Course

Business Analytics Basics: Definition of analytics, Evolution of analytics, Need of Analytics, Business analytics vs business analysis, Business intelligence vs Data Science, Data Analyst Vs Business Analyst, Business Analytics at the Strategic Level, Functional Level, Analytical Level, Data Warehouse Level. Market Segmentation Variables, Market Segmentation Types, Marketing Data Landscape, Analyzing the trend of data in Marketing– case studies.

Time series as a discrete parameter stochastic process, Auto – covariance, Auto-correlation functions and their properties. Exploratory time series analysis, Test for trend and seasonality, Exponential and moving average smoothing, forecasting based on smoothing. 

Linear time series models: Autoregressive, Moving Average, autoregressive Moving Average models, Autoregressive Integrated Moving Average models. Estimation of ARMA models: Yule-Walker estimation for AR Processes, Maximum likelihood and least squares estimation for ARMA Processes.

Course Outcomes:

CO1: Understand the basics of business analytics.

CO2: Gain knowledge about auto-correlations and time series analysis.

CO3: To understand the linear time series models.

CO4: To gain knowledge about sYule Walker estimation for AR processes.

CO-PO Mapping:

Text Books / References Books:

1. GrigsbyGert H.N Laursen and Jesper Thorlund : Business analytics for managers taking business intelligence beyond reporting, second edition 2016.
2. Wayne L. Winston:Marketing Analytics: Data-Driven Techniques with Microsoft Excel, Wiley,2014.
3. Mike Grigsby : Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques, Kogan Page; 2 edition ,2018
4. Mike Anderson, T.W : The Statistical Analysis of Time Series, John Wiley, New York, 1971.
5. Kendall, Sir Maurice and Ord, J.K. : Time Series, Edward Arnold, London, 1990.