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Course Outline
Module 1
Introduction to Data Science & Applications in Marketing
- Analytics Overview: Type 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 Secret – Example of Facebook, Twitter
Module 3
Exploratory Data Analysis & Statistical Modeling
- Data Presentation and Visualization – Understanding the Business data using Histogram, Pie-chart, Bar Chart, Scatter Diagram – Fast inference – Using Python
- Basic Statistical Modeling – Trend, Seasonality, Clustering, Classifications (Only basics, different Algorithm and usage, not any detail) – Ready code in Python
- Market Basket Analysis (MBA) – Case Study using Association rules, Support, Confidence, Lift
Module 4
Marketing Analytics I
- Introduction to Marketing Process – Case Study
- Utilizing Data to Improve Marketing Strategy
- Measuring Brand Assets, Snapple and Brand Value – Brand Positioning
- Text Mining for Marketing – Basics of Text mining – Case Study for Social Media Marketing
Module 5
Marketing Analytics II
- Customer Lifetime Value (CLV) with Calculation – Case Study of CLV for business decisions
- Measuring Case and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-rate Conversion, Website Analytics
Module 6
Regression Basics
- What Regression Reveals and basic Statistics (not much details of Mathematics)
- Interpreting Regression Results – With Case Study using Python
- Understanding Log-Log Models – With Case study using Python
- Marketing Mix Models – Case study using Python
Module 7
Classification and Clustering
- Basics of Classification and Clustering – Usage; Mention of Algorithms
- Interpreting the Results – Python Programs with Outputs
- Customer Targeting using Classification and Clustering – Case Study
- Business Strategy Improvement – Example of Email Marketing, Promotions
- Need of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis
- Trend and Seasonality – Using Python driven Case Study - 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 Campaign
Module 9
Recommendation Engine
- Personalization and Business Strategy
- Different Types of Personalized Recommendations – Collaborative, Content based
- Different Algorithms for Recommendation Engine – User driven, Item Driven, Hybrid, Matrix Factorization (Only mention and usage of the algorithms without Mathematical details)
- Recommendation Metrics for Incremental Revenue – Detailed Case Study
Module 10
Maximizing Sales using Data Science
- Basics of Optimization Technique and its Uses
- Inventory Optimization – 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 - Model and estimate the structure of price-response demand curves
- Pricing Decision – How to Optimize Pricing Decision – Case Study Using Python
- Promotion Analytics – Baseline Calculation and Trade Promotion Model
- Using Promotion for Better Strategy - Sales Model Specification – Multiplicative Model
Module 12
Data Science in Pricing and Promotion II
- Revenue Management - How to manage perishable resources with multiple market segments
- Product Bundling – Fast 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 requirements needed to attend this course.
21 Hours
Testimonials (4)
Understanding big data beter
Shaune Dennis - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
It is great to have the course custom made to the key areas that I have highlighted in the pre-course questionnaire. This really helps to address the questions that I have with the subject matter and to align with my learning goals.
Winnie Chan - Statistics Canada
Course - Jupyter for Data Science Teams
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Course - Machine Learning – Data science
The example and training material were sufficient and made it easy to understand what you are doing.