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Course Outline
Module 1
Introduction to Data Science & Applications in Marketing
- Analytics Overview: Types of analytics - Predictive, Prescriptive, Inferential
- Analytics Practice in Marketing
- Use of Big Data and Different Technologies - Introduction
Module 2
Marketing in a Digital World
- Introduction to Digital Marketing
- Online Advertising - Introduction
- Search Engine Optimization (SEO) – Google Case Study
- Social Media Marketing: Tips and Insights – 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 – Quick inference – Using Python
- Basic Statistical Modeling – Trends, seasonality, clustering, and classifications (only fundamentals, including different algorithms and their uses, without detailed mathematics) – 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 Improve 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 Calculations – Case study of CLV for business decision-making
- Measuring Cause and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-through rate conversion, website analytics
Module 6
Regression Basics
- What Regression Reveals and Basic Statistics (without extensive mathematical details)
- 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 – Applications; Mention of algorithms
- Interpreting Results – Python programs with outputs
- Customer Targeting using Classification and Clustering – Case Study
- Business Strategy Improvement – Examples from email marketing and promotions
- The Role of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trends and Seasonality – Using a Python-driven case study for 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 Engine
- Personalisation and Business Strategy
- Different Types of Personalised Recommendations – Collaborative and content-based
- Different Algorithms for Recommendation Engines – User-driven, item-driven, hybrid, and matrix factorisation (mention and usage of algorithms without mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximising Sales using Data Science
- Fundamentals of Optimisation Techniques and Their Uses
- Inventory Optimisation – Case Study
- Increasing ROI using Data Science
- Lean Analytics – Startup Accelerator
Module 11
Data Science in Pricing & Promotion I
- Pricing – The Science of Profitable Growth
- Demand Forecasting Techniques - Modelling and estimating the structure of price-response demand curves
- Pricing Decisions – How to optimise pricing decisions – Case Study Using Python
- Promotion Analytics – Baseline calculation and trade promotion model
- Using Promotions for Better Strategy - Sales Model Specification – Multiplicative Model
Module 12
Data Science in Pricing and Promotion II
- Revenue Management - How to manage 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
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.
