Course Outline

Module 1

Introduction to Data Science & Applications in Marketing

  • Analytics Overview: Type of analytics- Predictive, Prescriptive, Inferential
  • Analytics Practice in Marketing
  • Use of Big Data and Different Technologies - Introduction

Module 2

Marketing in a Digital World

  • Introduction to Digital Marketing
  • Online Advertising - Introduction
  • Search Engine Optimization (SEO) – Google Case Study
  • Social Media Marketing: Tips and Secret – Example of Facebook, Twitter

Module 3

Exploratory Data Analysis & Statistical Modeling

  • Data Presentation and Visualization – Understanding the Business data using Histogram, Pie-chart, Bar Chart, Scatter Diagram – Fast inference – Using Python
  • Basic Statistical Modeling – Trend, Seasonality, Clustering, Classifications (Only basics, different Algorithm and usage, not any detail) – Ready code in Python
  • Market Basket Analysis (MBA) – Case Study using Association rules, Support, Confidence, Lift

Module 4

Marketing Analytics I

  • Introduction to Marketing Process – Case Study
  • Utilizing Data to Improve Marketing Strategy
  • Measuring Brand Assets, Snapple and Brand Value – Brand Positioning
  • Text Mining for Marketing – Basics of Text mining – Case Study for Social Media Marketing

Module 5

Marketing Analytics II

  • Customer Lifetime Value (CLV) with Calculation – Case Study of CLV for business decisions
  • Measuring Case and Effect through Experiments – Case Study
  • Calculating Projected Lift
  • Data Science in Online Advertising – Click-rate Conversion, Website Analytics

Module 6

Regression Basics

  • What Regression Reveals and basic Statistics (not much details of Mathematics)
  • Interpreting Regression Results – With Case Study using Python
  • Understanding Log-Log Models – With Case study using Python
  • Marketing Mix Models – Case study using Python

Module 7

Classification and Clustering

  • Basics of Classification and Clustering – Usage; Mention of Algorithms
  • Interpreting the Results – Python Programs with Outputs
  • Customer Targeting using Classification and Clustering – Case Study
  • Business Strategy Improvement – Example of Email Marketing, Promotions
  • Need of Big Data Technologies in Classification and Clustering

Module 8

Time Series Analysis

  • Trend and Seasonality – Using Python driven Case Study - Visualizations
  • Different Time Series Techniques – AR and MA
  • Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
  • Time Series Prediction for Marketing Campaign

Module 9

Recommendation Engine

  • Personalization and Business Strategy
  • Different Types of Personalized Recommendations – Collaborative, Content based
  • Different Algorithms for Recommendation Engine – User driven, Item Driven, Hybrid, Matrix Factorization (Only mention and usage of the algorithms without Mathematical details)
  • Recommendation Metrics for Incremental Revenue – Detailed Case Study

Module 10

Maximizing Sales using Data Science

  • Basics of Optimization Technique and its Uses
  • Inventory Optimization – Case Study
  • Increasing ROI using Data Science
  • Lean Analytics – Startup Accelerator

Module 11

Data Science in Pricing & Promotion I

  • Pricing – The Science of Profitable Growth
  • Demand Forecasting Techniques - Model and estimate the structure of price-response demand curves
  • Pricing Decision – How to Optimize Pricing Decision – Case Study Using Python
  • Promotion Analytics – Baseline Calculation and Trade Promotion Model
  • Using Promotion for Better Strategy - Sales Model Specification – Multiplicative Model

Module 12

Data Science in Pricing and Promotion II

  • Revenue Management - How to manage perishable resources with multiple market segments
  • Product Bundling – Fast and Slow Moving Products – Case Study with Python
  • Pricing of Perishable Goods and Services - Airline & Hotel Pricing – Mention of Stochastic Models
  • Promotion Metrics – Traditional and Social

Requirements

There are no specific requirements needed to attend this course.

 21 Hours

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