Math for Data Science Masterclass Free Download

Learn about probability, statistics, and more using mathematics that is foundational to the field of data science. Learn about how to measure data with statistics.

Math for Data Science Course content

11 sections • 68 lectures • 16h 7m total length

Introduction2 lectures • 5min

  • Welcome to the Course! Important Info in this Lecture!00:42
  • Course Overview and CurriculumPreview04:10

Core Data Concepts4 lectures • 1hr 22min

  • Introduction to Core Data ConceptsPreview16:03
  • Measurements of Central Tendency – Mean, Median, and Mode26:33
  • Check-In Quiz: Central Tendency1 question
  • Measurements of Dispersion – Variance and Standard Deviation20:33
  • Quartiles and IQR18:28

Visualizing Data8 lectures • 1hr 47min

  • Introduction to Visualizing Data20:38
  • Scatter Plots21:35
  • Line Plots10:19
  • Distribution Plots – Histograms13:21
  • Categorical Plots – Bar Plots08:06
  • Categorical/Distribution Plots – Box and Whisker Plots08:00
  • Other Plot Types – Violin Plot, KDE Plot12:49
  • Common Plot Pitfalls11:55

Combinatorics5 lectures • 54min

  • Introduction to CombinatoricsPreview12:19
  • Factorials16:24
  • Permutations10:50
  • Combinations14:47
  • Combinatorics Practice Problem Set and Answers00:03

Probability9 lectures • 2hr 40min

  • Introduction to Probability23:01
  • Probability, Law of Large Numbers, Experimental vs. ExpectedPreview21:05
  • The Addition Rule, Union and Intersection, Venn Diagrams18:10
  • Conditional Probability, Independent and Dependent15:50
  • Bayes’ Theorem13:15
  • Discrete Probability26:02
  • Transforming Random Variables26:06
  • Combinations of Random Variables16:18
  • Probability Practice Problem Set and Answers00:04

Joint Distributions4 lectures • 56min

  • Introduction to Joint Distributions20:54
  • Covariance19:15
  • Pearson Correlation Coefficient15:48
  • Joint Distribution Practice Problem Set and Answers00:03

Data Distributions10 lectures • 2hr 9min

  • Introduction to Data Distributions15:56
  • Probability Mass Functions14:14
  • Discrete Uniform Distribution – Dice Roll06:45
  • Probability Density Functions18:51
  • Continuous Uniform Distribution – Voltage11:11
  • Cumulative Distribution Functions12:48
  • Binomial Distribution21:30
  • Bernoulli Distribution13:47
  • Poisson Distribution13:41
  • Data Distributions Practice Problem Set and Answers00:03

The Normal Distribution6 lectures • 1hr 15min

  • Introduction to The Normal Distribution15:58
  • Mean, Variance, and Standard Deviation16:00
  • Normal Distribution15:29
  • Standard Normal Distribution08:00
  • Z-Scores19:49
  • Normal Distribution Practice Problem Set and Answers00:03

Sampling6 lectures • 1hr 35min

  • Introduction to Sampling14:29
  • Sampling and Bias27:16
  • The Central Limit Theorem21:33
  • The Student’s t-Distribution11:01
  • Confidence Interval for the Mean20:31
  • Sampling Practice Problem Set and Answers00:03

Hypothesis Testing7 lectures • 1hr 42min

  • Introduction to Hypothesis Testing16:17
  • Inferential Statistics and Hypotheses11:04
  • Significance Level and Type I and II Errors17:14
  • Test Statistics for One- and Two-Tailed Tests14:28
  • The p-Value and Rejecting the Null21:49
  • A|B Testing21:01
  • Hypothesis Testing Practice Problem Set and Answers00:03

Regression7 lectures • 1hr 43min

  • Introduction to Regression12:58
  • Scatterplots and Regression08:17
  • Correlation Coefficient and the Residual22:45
  • Coefficient of Determination and the RMSE17:08
  • Chi-Square Tests16:53
  • ANOVA24:49
  • Regression Practice Problem Set and Answers00:03


  • Only basic arithmetic skills are needed, we’ll teach you the rest


Welcome to the best online course for learning about the Math behind the field of Data Science!

Working together for the first time ever, Krista King and Jose Portilla have combined forces to deliver you a best in class course experience in how to use mathematics to solve real world data science problems. This course has been specifically designed to help you understand the mathematical concepts behind the field of data science, so you can have a first principles level understanding of how to use data effectively in an organization.

Often students entering the field of data science are confused on where to start to learn about the fundamental math behind the concepts. This course was specifically designed to help bridge that gap and provide students a clear, guided path through the complex and interesting world of math used in the field of data science. Designed to balance theory and application, this is the ultimate learning experience for anyone wanting to really understand data science.

Why choose this course?

Combined together, Krista and Jose have taught over 3.2 million students about data science and mathematics and their joint expertise means you’ll be able to get the best and clearest mathematical explanations from Krista with framing about real world data science applications from Jose.  At the end of each section is a set of practice problems developed from real-world company situations, where you can directly apply what you know to test your understanding.

What’s covered in this course?

In this course, we’ll cover:

  • Understanding Data Concepts
  • Measurements of Dispersion and Central Tendency
  • Different ways to visualize data
  • Permutations
  • Combinatorics
  • Bayes’ Theorem
  • Random Variables
  • Joint Distributions
  • Covariance and Correlation
  • Probability Mass and Density Functions
  • Binomial, Bernoulli, and Poisson Distributions
  • Normal Distribution and Z-Scores
  • Sampling and Bias
  • Central Limit Theorem
  • Hypothesis Testing
  • Linear Regression
  • and much more!

Enroll today and we’ll see you inside the course!

Krista and Jose

Who this course is for:

  • Anyone interested in learning more about the mathematics behind data science

Created by Jose Portilla, Krista King
Last updated 11/2022
English [Auto]

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