Statistics & Mathematics for Data Science & Data Analytics

Course Curriculum

Chapter 1: Let's get started

Lecture 1: Welcome!

Lecture 2: What will you learn in this course?

Lecture 3: How can you get the most out of it?

Lecture 4: Download: Formula cheat sheet

Chapter 2: Descriptive statistics

Lecture 1: Intro

Lecture 2: Mean

Lecture 3: Median

Lecture 4: Mode

Lecture 5: Mean or Median?

Lecture 6: Skewness

Lecture 7: Practice: Skewness

Lecture 8: Solution: Skewness

Lecture 9: Range & IQR

Lecture 10: Sample vs. Population

Lecture 11: Variance & Standard deviation

Lecture 12: Impact of Scaling & Shifting

Lecture 13: Statistical moments

Chapter 3: Distributions

Lecture 1: What is a distribution?

Lecture 2: Normal distribution

Lecture 3: Z-Scores

Lecture 4: Practise: Normal distribution

Lecture 5: Solution: Normal distribution

Lecture 6: More distributions

Chapter 4: Probability theory

Lecture 1: Intro

Lecture 2: Probability Basics

Lecture 3: Calculating Simple Probabilities

Lecture 4: Practice: Simple Probabilities

Lecture 5: Quick solution: Simple Probabilites

Lecture 6: Detailed solution: Simple Probabilities

Lecture 7: Rule of addition

Lecture 8: Practice: Rule of addition

Lecture 9: Quick solution: Rule of addition

Lecture 10: Detailed solution: Rule of addition

Lecture 11: Rule of multiplication

Lecture 12: Practice: Rule of multiplication

Lecture 13: Solution: Rule of multiplication

Lecture 14: Bayes Theorem

Lecture 15: Bayes Theorem – Practical example

Lecture 16: Expected value

Lecture 17: Practice: Expected value

Lecture 18: Solution: Expected value

Lecture 19: Law of Large Numbers

Lecture 20: Central Limit Theorem – Theory

Lecture 21: Central Limit Theorem – Intuition

Lecture 22: Central Limit Theorem – Challenge

Lecture 23: Central Limit Theorem – Exercise

Lecture 24: Central Limit Theorem – Solution

Lecture 25: Binomial distribution

Lecture 26: Poisson distribtuion

Lecture 27: Real life problems

Chapter 5: Hypothesis testing

Lecture 1: Intro

Lecture 2: What is an hypothesis?

Lecture 3: Significance level and p-value

Lecture 4: Type I and Type II errors

Lecture 5: Confidence intervals and margin of error

Lecture 6: Excursion: Calculating sample size & power

Lecture 7: Performing the hypothesis test

Lecture 8: Practice: Hypothesis test

Lecture 9: Solution: Hypothesis test

Lecture 10: t-test and t-distribution

Lecture 11: Proportion testing

Lecture 12: Important p-z pairs

Chapter 6: Regressions

Lecture 1: Intro

Lecture 2: Linear Regression

Lecture 3: Correlation coefficient

Lecture 4: Practice: Correlation

Lecture 5: Solution: Correlation

Lecture 6: Practice: Linear Regression

Lecture 7: Solution: Linear Regression

Lecture 8: Residual, MSE & MAE

Lecture 9: Practice: MSE & MAE

Lecture 10: Solution: MSE & MAE

Lecture 11: Coefficient of determination

Lecture 12: Root Mean Square Error

Lecture 13: Practice: RMSE

Lecture 14: Solution: RMSE

Chapter 7: Advanced regression & machine learning algorithms

Lecture 1: Multiple Linear Regression

Lecture 2: Overfitting

Lecture 3: Polynomial Regression

Lecture 4: Logistic Regression

Lecture 5: Decision Trees

Lecture 6: Regression Trees

Lecture 7: Random Forests

Lecture 8: Dealing with missing data

Chapter 8: ANOVA (Analysis of Variance)