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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
- Practical Analytics in Marketing
- Introduction to Big Data and Various Technologies
Module 2
Marketing in the Digital Age
- Introduction to Digital Marketing
- Introduction to Online Advertising
- Search Engine Optimization (SEO) - A Google Case Study
- Social Media Marketing: Tips and Secrets - 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 - Quick inference - Using Python
- Basics of Statistical Modeling - Trends, Seasonality, Clustering, and Classifications (Overview of 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 on Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) with Calculation - Case Study on using CLV for business decisions
- Measuring Causality and Effect through Experiments - Case Study
- Calculating Projected Lift
- Data Science in Online Advertising - Click-rate Conversion and Website Analytics
Module 6
Basics of Regression
- What Regression Reveals and Basic Statistics (Minimal mathematical detail)
- Interpreting Regression Results - With a Case Study using Python
- Understanding Log-Log Models - With a 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 from Email Marketing and Promotions
- The Need for Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trends and Seasonality - Using Python-driven Case Studies and Visualizations
- Different 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 Engines
- Personalization and Business Strategy
- Types of Personalized Recommendations - Collaborative and Content-based
- Algorithms for Recommendation Engines - User-driven, Item-driven, Hybrid, Matrix Factorization (Mention and usage only, without mathematical details)
- Recommendation Metrics for Incremental Revenue - Detailed Case Study
Module 10
Maximizing Sales with Data Science
- Basics 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 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
- 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-Moving and Slow-Moving Products - Case Study with Python
- Pricing of Perishable Goods and Services - Airline and 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.
