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Course Outline
Module 1
Introduction to Data Science and Its Applications in Marketing
- Analytics Overview: Types of analytics - Predictive, Prescriptive, and Inferential
- Analytics Practices 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) - A Google Case Study
- Social Media Marketing: Tips 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 rapid insights - Implementation with Python
- Basics of Statistical Modeling - Trends, seasonality, clustering, and classifications - Overview of different algorithms and their usage (no detailed mathematics) - Python code examples provided
- 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 and Brand Value - Case study on Brand Positioning using Snapple as an example
- Text Mining for Marketing - Fundamentals of text mining - Case study on Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) Calculation - Case study on CLV for business decision-making
- Measuring Cause and Effect Through Experiments - Case study
- Calculating Projected Lift
- Data Science in Online Advertising - Click-rate conversion and website analytics
Module 6
Fundamentals of Regression
- What Regression Reveals 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
- Basics of Classification and Clustering - Usage and mention of algorithms
- Interpreting the Results - Python programs with output examples
- Customer Targeting Using Classification and Clustering - Case study
- Improving Business Strategy - Examples including 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 with visualizations
- Various Time Series Techniques - Autoregressive (AR) and Moving Average (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 without mathematical details
- Metrics for Incremental Revenue from Recommendations - Detailed case study
Module 10
Maximizing Sales Through Data Science
- Basics of Optimization Techniques and Their Applications
- Inventory Optimization - Case study
- Increasing Return on Investment (ROI) Using Data Science
- Lean Analytics - Insights from Startup Accelerators
Module 11
Data Science in Pricing and Promotion I
- Pricing - The Science of Profitable Growth
- Demand Forecasting Techniques - Modeling and estimating price-response demand curves
- Pricing Decisions - How to Optimize - Case study using Python
- Promotion Analytics - Baseline calculation and trade promotion models
- Utilizing Promotions for Better Strategy - Sales model specification using multiplicative models
Module 12
Data Science in Pricing and Promotion II
- Revenue Management - Managing perishable resources across multiple market segments
- Product Bundling - Fast-moving vs. 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 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.
