7 FREE Math Courses on Udacity for Data Science [2024]

Are you looking for FREE Math Courses for Data Science? If yes, then this article is for you. In this article, you will find the 7 FREE Math Courses on Udacity for Data Science. These free courses will help you to learn statistics, probability, linear algebra, and calculus.

All courses are completely free.

So give a few minutes and find out the FREE Math Courses on Udacity for Data Science. Now without any further ado, let’s get started-

FREE Math Courses on Udacity for Data Science

1. Linear Algebra Refresher Course

Time to Complete-	4 Months
Best For-	Intermediate

This is a Free refresher course to learn the basics of linear algebra. In this course, there are four chapters. In the first chapter, you will understand the points and vectors.

In the next chapter/lesson, the instructor will explain the vector module, operations on vectors, and how to implement addition, subtraction, and scalar multiplication functions in a language of your choice. You will also learn about inner products, Parallel and Orthogonal Vectors, Projecting Vectors, Cross Products, etc.

The third chapter is all about intersections, where you will learn Lines in Two Dimensions, Planes in 3 Dimensions, Intersections of Planes in 3D, Gaussian Elimination Practice, etc. There are various quizzes and exercises in this chapter for practice.

In the last lesson, you will learn Matrices and the Transformation of State, Kalman Prediction, Matrices in Python, and other mathematical concepts related to linear algebra.

Overall, this is a good course to understand linear algebra for free.

You Should Enroll if-

• You have experience with some programming languages.

Interested to Enroll?

If yes, then start learning- Linear Algebra Refresher Course

2. Intro to Statistics

Time to Complete-	2 Months
Best For-	Beginner

This Intro to Statistics is a completely FREE course for beginners. This course is taught by Udacity Co-Founder, Sebastian Thrun. The course structure is interesting and fun for beginners. The course begins with a teaser where Sebastian Thrun gives a challenging teaser.

Throughout the course, you will learn the statistics basics and understand charts, plots, and probability basics.

Along with that, you will also learn Central Limit Theorem, Normal Distribution, Confidence Interval, Hypothesis Test, Regression, etc. This course has 6 problem sets and various quizzes related to statistics.

At the end of this course, there is one final exam, where there are 16 questions. And you have to answer these questions by yourself.

You Should Enroll if-

• You are a beginner, but it’s good if you have already heard of some easy statistical concepts.

Interested to Enroll?

If yes, then start learning- Intro to Statistics

3. Intro to Inferential Statistics

Time to Complete-	2 Months
Best For-	Intermediate

This Intro to Inferential Statistics is the third Free statistics course at Udacity. This course is the next follow-up course to “Intro to Descriptive Statistics“. If you have not previously watched the descriptive statistics course, first watch the Intro to Descriptive Statistics course.

The course begins with an explanation of Klout Sampling Distribution (Mean), Klout Sampling Distribution (SD), Sampling Distribution Shape, Probability of Obtaining Mean, etc.

Along with that, you will learn Hypothesis Testing, t-Tests, ANOVA, Correlation, and Regression. There are also quizzes and exercises throughout the course to test your understanding.

Overall, this is an in-depth course and the instructor uses a visual representation to teach the concepts of statistics.

You Should Enroll if-

• You have a basic understanding of Descriptive Statistics.

Interested to Enroll?

If yes, then start learning- Intro to Inferential Statistics.

4.  Intro to Descriptive Statistics

Time to Complete-	2 Months
Best For-	Beginner

This Intro to Descriptive Statistics is a complete Free course for statistics. The course begins with an intro to Research Methods, where you will understand how to Measure Memory and Define Constructs. The first lesson is very insightful where you will get to know about the Golden Arches Theory of Conflict Prevention.

Throughout the course, there are various MCQ questions, which you have to answer.

After that, you will learn how to visualize the data and work on a problem set. This course also covers Skewed Distribution.

The last part of this course covers Variability, Standardizing, and Distributions. Along with these courses, there are problem sets associated. There is a separate lesson on Google Spreadsheet Tutorial.

Overall, this is a good course to learn and practice Descriptive Statistics. The course uses real-world examples to clear the concepts.

You Should Enroll if-

• You have an understanding of basic algebra and arithmetic.

Interested to Enroll?

If yes, then start learning- Intro to Descriptive Statistics

5. Eigenvectors and Eigenvalues

Time to Complete-	1 week
Best For-	Beginner

This is a very short course, where you will learn Linear Transformation, Eigenvalues, Eigenvectors, and Principle Component Analysis.

Throughout the course, there are some quizzes to test your understanding. To understand the concept of eigenvalues and eigenvectors, the instructor jumped into a 3-D space.

This is not a very detailed course but after this course, you will understand how to calculate eigenvectors and eigenvalues. You also know how to visualize them graphically and why they are so useful

You Should Enroll if-

• You have a previous mathematical background in Linear Algebra.

Interested to Enroll?

If yes, then start learning-Eigenvectors and Eigenvalues

6. Intro to Artificial Intelligence

Time to Complete-	4 months
Best For-	Intermediate

The name of this course is Introduction to AI, but this course is very in-depth. Sebastian Thrun is the instructor of this course.

The course begins with the basics of AI and its applications. Then he explains Problem Solving and gives an example of route finding. In this course, you will learn various search algorithms such as Tree Search, Graph Search, Breadth First Search, A* Search, etc.

The instructor also explains machine learning algorithms such as Unsupervised learning and Reinforcement Learning. Throughout this course, there are different problem sets and quizzes. Most of the tutorials are in video formats.

At the end of this course, the instructor explains the constraint satisfaction problem. The Suduko Solver Project is in this course.

Who Should Enroll?

• Those who have an understanding of probability theory.

Interested to Enroll?

If yes, then start learning here- Intro to Artificial Intelligence.

7. Differential Equations in Action

Time to Complete-	2 months
Best For-	Intermediate

There are 7 lessons in this course. In this course, you will learn how to find numerical solutions to systems of differential equations.

Throughout the course, there are different problem sets and you will write code in Python to fight forest fires, rescue the Apollo 13 astronauts, stop the spread of epidemics, and resolve other real-world dilemmas.

Overall, this is a good course that will teach you how to translate mathematical expressions into Python code and solve some really cool problems.

You Should Enroll if-

• You have a basic knowledge of programming in Python.

Interested to Enroll?

If yes, then start learning- Differential Equations in Action

That’s all!

These are the 7 FREE Math Courses on Udacity for Data Science. Now, it’s time to wrap up.

Conclusion

I hope these 7 FREE Math Courses on Udacity for Data Science will help you to learn the concepts of statistics, probability, linear algebra, and calculus. My aim is to provide you with the best resources for Learning. If you have any doubts or questions, feel free to ask me in the comment section.

All the Best!

Happy Learning!

Thank YOU!

Explore More about Data Science, Visit Here

Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden