Course Objectives

• To learn the Analytical Models and Data Dashboards using Excel
• To understand the design of various models for summarizing and visualizing data
• To understand how to build good spreadsheet models, What-If analysis & linear optimization models
• To learn how to apply various statistical techniques using SPSS package

UNIT I Decision Making and Data Visualization

Decision Making – Business Analytics Defined – Categorization of Analytical Methods and Models – Big Data – Business Analytics in Practice – Descriptive Statistics – Types of Data – Modifying Data in Excel – Creating Distributions from Data – Measures of Location – Measures of Variability – Analyzing Distributions – Measures of Association between Two Variables – Data Visualization – Table – Charts – Advanced Data Visualization – Data Dashboards

UNIT II Linear Regression & Forecasting

Linear Regression & Forecasting – Simple Linear Regression Model – Least Square Method – Multiple Regression Model – Inference and Regression – Time Series Patterns – Forecast Accuracy – Moving Averages and Exponential Smoothening – Regression Analysis for Forecasting

UNIT III Optimization Models

Spreadsheet Models & Linear Optimization Models – Building Good Spreadsheet Models – What-If Analysis – Useful Excel Functions for Modeling – Linear Optimization Models – Simple Maximization Problem – Simple Minimization Problem – Sensitivity Analysis

UNIT IV Analysis using SPSS

SPSS - Getting Started - Designing a Study - Preparing a Codebook - Getting to know IBM SPSS - Preparing the Data File - Creating a Data File and Entering Data - Descriptive Statistics - Using Graphs to Describe and Explore the Data - Manipulating the Data - Checking the Reliability of a Scale - Choosing the Right Statistic - Statistical Techniques to Explore Relationships among Variables – Correlation - Partial Correlation - Multiple Regression - Factor Analysis - Non-Parametric Statistics - t-Tests - One-Way Analysis of Variance - Two-Way Between-Groups ANOVA

UNIT V Introduction to Latest Data Visualization Tools

Data Visualization – Recent Developments in Visualization Software - Introduction to Tableau, Rapid Miner, Power BI, Machine Learning R & Python Programming – Simple Exercises

Learning Resources

• Jeffrey D. Camm,James J. Cochran,Michael J. Fry,Jeffrey W. Ohlmann,David R. Anderson,Dennis J. Sweeney,Thomas A. Williams – “Business Analytics” – Cengage – 3rd Edition – 2019
• Anil Maheswari - “Data Analytics”- McGraw Hill Education (India) Private Ltd, Sixth reprint 2019
• Andy Field - "Discovering Statistics Using IBM SPSS Statistics" - Sage Publications Ltd - 5th Edition - 2018
• JuliantPallant – “SPSS Survival: A step by step guide to data analysis using IBM SPSS” – McGraw Hill Education – 6th Edition - 2016.
• Tim Costello, Lori Blackshear – “Prepare Your Data For Tableau: A Practical Guide To The Tableau Data Prep Tool” – Apress – 1stEdition – 2020
• Daniel G. Murray - "Tableau Your Data!: Fast and Easy Visual Analysis with Tableau Software" - 2ndEdition - January 2016
• Markus Hofmann, Ralf Klinkenberg - “RapidMiner: Data Mining Use Cases and Business Analytics Applications” - Chapman and Hall/CRC –1st Edition –2016
• Vijay Kotu and Bala Deshpande - "Predictive Analytics and Data Mining Concepts and Practice with Rapidminer" - 1st Edition - 2015
• James (JD) Long – “R Cookbook” - O’Reilly Media Inc. - 2nd Edition– 2019
• SandipRakshit - "R Programming for Beginners" - McGraw Hill Education - First edition (21 July 2017)
• Brian Larson - "Data Analysis with Microsoft Power BI" - McGraw-Hill Education - 1st Edition – 2020
• Dan Clark - "Beginning Power BI with Excel 2013" – Apress - 1st Edition (10 October 2014)
• Gowrishankar S, Veena A - “Introduction to Python Programming” - Chapman and Hall/CRC – 1st Edition - 2018