Course

Discipline Specific Electives: Business Analytics

Unit 1

Introduction to Business Analytics

Definition of analytics, Evolution of analytics, Business Analytics versus business analysis, Importance and process of business analytics, Business analytics versus data science, Concept of insights, Data and types, the difference between data, information, and knowledge, various stages of an organization in terms of data maturity, Application of business analytics in business.

Unit 2

Types of Business Analytics

Types – descriptive, predictive, prescriptive, and diagnostic – application in business, Overview of Business analytics applications in – Marketing Analytics, HR Analytics, Supply Chain Analytics, Retail Industry, Sales Analytics, Web & Social Media Analytics, Healthcare Industry, Energy Analytics, Transportation Analytics, Lending Analytics, Sports Analytics. Future of Business Analytics.

Unit 3

Microsoft Excel in Business Analytics

Components- icons-application, Data exploration, Data mining using pivot tables and charts, Use of slicer, VLOOKUP, HLOOKUP, Trend function, Conditional formatting, Icon sets.

Unit 4

Data Visualization

Data Visualization – Meaning, Process, Popular Data Visualization Tools, Data Visualization Designing Strategies and Principles, Dashboard Development, Comparative analysis using visualization.

Unit 5

Introduction to Machine Learning

Machine Learning – History and evolution, Categories – Supervised, Unsupervised and Reinforcement Learning, Application of Machine Learning, Machine learning in real life, future of Machine Learning.

Objective:

This course helps students understand business analytics and application techniques used in business.

Course Outcome

CO1: Understand the concepts of business analytics and its application in business.

CO2: Understand and apply descriptive, predictive, and prescriptive analytics to business problems for input into management decision-making processes.

CO3: Identify the application of business analytics in various functional areas of an organization.

CO4: Illustrate and discuss the fundamental concept of Data Visualization and Reporting.

CO5: Gain an introduction to Machine Learning and its associated terminologies, and understand the types of Machine Learning and its real-world use cases.

References Textbook:

• Business Analytics: Communicating With Numbers– By Sanjiv Jaggia, Kevin Lertwachara, Alison Kelly, Leida Chen
• Scappini, (2016). 80 Fundamental Models for Business Analysts. Createspace Independent Publishing Platform.
• Runkler, A.2013. Data Analytics: models and algorithms for Intelligent Data Analysis.Springer Vieweg.
• Ohri, A, R for Business Analytics.Springer.
• Greasley, (2019). Simulating business processes for descriptive, predictive, and prescriptive analytics. Walter De Gruyter ; Boston.