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Course Outline
Module 1
Introduction to Data Science and Its Applications in Marketing
- Overview of Analytics: Types include Predictive, Prescriptive, and Inferential analytics
- Practical Applications of Analytics in Marketing
- Introduction to Big Data and Associated Technologies
Module 2
Marketing in the Digital Era
- Introduction to Digital Marketing
- Introduction to Online Advertising
- Search Engine Optimization (SEO) – Case Study of Google
- Social Media Marketing: Insights and Strategies – Examples from Facebook and Twitter
Module 3
Exploratory Data Analysis and Statistical Modeling
- Data Presentation and Visualization – Understanding Business Data using Histograms, Pie Charts, Bar Charts, and Scatter Diagrams for Quick Insights – Implementation using Python
- Fundamentals of Statistical Modeling – Trends, Seasonality, Clustering, and Classifications (Overview only; covering different algorithms and usage without deep technical details) – 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 – The Snapple Case Study and Brand Value – Brand Positioning
- Text Mining for Marketing – Basics of Text Mining – Case Study on Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) with Calculations – Case Study on using CLV for Business Decisions
- Measuring Cause and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-through Conversion and Website Analytics
Module 6
Fundamentals of Regression
- Insights from Regression and Basic Statistics (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; Overview 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 Need for Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trend 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
- 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 (Overview and usage only, without mathematical details)
- 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 Insights
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 – Case Study Using Python
- Promotion Analytics – Baseline Calculation and Trade Promotion Models
- Utilizing 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 and Hotel Pricing – Overview 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.
