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30-Day Money-Back Guarantee
Teaching & Academics Math

Master Math by Coding in Python

Use Python to learn algebra, calculus, graphing, trigonometry and more math topics!
Rating: 4.7 out of 54.7 (1,153 ratings)
10,415 students
Created by Codestars by Rob Percival, Mike X Cohen
Last updated 2/2021
English
English [Auto]
30-Day Money-Back Guarantee

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 31 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

Featured review

Michael McLean
Michael McLean
2212 courses
96 reviews
Rating: 5.0 out of 5a year ago
Mike is a great Instructor. He is very enthusiastic and gives off lots of engergy. All the concepts are explained well. This is a great course if you are learning python and/or you want to learn Data science and/or you want to brush up on your maths at the same time. For me its perfect. Thank you.

Course content

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

  • Preview05:57
  • Using Python through Jupyter (installing Anaconda)
    08:04
  • Using Python online (no installation!)
    08:41
  • Preview07:02
  • 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
  • Preview14:48
  • 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
  • Preview17:56
  • 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
  • Preview15:16
  • 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
  • Preview18:27
  • 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
  • Preview11:09
  • 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

Instructors

Codestars by Rob Percival
Teaching the Next Generation of Coders
Codestars by Rob Percival
  • 4.5 Instructor Rating
  • 381,664 Reviews
  • 1,779,667 Students
  • 62 Courses

Best-selling Udemy instructor Rob Percival wants to revolutionize the way people learn to code by making it simple, logical, fun and, above all, accessible.  But as just one man, Rob couldn’t create all the courses his students - more than half a million of them - wanted.  

That’s why Rob created Codestars.  Together, the instructors that make up the Codestars team create courses on all the topics that students want to learn in the way that students want to learn them: courses that are well-structured, super interactive, and easy to understand.  Codestars wants to make it as easy as possible for learners of all ages and levels to build functional websites and apps.

Mike X Cohen
Neuroscientist, writer, professor
Mike X Cohen
  • 4.6 Instructor Rating
  • 21,141 Reviews
  • 106,181 Students
  • 20 Courses

I am a neuroscientist (brain scientist) and associate professor at the Radboud University in the Netherlands. I have an active research lab that has been funded by the US, German, and Dutch governments, European Union, hospitals, and private organizations.

But you're here because of my teaching, so let me tell you about that: 

I have 20 years of experience teaching programming, data analysis, signal processing, statistics, linear algebra, and experiment design. I've taught undergraduate students, PhD candidates, postdoctoral researchers, and full professors. I teach in "traditional" university courses, special week-long intensive courses, and Nobel prize-winning research labs. I have >80 hours of online lectures on neuroscience data analysis that you can find on my website and youtube channel. And I've written several technical books about these topics with a few more on the way.

I'm not trying to show off -- I'm trying to convince you that you've come to the right place to maximize your learning from an instructor who has spent two decades refining and perfecting his teaching style.

Over 94,000 students have watched over 6,500,000 minutes of my courses (that's over 12 years of continuous learning). Come find out why!

I have several free courses that you can enroll in. Try them out! You got nothing to lose ;)

                                                  -------------------------

By popular request, here are suggested course progressions for various educational goals:

MATLAB programming: MATLAB onramp; Master MATLAB; Image Processing

Python programming: Master Python programming by solving scientific projects; Master Math by Coding in Python

Applied linear algebra: Complete Linear Algebra; Dimension Reduction

Signal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

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