CRT3011-Introduction to Business Analytics
Name Trimester Programme Batch Email
Dr. Sinimole K R Third PGDM A sinimole@rajagiri.edu
Dr. Angela Susan Mathew Third PGDM B angela@rajagiri.edu
Dr. Angela Susan Mathew Third PGDM C angela@rajagiri.edu
Dr. Minnu F Pynadath Third PGDM D minnu@rajagiri.edu
About the Course
The course intends to provide students with a comprehensive understanding of leveraging analytics in managerial decision-
making. Covering diverse topics such as data mining, predictive analytics, and prescriptive analytics, students learn to harness
tools like MS Excel for data interpretation and explore various models to predict business outcomes. The curriculum spans from
foundational analytics insights to ethical considerations, highlighting the significance of precise data, models, and ethical
approaches. Ultimately, it empowers students to recognize business issues and appreciate how analytics can enhance
organizational decision-making processes.
Course Outcomes mapped to Programme Learning Objectives (PLOs) PLO
To understand how managers use business analytics to formulate and solve business
CO1 PLO 1 b
problems
CO2 To apply MS Excel to explore and interpret business data PLO 1 a
To apply the concepts and methods of Predictive and Prescriptive Analytics to attain the
CO3 PLO 1 b
best possible outcomes
Mapped PLO
PLO 1a : Our graduates will be able to identify the business problem in a given situation.
PLO 1b : Our graduates will be able to generate alternatives for effective problem solving.
Session Plan
Session Topic/Assignment Reading Methodology
Module I – Introduction to Business
Analytics
1-2 Value of Business Analytics, Book 1 Lecture
Producing insights from information
through analytics
Categorization of analytical methods
and models – Descriptive, Predictive,
3-4 Book 1 Lecture
Prescriptive. Big Data analytics, Web
and Social media analytics
Module II – Data Mining
Business Data Overview -Sources
and Uses of Business Data,
5-6 Importance of Data Quality - Dealing Book 1 Lecture + Lab
with missing or incomplete data, Data
preprocessing – cleaning,
transformation, reduction
Introduction to Data
Mining - Data Mining Process, Data
7-8 Book 3 Lecture + Lab
Mining Algorithms – Association,
Classification, Prediction, Clustering
Module III – Predictive Analytics:
Classification Techniques
Classification Techniques -
9-10 Introduction to Classification Book 2 Lecture + Lab
Techniques, Theory of Tree-based
Classification Techniques: Decision
Tree and Random Forest.
Regression Techniques - Introduction
11-12 to Linear and Non-Linear Regression Book 2 Lecture + Lab
Methods
13-14 Simple and Multilinear Regression Book 2 Lecture + Lab
15-16 Excel implementation for MLR Book 2 Lecture + Lab
Excel implementation for Logistic
17-18 Book 2 Lecture + Lab
regression
Module IV – Prescriptive Analytics
Introduction to Prescriptive
19-20 Book3 Lecture + Lab
Analytics, Linear Programming,
Solving LPP
Linear Programming (LP) model
21-22 building, LP Problem (LPP) – Book3 Lecture + Lab
terminologies and assumptions
23-24 Sensitivity Analysis in LPP Book3 Lecture + Lab
25-26 Range of Optimality, Shadow Price Book3 Lecture + Lab
27-28 Dual Linear Programming Book 3 Lecture + Lab
Module V - Ethics of Data and
Analytics Value-laden biases in data
29-30 analytics, Ethical Theory and Data Book4 Presentations
analytics, Privacy, data and shared
responsibility
References/Books
1. Essentials of Business Analytics (1st Ed.) by Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams ISBN: 978-1-285-
18727-3
2. Fundamentals of Predictive Analytics with JMP By Ron Klimberg and B. D. McCullough ISBN: 978-1-61290-425-2. Publisher:
SAS Institute.
3. Discovering Knowledge in Data: An Introduction to Data Mining, Daniel T. Larose & Chantal D. Larose, Wiley, Second
Edition. 4.Business Analytics: An Introduction. Edited by Jay Liebowitz. CRC Press
Grading Structure
Sl. No. Evaluation tool Marks CO Assessed Tool for Measurement
1 Mid Trimester 20
2 End Trimester 30
3 Attendance 5
4 Individual Assignment 15 CO 2 Rubrics
5 Viva Voce 15 CO 1 Marks
6 Group Presentation 15 CO 3 Marks
Learning & Teaching Activities -
Learning & Teaching Activitiy Session Course Outcomes
Individual Assignment Session 10 CO2
Viva Voce Session 10-20 CO1
Group Presentation Session 20 CO3
Grading or Evaluation tools other than Examinations
Individual assignment 1 (15 marks)
The Individual Assignment will be a hands-on task to be completed by each student and submitted on Moodle.
The assignment will be based on the data and questions that will be provided to you.
Individual assignment 2 (15 marks)
The individual assignment is based on a dataset a hands-on task to be completed by each student and submitted on Moodle.
Viva Voce (15 marks) - Vice voce will be conducted based on different modules, multiple-choice questions
Course policies
Assignment Schedule
Date /
Sl.No. Session Assignment/Presentation Due Date/ Session
1 Session 10 Individual Assignment 1 15
2 Session 20 Individual Assignment 2 25
Course Requirement
Mode of End Term Examination is Lab based Yes
This is a non-technical program, no coding background is required. Preliminary understanding of MS Excel is appreciated
