Math for Data Science Masterclass

Math for Data Science Masterclass, available at $94.99, has an average rating of 4.69, with 68 lectures, 7 quizzes, based on 1150 reviews, and has 13777 subscribers.

You will learn about Understand core concepts about data quality and quantity Learn about how to measure data with statistics Discover how to visualize data with a variety of plot types Use combinatorics to calculate permutations and combinations of objects Understand the key ideas in using probability to solve problems Learn how to use data distributions with real world data Discover the powerful insights from the normal distribution Use sampling and the central limit theorem Understand hypothesis testing on sample groups Cover the basics of linear regression This course is ideal for individuals who are Anyone interested in learning more about the mathematics behind data science It is particularly useful for Anyone interested in learning more about the mathematics behind data science.

Enroll now: Math for Data Science Masterclass

Summary

Title: Math for Data Science Masterclass

Price: $94.99

Average Rating: 4.69

Number of Lectures: 68

Number of Quizzes: 7

Number of Published Lectures: 68

Number of Published Quizzes: 6

Number of Curriculum Items: 75

Number of Published Curriculum Objects: 74

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Understand core concepts about data quality and quantity
Learn about how to measure data with statistics
Discover how to visualize data with a variety of plot types
Use combinatorics to calculate permutations and combinations of objects
Understand the key ideas in using probability to solve problems
Learn how to use data distributions with real world data
Discover the powerful insights from the normal distribution
Use sampling and the central limit theorem
Understand hypothesis testing on sample groups
Cover the basics of linear regression

Who Should Attend

Anyone interested in learning more about the mathematics behind data science

Target Audiences

Anyone interested in learning more about the mathematics behind data science

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome to the Course! Important Info in this Lecture!

Lecture 2: Course Overview and Curriculum

Chapter 2: Core Data Concepts

Lecture 1: Introduction to Core Data Concepts

Lecture 2: Measurements of Central Tendency – Mean, Median, and Mode

Lecture 3: Measurements of Dispersion – Variance and Standard Deviation

Lecture 4: Quartiles and IQR

Chapter 3: Visualizing Data

Lecture 1: Introduction to Visualizing Data

Lecture 2: Scatter Plots

Lecture 3: Line Plots

Lecture 4: Distribution Plots – Histograms

Lecture 5: Categorical Plots – Bar Plots

Lecture 6: Categorical/Distribution Plots – Box and Whisker Plots

Lecture 7: Other Plot Types – Violin Plot, KDE Plot

Lecture 8: Common Plot Pitfalls

Chapter 4: Combinatorics

Lecture 1: Introduction to Combinatorics

Lecture 2: Factorials

Lecture 3: Permutations

Lecture 4: Combinations

Lecture 5: Combinatorics Practice Problem Set and Answers

Chapter 5: Probability

Lecture 1: Introduction to Probability

Lecture 2: Probability, Law of Large Numbers, Experimental vs. Expected

Lecture 3: The Addition Rule, Union and Intersection, Venn Diagrams

Lecture 4: Conditional Probability, Independent and Dependent

Lecture 5: Bayes' Theorem

Lecture 6: Discrete Probability

Lecture 7: Transforming Random Variables

Lecture 8: Combinations of Random Variables

Lecture 9: Probability Practice Problem Set and Answers

Chapter 6: Joint Distributions

Lecture 1: Introduction to Joint Distributions

Lecture 2: Covariance

Lecture 3: Pearson Correlation Coefficient

Lecture 4: Joint Distribution Practice Problem Set and Answers

Chapter 7: Data Distributions

Lecture 1: Introduction to Data Distributions

Lecture 2: Probability Mass Functions

Lecture 3: Discrete Uniform Distribution – Dice Roll

Lecture 4: Probability Density Functions

Lecture 5: Continuous Uniform Distribution – Voltage

Lecture 6: Cumulative Distribution Functions

Lecture 7: Binomial Distribution

Lecture 8: Bernoulli Distribution

Lecture 9: Poisson Distribution

Lecture 10: Data Distributions Practice Problem Set and Answers

Chapter 8: The Normal Distribution

Lecture 1: Introduction to The Normal Distribution

Lecture 2: Mean, Variance, and Standard Deviation

Lecture 3: Normal Distribution

Lecture 4: Standard Normal Distribution

Lecture 5: Z-Scores

Lecture 6: Normal Distribution Practice Problem Set and Answers

Chapter 9: Sampling

Lecture 1: Introduction to Sampling

Lecture 2: Sampling and Bias

Lecture 3: The Central Limit Theorem

Lecture 4: The Student's t-Distribution

Lecture 5: Confidence Interval for the Mean

Lecture 6: Sampling Practice Problem Set and Answers

Chapter 10: Hypothesis Testing

Lecture 1: Introduction to Hypothesis Testing

Lecture 2: Inferential Statistics and Hypotheses

Lecture 3: Significance Level and Type I and II Errors

Lecture 4: Test Statistics for One- and Two-Tailed Tests

Lecture 5: The p-Value and Rejecting the Null

Lecture 6: A|B Testing

Lecture 7: Hypothesis Testing Practice Problem Set and Answers

Chapter 11: Regression

Lecture 1: Introduction to Regression

Lecture 2: Scatterplots and Regression

Lecture 3: Correlation Coefficient and the Residual

Lecture 4: Coefficient of Determination and the RMSE

Lecture 5: Chi-Square Tests

Lecture 6: ANOVA

Lecture 7: Regression Practice Problem Set and Answers

Instructors

Jose Portilla
Head of Data Science at Pierian Training
Krista King
Your geeky, trusty math tutor
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 1 votes
2 stars: 10 votes
3 stars: 66 votes
4 stars: 324 votes
5 stars: 749 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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