Master Math by Coding in Python

Use Python to learn algebra, calculus, graphing, trigonometry and more math topics!

What you’ll learn

Most important: Confidence in learning math!

Arithmetic

Algebra (1, 2)

Graphing

Trigonometry

Calculus

Linear algebra

Python programming

Formatting beautiful equations in LaTeX

Data visualization

Integrating Python, Markdown, and LaTeX

Requirements

Just the course, a computer, and a positive attitude!

No Python experience necessary – I take you through everything!

Jupyter IPython notebook – free to use! Either local installation or use online

Description

You can learn a lot of math with a bit of coding!

Many people don’t know that Python is a really powerful tool for learning math. Sure, you can use Python as a simple calculator, but did you know that Python can help you learn more advanced topics in algebra, calculus, and matrix analysis? That’s exactly what you’ll learn in this course.

This course is a perfect supplement to your school/university math course, or for your post-school return to mathematics.

Let me guess what you are thinking:

“But I don’t know Python!” That’s okay! This course is aimed at complete beginners; I take you through every step of the code. You don’t need to know anything about Python, although it’s useful if you already have some programming experience.

“But I’m not good at math!” You will be amazed at how much better you can learn math by using Python as a tool to help with your courses or your independent study. And that’s exactly the point of this course: Python programming as a tool to learn mathematics. This course is designed to be the perfect addition to any other math course or textbook that you are going through.

What do you get in this course?

Over 33 hours of instruction that includes Python coding, visualization, loops, variables, and functions.

LOTS of practical exercises! Each video has at least one hands-on coding/math exercise (and you’ll get to watch me solve those exercises). And each section ends with “bug hunts” where you get to find and fix my math-coding errors!

That warm, fuzzy feeling of confidence that you can combine the skills from this course to improve your understanding of mathematics.

A big-picture overview of beginner and advanced mathematics, from solving for “x” to computing integrals to finding eigenvalues. If you are only just beginning your adventures in maths, then this course will show you what you have to look forward to!

This course is right for you if you are:

In middle/high school, university, or are returning to math as an independent learner.

A data professional who wants to brush up on math and Python skills.

A complete beginner to Python.

Already proficient with math “in theory” and want to learn how to translate math formulas and concepts into computer code.

Bored and looking for a fun intellectual challenge.

With over 33 hours of teaching, plus student exercises, challenges and an active course Q&A forum (get a response to any question within 48 hours!), this course gives you everything you need to succeed in your maths course or independent adventures in learning math.

All the code that appears in the videos is also included for download. You can code along as you watch the videos, or download the code and use it directly.

This course covers the following topics:

Arithmetic

Introduction to Sympy

Introduction to LaTeX (to print beautiful equations!)

Algebra 1

Graphing

Algebra 2

Graphing conic sections

Trigonometry

Calculus

Linear algebra

…and more!

Who is your teacher?

I am Mike X Cohen, an associate professor at the Radboud University (the Netherlands). I’m a bestselling and highly rated instructor on Udemy. I’ve taught over 73,000 students the foundations of scientific programming, data analysis, and applied mathematics, and I’ve written several textbooks on programming and data analyses.

I worked really hard to make this course a great learning experience for you. Check out what some of my students have said about my other courses:

***** ‘Best teacher ever. I am a psychologist and I didn’t have mathematical training as an undergrad, but the books and lectures of Dr. Cohen have been life saving’

***** ‘What I REALLY like about Mike’s style is that not only clear and direct, but he mixes in appropriate amounts of foreshadowing … to make it easier for me to connect the dots.’

***** ‘Mike X Cohen’s courses are by far the best ones I’ve done in Udemy.’

What you should do right now:

Watch the free preview videos.

Check out the reviews of this course.

Joining this course is risk-free: If you change your mind after enrolling, Udemy offer a 30 day money back guarantee, and you can find full details here: https://support.udemy.com

Who this course is for:

Maths students looking to use computers as a learning tool

Developers keen to improve their math skills

Independent learners returning to maths

Programmers who want to use their coding skills to explore mathematics

Course content

15 sections • 175 lectures • 37h 9m total length

Preview

05:57

Using Python through Jupyter (installing Anaconda)

08:04

Using Python online (no installation!)

08:41

Preview

07:02

Getting help in Python

09:02

(optional) Entering time-stamped notes in the Udemy video player

01:52

Python code for this section

00:02

Addition, subtraction, multiplication, division

08:56

Using variables in place of numbers

11:38

Printing out equations in Jupyter notebook

23:21

Writing comments in Python

04:44

Exponents (powers)

17:09

Using for-loops to compute powers

15:47

Preview

14:48

Testing inequalities and Boolean data type

13:55

Using if-statements and logical operators

17:27

Absolute value

13:38

Remainder after division (modulus)

15:25

Create interactive math functions, part 1

13:01

Create interactive math functions, part 2

17:26

Create interactive math functions, part 3

14:05

Arithmetic bug hunt!

16:36

Python code for this section

00:03

Intro to Sympy, part 1

13:12

Intro to LaTeX

20:23

Intro to Sympy, part 2

19:51

Printing with f-strings

08:23

Example: Use Sympy to understand the law of exponents

14:59

Sympy/Latex bug hunt!

13:49

Python codes for this section

00:00

Numbers and strings

16:42

Lists and numpy arrays

22:35

Python code for this section

00:03

Solving for x

15:39

Solving for x: exercises

17:23

Expanding terms

16:22

Creating and accessing matrices with numpy

15:48

Exercise: Create a multiplication table

11:14

Associative, commutative, and distributive properties

15:18

Creating and working with Python lists

17:28

More on “slicing” in Python

09:33

Greatest common denominator

10:19

Greatest common denominator: exercises

09:56

Introduction to Python dictionaries

13:07

Prime factorization

12:15

Solving inequalities

13:47

Preview

17:56

Multiplying polynomials

13:08

Dividing by polynomials

16:03

Factoring polynomials

12:57

Algebra 1 bug hunt!

13:02

Python code for this section

00:02

Plotting coordinates on a plane

13:12

Plotting coordinates on a plane: exercise

04:27

Graphing lines part 1: start/end notation

16:18

Graphing lines part 2: slope-intercept form

16:26

Preview

15:16

Plotting with Sympy

18:03

Plotting with Sympy: exercises

11:58

Course tangent: self-accountability in online learning

03:03

Making images from matrices

16:30

Images from matrices: exercise

07:06

Drawing patches with polygons

18:43

Exporting graphics as pictures

03:45

Graphing bug hunt!

18:44

Python code for this section

00:02

Summation and products

17:12

Differences (discrete derivative)

17:27

Roots of polynomials

11:26

Roots of polynomials: exercise

07:25

The quadratic equation

21:01

Complex numbers: addition and subtraction

15:33

Complex numbers: conjugate and multiplication

13:30

Complex numbers: division

16:14

Graphing complex numbers

10:58

Revisiting the quadratic equation with complex numbers

08:51

The unit circle

13:48

Natural exponent and logarithm

11:27

Find a specific point on a Gaussian

16:17

Exercise: A family of Gaussians

07:47

Preview

18:27

Log-spaced and linearly spaced numbers

09:25

Logarithm properties: Multiplication and division

16:11

Arithmetic and geometric sequences

15:57

Orders of magnitude and scientific notation

20:09

Maxima and minima of functions

16:43

Even and odd functions

11:56

Algebra 2 bug hunt!

20:22

Python code for this section

00:03

Graphing parabolas

14:36

Creating contours from meshes in Python

14:53

Graphing circles

17:41

Graphing ellipses

15:28

Graphing hyperbolas

15:32

Conic bug hunt!

05:49

Python code for this section

00:02

Introduction to random numbers

12:31

Introduction to random numbers: exercise

10:37

Exercise: Plotting random phase angles

06:32

Converting between radians and degrees

09:04

Converting angles: exercise

16:45

The Pythagorean theorem

17:52

Graphing resolution for sine, cosine, and tangent

13:11

Graphing and resolution: Exercise

16:30

Euler’s formula

12:48

Euler’s formula: exercise

11:50

Exercise: random exploding Euler

08:06

Preview

11:09

Trigonometry bug hunt!

12:23

Python code for this section

00:02

Astroid radial curve

16:30

Rose curves

12:24

Squircle

09:19

Logarithmic spiral

11:15

Logistic map

21:41

Python code for this section

00:02

Mathematical proofs vs. intuition with examples

03:44

Computing limits of a function

15:02

Computing limits: exercise

13:18

Piecewise functions

16:29

Derivatives of polynomials

14:32

Derivatives of polynomials: exercise

09:49

Preview

12:15

Derivatives of trig functions: exercise

08:28

Graphing a function tangent line

14:12

Preview

12:54

Finding critical points

17:03

Finding critical points: exercise

15:09

Partial derivatives

11:13

Indefinite and definite integrals

15:52

Exercise: The fundamental theorem of calculus

04:46

Area between two curves

12:41

Area between two curves: exercise

14:40

Calculus bug hunt!

15:29

Python code for this section

00:03

Row and column vectors

17:17

Adding and scalar-multiplying vectors

17:16

The dot product

16:01

Dot product application: Correlation coefficient

12:26

The outer product

10:31

Matrix multiplication

17:07

Transposing vectors and matrices

14:26

Various special matrices

18:25

Matrix inverse

11:16

Matrix pseudoinverse: exercise

10:09

Solving a system of equations

19:36

Visualizing matrix-vector multiplication

14:14

Eigenvalues and eigenvectors

14:55

Eigendecomposition: Exercise

12:44

Singular value decomposition

12:57

SVD of Einstein: exercise

12:57

Linear algebra BUG HUNT!

20:13

Python codes for this section

00:01

Histograms and probability densities

13:46

Probability exercise: math functions

11:49

Virtual coin tosses

13:28

Exercise: Virtual weighted dice

15:26

Building distributions from random numbers

18:21

Exercise: Normalize any distribution to Gaussian

12:31

The central limit theorem

14:54

Exercise: the central limit theorem

11:49

Joint probability distributions

15:31

Probability bug hunt!

10:11

Python codes for this section

00:02

Counting perfect numbers

24:19

Euclid’s Pythagorean triplets

18:47

Fermat’s theorem

17:34

Plotting number sequences

15:36

Exercise: con/divergent sequences

13:07

Heron’s method of square roots

23:47

Exercise: Heron’s mosquito spaceship #13

15:26

Smooth numbers

22:19

Exercise: Smooth numbers

08:56

Number theory bug hunt!

14:31