Part 1: Introduction
  • What Does the Course Cover
  • Download All Resources
Intro to Data and Data Science - The Different Data Science Fields
  • Why Are There So Many Business and Data Science Buzzwords?
  • Analysis vs Analytics
  • Intro to Business Analytics, Data Analytics, and Data Science
  • Intro to Business Analytics, Data Analytics, and Data Science
  • Adding Business Intelligence (BI), Machine Learning (ML), and AI to the Picture
  • Adding Business Intelligence (BI), Machine Learning (ML), and AI to the Picture
  • An Overview of our Data Science Infographic
  • An Overview of our Data Science Infographic
Intro to Data and Data Science - The Relationship between Different Fields
  • When are Traditional data, Big Data, BI, Traditional Data Science and ML applied
  • When are Traditional data, Big Data, BI, Traditional Data Science and ML applied
Intro to Data and Data Science - What is the Purpose of each Data Science Field
  • Why do we Need each of these Disciplines?
  • Why do we Need each of these Disciplines?
Intro to Data and Data Science - Common Data Science Techniques
  • Traditional Data: Techniques
  • Traditional Data: Techniques
  • Traditional Data: Real-life Examples
  • Big Data: Techniques
  • Big Data: Techniques
  • Big Data: Real-life Examples
  • Business Intelligence (BI): Techniques
  • Business Intelligence (BI): Techniques
  • Business Intelligence (BI): Real-life Examples
  • Traditional Methods: Techniques
  • Traditional Methods: Techniques
  • Traditional Methods: Real-life Examples
  • Machine Learning (ML): Techniques
  • Machine Learning (ML): Techniques
  • Machine Learning (ML): Types of Machine Learning
  • Machine Learning (ML): Types of Machine Learning
  • Machine Learning (ML): Real-life Examples
  • Machine Learning (ML): Real-life Examples
Intro to Data and Data Science - Common Data Science Tools
  • Programming Languages & Software Employed in Data Science - All the Tools Needed
  • Programming Languages & Software Employed in Data Science - All the Tools Needed
Intro to Data and Data Science - Data Science Career Paths
  • Data Science Job Positions: What do they Involve and What to Look out for?
  • Data Science Job Positions: What do they Involve and What to Look out for?
Intro to Data and Data Science - Dispelling Common Misconceptions
  • Dispelling common Misconceptions
  • Dispelling common Misconceptions
Part 2: Statistics - Population and Sample
  • Population vs sample
  • Population and Sample
Statistics - Descriptive Statistics
  • Types of Data
  • Types of data
  • Levels of Measurement
  • Levels of measurement
  • Categorical Variables - Visualization Techniques
  • Categorical variables. Visualization Techniques
  • Categorical Variables Exercise
  • Numerical Variables - Frequency Distribution Table
  • Numerical variables. Using a frequency distribution table
  • Numerical Variables Exercise
  • The Histogram
  • The Histogram
  • Histogram Exercise
  • Cross Table and Scatter Plot
  • Cross Tables and Scatter Plots
  • Cross Tables and Scatter Plots Exercise
  • Mean, median and mode
  • Mean, Median and Mode Exercise
  • Skewness
  • Skewness
  • Skewness Exercise
  • Variance
  • Variance Exercise
  • Standard Deviation and Coefficient of Variation
  • Standard deviation
  • Standard Deviation and Coefficient of Variation Exercise
  • Covariance
  • Covariance
  • Covariance Exercise
  • Correlation Coefficient
  • Correlation Coefficient
  • Correlation Coefficient Exercise
Statistics - Practical Example: Descriptive Statistics
  • Practical Example
  • Practical Example Exercise
Statistics - Inferential Statistics Fundamentals
  • Introduction
  • What is a Distribution
  • What is a Distribution
  • The Normal Distribution
  • The Normal Distribution
  • The Standard Normal Distribution
  • The Standard Normal Distribution
  • The Standard Normal Distribution Exercise
  • Central Limit Theorem
  • Central Limit Theorem
  • Standard error
  • Standard error
  • Estimators and Estimates
  • Estimators and Estimates