Predictive Business Analytics

Course Objectives:

1. Understand the fundamentals of predictive business analysis and its applications in various industries.
2. Acquire skills in data preprocessing, exploratory data analysis, and feature engineering for business insights.
3. Master predictive modeling techniques and evaluation metrics for accurate business forecasting.
4. Explore customer analytics, market sentiment analysis, and ethical considerations in business data usage.

Course Outcomes:

• CO1 Apply statistical and machine learning techniques to analyze business data and predict trends for making informed decisions.
• CO2 Implement predictive models, including regression, classification, and time series analysis, to solve real-world business problems.
• CO3 Design and develop recommender systems and customer analytics solutions for personalized business recommendations.
• CO4 Create data-driven business intelligence dashboards and reports to support strategic decision- making processes.

UNIT I

Introduction to Predictive Business Analytics: Overview, applications in various industries, predictive modeling, data preprocessing, and evaluation metrics, structured and unstructured data, data lifecycle, data quality and preprocessing, data manipulation and exploration Business analytics: Overview of Business analytics, Scope of Business analytics, Business Analytics Process, Relationship of Business Analytics Process and organization, competitive advantages of Business Analytics. Statistical Tools: Statistical Notation, Descriptive Statistical methods, Review of probability distribution and data modelling.

UNIT II

Trendiness and Regression Analysis: Modelling Relationships and Trends in Data, Important Resources, Business Analytics Personnel, Data and models for Business analytics, problem solving, Visualizing and Exploring Data, Business Analytics Technology.

UNIT III

Organization Structures of Business analytics: Team management, Management Issues, Designing Information Policy, Outsourcing, Ensuring Data Quality, measuring contribution of Business analytics, Managing Changes. Descriptive Analytics, Predictive Analytics, Predicative Modelling, Predictive analytics analysis.

UNIT IV

Forecasting Techniques: Qualitative and Judgmental Forecasting, Statistical Forecasting Models, Forecasting Models for Stationary Time Series, Forecasting Models for Time Series with a Linear Trend, Forecasting Time Series with Seasonality, Regression Forecasting with Casual Variables.

Case studies: customer segmentation, churn prediction, fraud detection, and demand forecasting.

Text Books:

1. Business analytics Principles, Concepts, and Applications FT Press Analytics, Marc J. Schniederjans, Dara G. Schniederjans, Christopher M. Starkey, 1st Edition, 2014, ISBN-13: 978-0133989403, ISBN-10: 0133989402.
2. The Value of Business Analytics: Identifying the Path to Profitability, Evan Stubs , John Wiley & Sons, ISBN:9781118983881 |DOI:10.1002/9781118983881,1st Edition 2014

Reference Books:

1. Business Analytics, James Evans, Pearsons Education 2nd Edition, ISBN-13: 978-0321997821 ISBN10: 0321997824
2. Predictive Business Analytics Forward Looking Capabilities to Improve Business, Gary Cokins and Lawrence Maisel, Wiley; 1st Edition, 2013