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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
- Practical Applications of Analytics in Marketing
- Introduction to Big Data and Relevant Technologies
Module 2
Marketing in the Digital Era
- Introduction to Digital Marketing
- Overview of Online Advertising
- Search Engine Optimization (SEO) – Case Study on Google
- Social Media Marketing: Tips and Strategies – Examples from Facebook and Twitter
Module 3
Exploratory Data Analysis & Statistical Modeling
- Data Presentation and Visualization – Interpreting business data using Histograms, Pie Charts, Bar Charts, and Scatter Diagrams for rapid inference using Python
- Fundamentals of Statistical Modeling – Trends, Seasonality, Clustering, and Classifications (focus on basics, algorithm types, and usage rather than deep technical details) – Ready-to-use Python code
- Market Basket Analysis (MBA) – Case Study involving 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 and Brand Value – Brand Positioning using Snapple as a case study
- 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) with Calculation – Case Study on CLV for business decision-making
- Measuring Causality and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-through rates, conversion, and website analytics
Module 6
Regression Basics
- What Regression Reveals and Basic Statistics (with minimal 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 from Email Marketing and Promotions
- The Necessity 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 models
- Time Series Models – ARMA, ARIMA, ARIMAX (Usage and examples with Python) – Case Study
- Time Series Prediction for Marketing Campaigns
Module 9
Recommendation Engines
- Personalization and Business Strategy
- Types of Personalized Recommendations – Collaborative and Content-based filtering
- Algorithms for Recommendation Engines – User-driven, Item-driven, Hybrid, and Matrix Factorization (Mention and usage only, without mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales using Data Science
- Fundamentals of Optimization Techniques and Their Applications
- Inventory Optimization – Case Study
- Increasing ROI through Data Science
- Lean Analytics – Startup Accelerator insights
Module 11
Data Science in Pricing & Promotion I
- Pricing – The Science of Profitable Growth
- Demand Forecasting Techniques – Modeling and estimating price-response demand curves
- Pricing Decisions – How to Optimize Pricing – 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-moving 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 media metrics
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.
