Customer Data Platform 101 Course

Good books to learn Marketing Science




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Online Course: Introduction to Big Data-driven Marketing

  • Overview of Digital Marketing, Python programming, Data Science and Marketing Science. 
  • Final project: building a simple Customer Data Platform for E-Commerce & Retails (B2C)
Part 1: Introduction to Digital Marketing Technology
  1. Definitions and Background
  2. Strategic Thought as It Relates to Digital Marketing
  3. The Website: The Center of Digital Marketing Delivery Mix (Introduction to HTML5, CSS3, Bootstrap 4 and JavaScript)
  4. Content Management System (CMS): Wordpress as digital media hub for content marketing
  5. Search Engine Marketing
  6. Email Marketing
  7. Social Media and Mobile Marketing
  8. Introduction to Customer Database, CRM and Analytics
  9. Introduction to Advertising Technology
  10. Legal Issues: Data privacy, Security, and Intellectual Property
Part 2: Introduction to Computer Programming with Python
Code Editors for programming: VS code and Jupyter
  1. Overview about Computer Programming
  2. Introduction about Python and history
  3. How to use code comments for noting and documentation
  4. Getting Started with Python 
  5. Input and Output
  6. Python Variables
  7. Numbers
  8. String
  9. Conditionals
  10. Functions
  11. Working with Data (Abstract Data Container, Data Structure )
  12. Lists
  13. Tuples
  14. Dictionaries
  15. Set
  16. Expressions
  17. Object Oriented Programming
  18. Classes and Objects
  19. Classes and Objects II (Inheritance and Composition)
  20. Errors and Exceptions
  21. Overview about Internet Application Programming
  22. Overview about Flask framework for API Server 
  23. Python Quiz
  24. Practical project: build a simple website with Flask framework
Part 3: Introduction to Big Data, AI and Data Science
  1. Introduction to Big Data and AI for Marketing 
  2. How to start a data science project 
  3. How  to store data with database systems: SQL,NoSQL, NewSQL
  4. How to use the NumPy & Pandas library for scientific computing and data analysis
  5. Introduction to Data Visualization with popular chart types 
  6. How to implement Data Visualization with matplotlib and d3.js
  7. Introduction Machine Learning with scikit-learn
  8. How to manage machine learning model persistence
  9. Practical project: Using machine learning on advertising data to optimize its performance
Part 4: Introduction to Marketing Science
Overview Marketing Science (how to connect Marketing and Data Science)
  1. Review basic Probability & Statistics that Every Data Scientist Should Know
    • Basic Probability Models and Rules
    • Bayes’ rules, Conditional probability, Chain rule
    • Discrete Random Variables
    • Continuous Random Variables
    • Discrete vs. Continuous
    • Statistical Distributions: Poisson Distribution, Binomial Distribution. Uniform distribution
    • Probability Density Functions And Cumulative Density Function
    • Accuracy Analysis and Testing Data Science Models
    • Theorems and Algorithms: Bayes Theorem, K-Nearest Neighbor Algorithm, Bagging/Bootstrap aggregating
  2. Brief principles of consumer behaviour and marketing strategy for Industry 4.0
    • Marketing Mix Models (4P, 7P, 4C)
    • Quickly review 4P(Product, Place, Price, Promotion) in the age of Big Data
    • Introduction to new strategic marketing concepts 4 P’s: People, Partners, Processes, and Platforms
    • Introduction to Customer-First Marketing Model
    • Introduction to Customer Data Analytics
    • Introduction to Visual Thinking 
    • Introduction to Customer Experience and omni-channel marketing 
    • How to apply Design thinking for Customer Experience Optimization 
    • Introduction to USPA framework
    • Customer lifetime value and customer journey analysis
    • Big Data for Customer Journey Analytics
  3. Practical case studies:
    • Exploratory Data Analysis with Python in B2B Marketing
    • User’s Behaviors Better with Cohort Analysis in Python
Part 5: Practical Project for Business
  1. Introduction to practical case study: CDP for Retail and E-Commerce
  2. How to unify customer data profiles from multiple sources
    • Website 
    • Mobile App
    • Social Media
    • Digital marketing campaign
    • Point of Sales (Offline store)
  3. How to segment profiles
    • Standard segmentation methodology  
    • RFM model 
    • Lead Scoring Model 
  4. How to personalize customer experience 
    • How to predict buyer persona from behavioral data stream  
    • How to match buyer persona with right products and services 
  5. How to activate customer engagement with Data Science and Marketing Science
    • Chatbot
    • Email Marketing
    • Programmatic Ad Tech
    • Recommendation System 

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