Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Module 1
Introduction to Data Science and Its 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 Landscape
- Introduction to Digital Marketing
- Overview of Online Advertising
- Search Engine Optimization (SEO) – A Google Case Study
- Social Media Marketing: Tips and Insights – Examples using 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
- Fundamentals of Statistical Modeling – Trends, Seasonality, Clustering, and Classifications (Basic overview of different algorithms and usage, without detailed explanations) – Python Code 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, 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 on CLV for Business Decisions
- Measuring Causality and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-Through Rate Conversion and Website Analytics
Module 6
Fundamentals 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 involving 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
- Various 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, and Matrix Factorization (Only mention and usage of algorithms, without mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales through Data Science
- Fundamentals 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 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 – 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 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.
