The objective of the course is to provide an understanding of basic concepts of Business Analytics like
Descriptive, Predictive and Prescriptive Analytics and an overview of Programming using R.
CO 1 Develop a basic understanding on business analytics and the types of business analytics.
CO 2 Identify and categorize of business analytics, descriptive, predictive, and prescriptive.
CO 3 Understand the various business analytics in practice.
CO 4 Students can prepare tables and execute the results using Ms- excel.
CO 5 Students can have a brief understanding on the various aspects of descriptive statistics.
CO 6 Understand the different measures of central tendency and measure of variability.
CO 7 Understand and explain the nature of predictive analytics.
CO 8 Identify the various forecasting techniques.
CO 9 Understand data mining and its applications and techniques.
CO 10 Understand and explain the regression analysis –linear and multiple.
CO 11 Understand linear optimization.
CO 12 Student will understand how to solve a linear program using open solver.
CO 13 Understand modern approaches to decision-making under uncertainty.
CO 14 Understand the R-Environment.
CO 15 Understand the R-Package.
CO 16 Students will be able to reading and writing data in R.
SCOPE OF THE COURSE:
Business analytics have become vital for the growth and development of the companies of today. Large investments are being made in big data analysis to make better business decisions from past data this past data is being generated by different sources such as business people,marketing,education,engineering etc business analytics plays a very important role by using statistics and tools to decode consumer insights.
UNIT - I: INTRODUCTION TO BUSINESS ANALYTICS:
Definition of Business Analytics, Categories of Business Analytical methods and models, Business Analytics
in practice, Big Data - Overview of using Data, Types of Data.
UNIT - II: DESCRIPTIVE ANALYTICS:
Over view of Description Statistics (Central Tendency, Variability), Data Visualization-Definition,
Visualization Techniques – Tables, Cross Tabulations, charts, Data Dashboards using Ms-Excel or SPSS.
UNIT - III: PREDICTIVE ANALYTICS:
Approaches in Data Mining- Data Exploration & Reduction, Classification, Association, Cause Effect
UNIT - IV: PRESCRIPTIVE ANALYTICS:
Overview of Linear Optimization, Non Linear Programming Integer Optimization, Cutting Plane algorithm and
other methods, Decision Analysis – Risk and uncertainty methods.
UNIT - V: PROGRAMMING USING R.
R Environment, R packages, Reading and Writing data in R, R functions, Control Statements, Frames and
Subsets, Managing and manipulating data in R.
· Lecture & Discussion method
· Problem Solving
· Power Point Presentations
· Case Study Analysis
The students will be asked to prepare a presentation of Business Analytics of any Business of their choice.
GUIDELINES ON CLASS PARTICIPATION:
· Students are requested to be in class before Lecturer comes into the class room
· Late entries (After 5 Minutes) into the class are strictly prohibited.
· There will be 2 internal exams of 15 marks each and one Assignment of 5 marks.
· Internal Exam Pattern: 10 MCQ type questions, 10 Fill in the blanks and 5 short answer type.
- Teacher: Jayasree M