Course Outline
Module 1
Introduction to Data Science and Applications in Marketing for Government
- Overview of Analytics: Types of analytics—Predictive, Prescriptive, Inferential
- Practical Application of Analytics in Marketing
- Introduction to Big Data and Relevant Technologies
Module 2
Marketing in a Digital World for Government
- Introduction to Digital Marketing
- Overview of Online Advertising
- Search Engine Optimization (SEO) – Case Study: Google
- Social Media Marketing Strategies – Examples: Facebook, Twitter
Module 3
Exploratory Data Analysis and Statistical Modeling for Government
- Data Presentation and Visualization – Using Histograms, Pie Charts, Bar Charts, Scatter Diagrams to Understand Business Data – Fast Inference with Python
- Basic Statistical Modeling – Trends, Seasonality, Clustering, Classification (Overview of Algorithms and Usage) – Ready-to-Use Code in Python
- Market Basket Analysis (MBA) – Case Study Using Association Rules, Support, Confidence, Lift
Module 4
Marketing Analytics I for Government
- Introduction to the Marketing Process – Case Study
- Leveraging Data to Enhance Marketing Strategy
- Measuring Brand Assets and Value – Case Studies: Snapple, Brand Positioning
- Text Mining for Marketing – Basics of Text Mining – Case Study for Social Media Marketing
Module 5
Marketing Analytics II for Government
- Customer Lifetime Value (CLV) Calculation – Case Study for Business Decisions
- Measuring Cause and Effect through Experiments – Case Study
- Calculating Projected Lift
- Data Science in Online Advertising – Click-rate Conversion, Website Analytics
Module 6
Regression Basics for Government
- What Regression Reveals and Basic Statistics (Minimal Mathematical Details)
- 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 for Government
- Basics of Classification and Clustering – Usage; Overview of Algorithms
- Interpreting the Results – Python Programs with Outputs
- Customer Targeting Using Classification and Clustering – Case Study
- Business Strategy Improvement – Examples: Email Marketing, Promotions
- Importance of Big Data Technologies in Classification and Clustering
Module 8
Time Series Analysis for Government
- Trend and Seasonality – Python-Driven Case Study with Visualizations
- Various Time Series Techniques – AR and MA
- Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
- Predicting Marketing Campaigns Using Time Series Analysis
Module 9
Recommendation Engine for Government
- Personalization and Business Strategy
- Types of Personalized Recommendations – Collaborative, Content-Based
- Algorithms for Recommendation Engines – User-Driven, Item-Driven, Hybrid, Matrix Factorization (Mention and Usage Without Mathematical Details)
- Metrics for Incremental Revenue from Recommendations – Detailed Case Study
Module 10
Maximizing Sales Using Data Science for Government
- Basics of Optimization Techniques and Their Uses
- Inventory Optimization – Case Study
- Increasing ROI Through Data Science
- Lean Analytics – Startup Accelerator
Module 11
Data Science in Pricing and Promotion I for Government
- Pricing – The Science of Profitable Growth
- Demand Forecasting Techniques – Modeling and Estimating Price-Response Demand Curves
- Pricing Decisions – Optimizing Pricing Decisions – Case Study Using Python
- Promotion Analytics – Baseline Calculation and Trade Promotion Model
- Strategic Use of Promotions – Sales Model Specification – Multiplicative Model
Module 12
Data Science in Pricing and Promotion II for Government
- Revenue Management – Managing Perishable Resources with Multiple Market Segments
- Product Bundling – Fast 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 participate in this course for government professionals.
Testimonials (5)
Younes is a great trainer. Always willing to assist, and very patient. I will give him 5 stars. Also, the QLIK sense training was excellent, due to an excellent trainer.
Dietmar Glanninger - BMW
Course - Qlik Sense for Data Science
Trainer was accommodative. And actually quite encouraging for me to take up the course.
Grace Goh - DBS Bank Ltd
Course - Python in Data Science
Subject presentation knowledge timing
Aly Saleh - FAB banak Egypt
Course - Introduction to Data Science and AI (using Python)
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
