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Course Outline
Module 1
Introduction to Data Science & Applications in Marketing
- Overview of Analytics: Types including Predictive, Prescriptive, and Inferential
- Analytics Practices in Marketing
- Introduction to Big Data and Associated Technologies
Module 2
Marketing in a Digital World
- Introduction to Digital Marketing
- Overview of Online Advertising
- Search Engine Optimization (SEO) – Google Case Study
- Social Media Marketing: Tips and Insights – Examples of Facebook, Twitter
Module 3
Exploratory Data Analysis & Statistical Modeling
- Data Presentation and Visualization – Understanding business data using Histograms, Pie charts, Bar Charts, Scatter Diagrams – Quick Inferences – Using Python
- Basic Statistical Modeling – Trends, Seasonality, Clustering, Classifications (Fundamental overview of different algorithms and usage, without deep technical details) – Ready-to-use Python code
- Market Basket Analysis (MBA) – Case Study using Association Rules, Support, Confidence, and Lift
Module 4
Marketing Analytics I
- Introduction to the Marketing Process – Case Study
- Leveraging Data to Enhance Marketing Strategy
- Measuring Brand Assets, Snapple, and Brand Value – Brand Positioning
- Text Mining for Marketing – Fundamentals 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 Cause 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 (Minimal mathematical details)
- 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
- Fundamentals of Classification and Clustering – Usage; Mention of Algorithms
- Interpreting the Results – Python Programs with Outputs
- Customer Targeting using Classification and Clustering – Case Study
- Improving Business Strategy – Examples of Email Marketing, Promotions
- The Necessity of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trends and Seasonality – Using Python-driven Case Studies and Visualizations
- Various Time Series Techniques – AR and MA
- Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
- Time Series Prediction for Marketing Campaigns
Module 9
Recommendation Engine
- Personalization and Business Strategy
- Different Types of Personalized Recommendations – Collaborative, Content-based
- Different Algorithms for Recommendation Engines – User-driven, Item-driven, Hybrid, Matrix Factorization (Mention and usage of algorithms without mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales using Data Science
- Fundamentals of Optimization Techniques and Their 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 – Modeling and Estimating the Structure of Price-Response Demand Curves
- Pricing Decisions – How to Optimize Pricing Decisions – 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 – Managing Perishable Resources Across 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 prerequisites for attending this course.
21 Hours
Testimonials (1)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
