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