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Course Outline
Module 1
Introduction to Data Science and Its Applications in Marketing
- Analytics Overview: Types of analytics - Predictive, Prescriptive, Inferential
- Practicing Analytics in Marketing
- Leveraging Big Data and Various Technologies - Introduction
Module 2
Marketing in the Digital Era
- Introduction to Digital Marketing
- Online Advertising - Introduction
- Search Engine Optimization (SEO) – Google Case Study
- Social Media Marketing: Tips and Insights – Examples from Facebook and Twitter
Module 3
Exploratory Data Analysis and Statistical Modeling
- Data Presentation and Visualization – Interpreting business data using Histograms, Pie charts, Bar charts, and Scatter diagrams for rapid inference – Utilizing Python
- Basics of Statistical Modeling – Trends, Seasonality, Clustering, and Classifications (Foundational concepts, algorithms, and usage; detailed mathematical derivation is not covered) – Ready-to-use Python code
- Market Basket Analysis (MBA) – Case study applying Association rules, Support, Confidence, and Lift
Module 4
Marketing Analytics I
- Introduction to the Marketing Process – Case Study
- Using Data to Enhance Marketing Strategy
- Evaluating Brand Assets and Brand Value – Brand Positioning (Case Study: Snapple)
- Text Mining for Marketing – Fundamentals of Text Mining – Case Study on Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) – Calculation and Case Study on using CLV for business decisions
- Measuring Causality and Impact through Experiments – Case Study
- Calculating Projected Lift
- Applying Data Science in Online Advertising – Click-through Rate Conversion, Website Analytics
Module 6
Regression Basics
- What Regression Reveals and Foundational Statistics (limited mathematical detail)
- Interpreting Regression Results – Case Study using Python
- Understanding Log-Log Models – Case Study using Python
- Marketing Mix Models – Case Study using Python
Module 7
Classification and Clustering
- Fundamentals of Classification and Clustering – Usage and Mention of Algorithms
- Interpreting the Results – Python Programs with Outputs
- Customer Targeting using Classification and Clustering – Case Study
- Improving Business Strategy – Examples involving Email Marketing and Promotions
- The Role of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trends and Seasonality – Python-driven Case Study and Visualizations
- Various Time Series Techniques – AR and MA
- Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
- Forecasting for Marketing Campaigns
Module 9
Recommendation Engines
- Personalization and Business Strategy
- Types of Personalized Recommendations – Collaborative and Content-based
- Algorithms for Recommendation Engines – User-driven, Item-driven, Hybrid, Matrix Factorization (Overview of algorithms and usage without detailed mathematics)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales Through Data Science
- Fundamentals of Optimization Techniques and Their Applications
- Inventory Optimization – Case Study
- Increasing ROI Using Data Science
- Lean Analytics – Startup Accelerator
Module 11
Data Science in Pricing and 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 Strategies – Case Study Using Python
- Promotion Analytics – Baseline Calculation and Trade Promotion Models
- Leveraging Promotions 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 Perishable Goods and Services – Airline and Hotel Pricing – Mention of Stochastic Models
- Promotion Metrics – Traditional and Social
Requirements
There are no specific prerequisites required to enroll in 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.
