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Course Outline
Module 1
Introduction to Data Science & Applications in Marketing
- Analytics Overview: Types of analytics - Predictive, Prescriptive, and Inferential
- Practical Applications of Analytics in Marketing
- Introduction to Big Data and Relevant Technologies
Module 2
Marketing in the Digital Era
- Introduction to Digital Marketing
- Introduction to Online Advertising
- Search Engine Optimization (SEO) – A Google Case Study
- Social Media Marketing: Tips and Strategies – Examples from Facebook and Twitter
Module 3
Exploratory Data Analysis & Statistical Modeling
- Data Presentation and Visualization – Understanding business data using Histograms, Pie charts, Bar charts, and Scatter Diagrams for rapid insights using Python
- Fundamentals of Statistical Modeling – Trends, Seasonality, Clustering, and Classifications (covering basics, different algorithms, and usage without detailed technical depth) – Ready-to-use Python code provided
- Market Basket Analysis (MBA) – Case Study utilizing 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 and a Case Study on Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) – Calculation and Case Study on CLV for business decisions
- Measuring Cases and Effects through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-rate Conversion and Website Analytics
Module 6
Regression Basics
- What Regression Reveals and Basic 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 Results – Python Programs with Outputs
- Customer Targeting using Classification and Clustering – Case Study
- Improving Business Strategy – Examples including 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
- Predicting Time Series 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, and Matrix Factorization (Mention and usage of algorithms without mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales with Data Science
- Fundamentals of Optimization Techniques and Their Uses
- Inventory Optimization – Case Study
- Increasing ROI through 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 Models
- Using 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 of Perishable Goods and Services – Airline & Hotel Pricing – Mention of Stochastic Models
- Promotion Metrics – Traditional and Social
Requirements
There are no specific prerequisites required to attend 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.
