Business Analytics

Syllabus

20MBA203: Business Analytics

Module 1

Business Analytics: Business Analytics, evolution and scope, Business Analytics process, Business Intelligence.

Decision Models. Spreadsheets Modelling, Datasets, data cleaning, outliers and outliers diagnostics. Data Visualization -Tools and techniques, Applications. Big Data Analytics, Machine learning algorithms, Data Mining process, Quality of predictions, Optimizing complex decisions, Challenges in data driven decision making.

Module 2

Financial Analytics and Operations Analytics: Understanding Risk, credit risk analysis, fraud detection and prevention analytics, analytics in banking and financial services - analytics in retail banking and wealth management.

Demand Planning – Forecasting, Model building, Supply chain analytics- Supply planning - Procurement and Strategic Sourcing, Inventory Modeling - Aggregate planning and resource allocation decisions, Make/Buy decision.

Module 3

Human Resource Analytics: Basics of HR analytics, HR Metric and HR Analytics, Intuition versus analytical thinking; People analytics, Recruitment Analytics- Evaluating Reliability and validity of selection models, Finding out selection bias, Predicting the performance and turnover, On Boarding Analytics, Staffing Analytics, Performance & Skill Gap Analytics, Compensation & Benefit Analytics, Training & Learning Analytics, Promotion and Succession Planning Analytics, Compliance Analytics, Attrition & Retention Analytics, HRMS/HRIS and data sources; Analytics frameworks like LAMP, HCM:21 Model.HR dashboards, Power BI.

Module 4

Marketing Analytics: Basics of marketing analytics, marketing decisions models, characteristics, types and benefits of marketing decisions models, Market Intelligence, Text analysis and search analysis, Customer profiling, Statistical programming, Choice Models and Logistic Regression, data mining and visualization, Response Models Concept, Factor segmentation, clustering algorithms, perceptual maps, Use of Excel to solve business problems, Google Analytics.

Web Analytics: Click stream analytics, anonymous vs. registered users analysis, Social Media Analytics - User generated content – Page tagging, Server log files, Data abstractions. Sentiment Analysis, Analytics in digital decoding consumer intent, decoding customer sentiments from comments, Text mining from opinion platforms.

Module 5

Predictive Analytics and Application of Analytics in Business:

Logic driven predictive models – single-period purchase decisions, multiple time period models, overbooking decisions. Data Driven predictive models - retail pricing markdowns, modeling relationships and trends Models involving uncertainty - what-if analysis, scenario manager, goal-seek.

Applications in other business areas - Analytics in telecom and location-based intelligence marketing, analytics in consumer-packaged goods (CPG), analytics in utilities, analytics in healthcare, analytics in online retail.