Data Science essential for Marketing/Sales professionals Training Course

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