization.
Identify and understand appropriate analytic tools and techniques to be used for different business problems.
Deploy and implement the various analytics techniques to make better decisions.
Should be able to interpret the results of various analytics techniques and their implications for business.
Learn the utilization of various software tools such as Python, Tableau, MS Excel.

A complete list of topics covered in the course can be found in the course schedule.

Instructors and Coordinator

Local Coordinator - ck

Important Instructions

Students crediting this course are expected to have taken a first course in machine learning.
Note that this course requires students to read the relevan reference material ahead in time and come prepared for the class.

Lecture Timings

Monday 12.00-12.50pm and 2.30-5.00pm (in person lectures) There will be adequate gap between the two afternoon lectures

Friday 2.30-5.00pm - Compensatory classes

Reference Material

Primary Textbook : Business Analytics: The Science of Data-Driven Decision Making, U Dinesh Kumar, 2nd edition, November 2021. ISBN-13 978-9354246197
Other Reference Material: Case Studies from Harvard Business School, IIMs, and other sources

Academic integrity

Students enrolled in this course are expected to exhibit a strong desire to learn, rather than just fulfilling a requirement for their degree. Engaging in discussions that help students better understand concepts or problems is encouraged. However, all submitted work must be original. Plagiarism, including copying from the internet, textbooks, or any other source for which the student does not hold the copyright, as well as sharing code with other students, will not be tolerated and will result in strict disciplinary action, including a failing grade in the course. If you have any questions about this policy, please contact the instructor. All academic integrity violations will be handled in accordance with institute regulations.

Grading Policy

Tests Two tests will be conducted during the semester. Check the course calendar for the quiz dates. Each quiz will account for 15% of your overall grade.

Assignments There will be 5-6 assignments during the semester that will account for 20% of the overall grade.

Project The project will account for 10% of the overall grade.

Exams There will be an end-semester exam that will account for 40% of the overall grade.

Attendance

This course follows the attendance criteria mandated by the institute.

Course Schedule

week 1 (3) - Introduction to Analytics and Case Teaching
week 2 (0) - Introduction to Analytics and Case Teaching (Compensatory class on Feb 2nd, Friday)
week 3 (3) - Descriptive Analytics I
week 4 (3) - Business Operations I, (Compensatory class on Feb 16, Friday)
week 5 (3) - Business Operation II
week 6 (3) - Cluster Analysis
week 7 (3) - Predictive Analytics (Regression and Applications)
week 8 (0) - Study week
week 9 (3) - Business Operations III (Compensatory class on March 22nd, Friday)
week 10 (0) - Study week
week 11 (3) - Predictive Analytics (Classification and Applications)
week 12 (3) - Supply Chain Management and Analytics I
week 13 (3) - MBA and CLV (Compensatory class on April 19, Friday)
week 14 (3) - Project Presentations,

Lecture Material

Students enrolled in the course can access the lecture material from Moodle