Part 1: Introduction
  • A Practical Example: What You Will Learn in This Course
  • What Does the Course Cover
  • Download All Resources and Important FAQ
The Field of Data Science - The Various Data Science Disciplines
  • Data Science and Business Buzzwords: Why are there so Many?
  • Data Science and Business Buzzwords: Why are there so Many?
  • What is the difference between Analysis and Analytics
  • What is the difference between Analysis and Analytics
  • Business Analytics, Data Analytics, and Data Science: An Introduction
  • Business Analytics, Data Analytics, and Data Science: An Introduction
  • Continuing with BI, ML, and AI
  • Continuing with BI, ML, and AI
  • A Breakdown of our Data Science Infographic
  • A Breakdown of our Data Science Infographic
The Field of Data Science - Connecting the Data Science Disciplines
  • Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
  • Applying Traditional Data, Big Data, BI, Traditional Data Science and ML
The Field of Data Science - The Benefits of Each Discipline
  • The Reason Behind These Disciplines
  • The Reason Behind These Disciplines
The Field of Data Science - Popular Data Science Techniques
  • Techniques for Working with Traditional Data
  • Techniques for Working with Traditional Data
  • Real Life Examples of Traditional Data
  • Techniques for Working with Big Data
  • Techniques for Working with Big Data
  • Real Life Examples of Big Data
  • Business Intelligence (BI) Techniques
  • Business Intelligence (BI) Techniques
  • Real Life Examples of Business Intelligence (BI)
  • Techniques for Working with Traditional Methods
  • Techniques for Working with Traditional Methods
  • Real Life Examples of Traditional Methods
  • Machine Learning (ML) Techniques
  • Machine Learning (ML) Techniques
  • Types of Machine Learning
  • Types of Machine Learning
  • Real Life Examples of Machine Learning (ML)
  • Real Life Examples of Machine Learning (ML)
The Field of Data Science - Popular Data Science Tools
  • Necessary Programming Languages and Software Used in Data Science
  • Necessary Programming Languages and Software Used in Data Science
The Field of Data Science - Careers in Data Science
  • Finding the Job - What to Expect and What to Look for
  • Finding the Job - What to Expect and What to Look for
The Field of Data Science - Debunking Common Misconceptions
  • Debunking Common Misconceptions
  • Debunking Common Misconceptions
Part 2: Probability
  • The Basic Probability Formula
  • The Basic Probability Formula
  • Computing Expected Values
  • Computing Expected Values
  • Frequency
  • Frequency
  • Events and Their Complements
  • Events and Their Complements
Probability - Combinatorics
  • Fundamentals of Combinatorics
  • Fundamentals of Combinatorics
  • Permutations and How to Use Them
  • Permutations and How to Use Them
  • Simple Operations with Factorials
  • Simple Operations with Factorials
  • Solving Variations with Repetition
  • Solving Variations with Repetition
  • Solving Variations without Repetition
  • Solving Variations without Repetition
  • Solving Combinations
  • Solving Combinations
  • Symmetry of Combinations
  • Symmetry of Combinations
  • Solving Combinations with Separate Sample Spaces
  • Solving Combinations with Separate Sample Spaces
  • Combinatorics in Real-Life: The Lottery
  • Combinatorics in Real-Life: The Lottery
  • A Recap of Combinatorics
  • A Practical Example of Combinatorics
Probability - Bayesian Inference
  • Sets and Events
  • Sets and Events
  • Ways Sets Can Interact
  • Ways Sets Can Interact
  • Intersection of Sets
  • Intersection of Sets
  • Union of Sets
  • Union of Sets
  • Mutually Exclusive Sets
  • Mutually Exclusive Sets
  • Dependence and Independence of Sets
  • Dependence and Independence of Sets
  • The Conditional Probability Formula
  • The Conditional Probability Formula
  • The Law of Total Probability
  • The Additive Rule
  • The Additive Rule
  • The Multiplication Law
  • The Multiplication Law
  • Bayes' Law