Back close

Course Detail

Course Name Business Analytics
Course Code 26CSC3333
Program 5 Year Integrated M.Sc in Data Science
Credits 3
Campus Coimbatore

Syllabus

Unit 1

Introduction to Business Analytics: Definition and Scope of Business analytics. Importance of data-driven decision making. Overview of analytics tools and techniques. Ethical considerations in business analytics. Data collection methods and sources, Types of Data – Attributes and Measurement – Types of data sets – Data quality – Types of Digital Data.  Data Warehouse Architecture, Data Warehouse technologies, Pre-processing techniques – Data cleaning and data quality assurance, Data integration and transformation techniques, Data governance and security.

Unit 2

Descriptive Analytics Datavisualisation using charts: histograms, bar charts, box plots, line graphs, scatter plots. etc; Data description: Measure of Central Tendency, Measure of Dispersion, Relationship between variables: Covariance, Correlation and coefficient of determination.

OLTP and OLAP OLTP – OLAP – Different OLAP Architectures – OLTP and OLAP – Data models for OLTP and OLAP – Role of OLAP Tools in BI Architecture. Business Intelligence – Business Intelligence defined – Evolution of BI and Role of DSS, EIS, MIS and Digital Dashboards – Need for BI – BI value chain – Introduction to Business Analytics. BI Definitions and Concepts – BI Component Framework – Need for BI – BI Users – Business Intelligence applications – BI roles and responsibilities.

Unit 3

Predictive Analytics Regression Analysis: Linear, logistic regression. Time Series Analysis: Forecasting methods (e.g. exponential smoothing). – Measures of Forecast Error – Trend Analysis – Time Series Models – Auto Regressive Model – Applications of Time Series Models. Advanced Forecasting Techniques: Ratio to Moving Average, Winter Method.

Prescriptive Analytics

Nonlinear optimization, Monte Carlo simulations, Decision trees, Bayesian networks and BIC, Cross- validation, Boot strap methods, Decision Support Systems (DSS) and their applications.

Text Books / References

Text books / References

  1. Marc J. Schniederjans, Dara G. Schniederjans and Christopher M. Starkey, ” Business Analytics Principles, Concepts, and Applications – What, Why, and How”, Pearson Ed, 2014
  2. Christian Albright S and Wayne L. Winston, “Business Analytics – Data Analysis and Decision Making”, Fifth edition, Cengage Learning, 2015.
  3. James R. Evans, “Business Analytics – Methods, Models and Decisions”, Pearson Ed, 2012.

Objectives and Outcomes

Course Outcomes:

  • CO1: Understand the concept of data warehouse architecture and data preprocessing
  • CO2: Gain knowledge in graphical visualization techniques, OLTP and OLAP
  • CO3: Apply and analyse the real time data using predictive analytics   and prescriptive analytics

CO-PO Mapping

  PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12
CO1 3 3 3 2 2 3         2 2
CO2 3 3 3 3 3 3         2 2
CO3 2 3 3 3 3 3         2 2

DISCLAIMER: The appearance of external links on this web site does not constitute endorsement by the School of Biotechnology/Amrita Vishwa Vidyapeetham or the information, products or services contained therein. For other than authorized activities, the Amrita Vishwa Vidyapeetham does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this web site.

Admissions Apply Now