Data Science essential for Marketing/Sales professionals Training Course

Module 1

Introduction to Data Science and Applications in Marketing for Government

• Analytics Overview: Types of Analytics—Predictive, Prescriptive, Inferential
• Application of Analytics in Marketing for Government
• Introduction to Big Data and Relevant Technologies

Module 2

Marketing in a Digital World for Government

• Overview of Digital Marketing for Government
• Introduction to Online Advertising for Government
• Search Engine Optimization (SEO) – Case Study: Google's Practices
• Social Media Marketing Strategies – Examples from Facebook and Twitter

Module 3

Exploratory Data Analysis and Statistical Modeling for Government

• Data Presentation and Visualization Techniques – Utilizing Histograms, Pie Charts, Bar Charts, Scatter Diagrams – Fast Inference Using Python
• Basic Statistical Modeling – Understanding Trends, Seasonality, Clustering, and Classifications (Overview of Algorithms and Usage) – Ready-to-Use Code in Python
• Market Basket Analysis (MBA) – Case Study Using Association Rules, Support, Confidence, and Lift

Module 4

Marketing Analytics I for Government

• Introduction to the Marketing Process – Case Study
• Leveraging Data to Enhance Marketing Strategies for Government
• Measuring Brand Assets and Value – Case Studies on Brand Positioning (e.g., Snapple)
• Text Mining in 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 on Business Decisions
• Measuring Cause and Effect Through Experiments – Case Study
• Calculating Projected Lift in Marketing Campaigns
• Application of Data Science in Online Advertising – Click-Through Rate Conversion, Website Analytics

Module 6

Regression Basics for Government

• Understanding What Regression Reveals and Basic Statistics (Limited Mathematical Details)
• Interpreting Regression Results – Case Study Using Python
• Exploring 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 and Mention of Algorithms
• Interpreting Results – Python Programs with Outputs
• Customer Targeting Using Classification and Clustering – Case Study
• Enhancing Business Strategies – Examples from Email Marketing and Promotions
• The Role of Big Data Technologies in Classification and Clustering

Module 8

Time Series Analysis for Government

• Analyzing Trends and Seasonality – Python-Driven Case Study with Visualizations
• Different Time Series Techniques – AR and MA Models
• Time Series Models – ARMA, ARIMA, ARIMAX (Usage and Examples with Python) – Case Study
• Predicting Marketing Campaign Outcomes Using Time Series Analysis

Module 9

Recommendation Engines for Government

• Personalization and Business Strategy in Government Services
• Types of Personalized Recommendations – Collaborative, Content-Based
• Algorithms for Recommendation Engines – User-Driven, Item-Driven, Hybrid, Matrix Factorization (Overview and Usage Without Mathematical Details)
• Measuring Incremental Revenue Through Recommendation Metrics – Detailed Case Study

Module 10

Maximizing Sales Using Data Science for Government

• Fundamentals of Optimization Techniques and Their Applications
• Inventory Optimization – Case Study
• Increasing Return on Investment (ROI) Through Data Science
• Lean Analytics for Government Startups – Accelerator Programs

Module 11

Data Science in Pricing and Promotion I for Government

• The Science of Profitable Growth Through Pricing
• Demand Forecasting Techniques – Modeling and Estimating Price-Response Demand Curves
• Optimizing Pricing Decisions – Case Study Using Python
• Promotion Analytics – Baseline Calculation and Trade Promotion Models
• Enhancing Strategies Through 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 Strategies – Case Study Using Python (Fast and Slow Moving Products)
• Pricing of Perishable Goods and Services – Airline and Hotel Pricing – Overview of Stochastic Models
• Promotion Metrics – Traditional and Social Media Perspectives