Master Math by Coding in Python

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Mike X Cohen,Codestars • over 2 million students worldwide!

37:08:16

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01 - Introductions and installations

001 (Important) How to get the most out of this course!.mp4

05:57

002 Using Python through Jupyter (installing Anaconda).mp4

08:04

003 Using Python online (no installation!).mp4

08:41

004 Create a beautiful harmonograph!.mp4

07:02

004 MXC-pymath-harmonograph.zip

005 Getting help in Python.mp4

09:02

006 (optional) Entering time-stamped notes in the Udemy video player.mp4

01:52

02 - Arithmetic

001 Python code for this section.html

001 pymath-arithmetic-pythonCodes.zip

002 Addition, subtraction, multiplication, division.mp4

08:56

003 Using variables in place of numbers.mp4

11:38

004 Printing out equations in Jupyter notebook.mp4

23:21

005 Writing comments in Python.mp4

04:44

006 Exponents (powers).mp4

17:10

007 Using for-loops to compute powers.mp4

15:47

008 Order of operations.mp4

14:48

009 Testing inequalities and Boolean data type.mp4

13:55

010 Using if-statements and logical operators.mp4

17:27

011 Absolute value.mp4

13:39

012 Remainder after division (modulus).mp4

15:25

013 Create interactive math functions, part 1.mp4

13:01

014 Create interactive math functions, part 2.mp4

17:26

015 Create interactive math functions, part 3.mp4

14:05

016 Arithmetic bug hunt!.mp4

16:36

03 - Introduction to Sympy and LaTeX

001 Python code for this section.html

001 pymath-sympyLatex-pythonCodes.zip

002 Intro to Sympy, part 1.mp4

13:12

003 Intro to LaTeX.mp4

20:23

004 Intro to Sympy, part 2.mp4

19:51

005 Printing with f-strings.mp4

08:23

006 Example Use Sympy to understand the law of exponents.mp4

14:59

007 SympyLatex bug hunt!.mp4

13:49

04 - Python data types

001 Python codes for this section.html

001 mxc-pymath-datatypes.zip

002 Numbers and strings.mp4

16:42

003 Lists and numpy arrays.mp4

22:36

05 - Algebra 1

001 Python code for this section.html

001 pymath-algebra1-pythonCodes.zip

002 Solving for x.mp4

15:39

003 Solving for x exercises.mp4

17:23

004 Expanding terms.mp4

16:22

005 Creating and accessing matrices with numpy.mp4

15:48

006 Exercise Create a multiplication table.mp4

11:15

007 Associative, commutative, and distributive properties.mp4

15:18

008 Creating and working with Python lists.mp4

17:29

009 More on slicing in Python.mp4

09:33

010 Greatest common denominator.mp4

10:19

011 Greatest common denominator exercises.mp4

09:56

012 Introduction to Python dictionaries.mp4

13:07

013 Prime factorization.mp4

12:15

014 Solving inequalities.mp4

13:47

015 Adding polynomials.mp4

17:56

016 Multiplying polynomials.mp4

13:08

017 Dividing by polynomials.mp4

16:03

018 Factoring polynomials.mp4

12:57

019 Algebra 1 bug hunt!.mp4

13:02

06 - Graphing and visualization

001 Python code for this section.html

001 pymath-graphing-pythonCodes.zip

002 Plotting coordinates on a plane.mp4

13:12

003 Plotting coordinates on a plane exercise.mp4

04:27

004 Graphing lines part 1 startend notation.mp4

16:18

005 Graphing lines part 2 slope-intercept form.mp4

16:26

006 Graphing rational functions.mp4

15:16

007 Plotting with Sympy.mp4

18:03

008 Plotting with Sympy exercises.mp4

11:58

009 Course tangent self-accountability in online learning.mp4

03:04

010 Making images from matrices.mp4

16:30

011 Images from matrices exercise.mp4

07:06

012 Drawing patches with polygons.mp4

18:43

013 Exporting graphics as pictures.mp4

03:45

014 Graphing bug hunt!.mp4

18:44

07 - Algebra 2

001 Python code for this section.html

001 pymath-algebra2-pythonCodes.zip

002 Summation and products.mp4

17:12

003 Differences (discrete derivative).mp4

17:27

004 Roots of polynomials.mp4

11:27

005 Roots of polynomials exercise.mp4

07:25

006 The quadratic equation.mp4

21:01

007 Complex numbers addition and subtraction.mp4

15:33

008 Complex numbers conjugate and multiplication.mp4

13:31

009 Complex numbers division.mp4

16:14

010 Graphing complex numbers.mp4

10:58

011 Revisiting the quadratic equation with complex numbers.mp4

08:51

012 The unit circle.mp4

13:48

013 Natural exponent and logarithm.mp4

11:27

014 Find a specific point on a Gaussian.mp4

16:17

015 Exercise A family of Gaussians.mp4

07:47

016 Graphing the complex roots of unity.mp4

18:27

017 Log-spaced and linearly spaced numbers.mp4

09:25

018 Logarithm properties Multiplication and division.mp4

16:11

019 Arithmetic and geometric sequences.mp4

15:57

020 Orders of magnitude and scientific notation.mp4

20:09

021 Maxima and minima of functions.mp4

16:43

022 Even and odd functions.mp4

11:56

023 Algebra 2 bug hunt!.mp4

20:22

08 - Graphing conic sections

001 Python code for this section.html

001 pymath-conics-pythonCodes.zip

002 Graphing parabolas.mp4

14:36

003 Creating contours from meshes in Python.mp4

14:56

004 Graphing circles.mp4

17:41

005 Graphing ellipses.mp4

15:28

006 Graphing hyperbolas.mp4

15:32

007 Conic bug hunt!.mp4

05:49

09 - Trigonometry

001 Python code for this section.html

001 pymath-trigonometry-pythonCodes.zip

002 Introduction to random numbers.mp4

12:31

003 Introduction to random numbers exercise.mp4

10:37

004 Exercise Plotting random phase angles.mp4

06:32

005 Converting between radians and degrees.mp4

09:05

006 Converting angles exercise.mp4

16:45

007 The Pythagorean theorem.mp4

17:52

008 Graphing resolution for sine, cosine, and tangent.mp4

13:11

009 Graphing and resolution Exercise.mp4

16:30

010 Eulers formula.mp4

12:48

011 Eulers formula exercise.mp4

11:50

012 Exercise random exploding Euler.mp4

08:06

013 Exercise random snakes with cosine and sine.mp4

11:09

014 Trigonometry bug hunt!.mp4

12:23

10 - Art from trigonometry

001 MXC-pymath-art-from-trig.zip

001 Python code for this section.html

002 Astroid radial curve.mp4

16:30

003 Rose curves.mp4

12:24

004 Squircle.mp4

09:19

005 Logarithmic spiral.mp4

11:15

006 Logistic map.mp4

21:41

11 - Calculus

001 Python code for this section.html

001 pymath-calc.zip

002 Mathematical proofs vs. intuition with examples.mp4

03:44

003 Computing limits of a function.mp4

15:03

004 Computing limits exercise.mp4

13:18

005 Piecewise functions.mp4

16:29

006 Derivatives of polynomials.mp4

14:32

007 Derivatives of polynomials exercise.mp4

09:49

008 Derivatives of trig functions.mp4

12:15

009 Derivatives of trig functions exercise.mp4

08:28

010 Graphing a function tangent line.mp4

14:12

011 Graphing tangent lines exercise.mp4

12:54

012 Finding critical points.mp4

17:03

013 Finding critical points exercise.mp4

15:09

014 Partial derivatives.mp4

11:13

015 Indefinite and definite integrals.mp4

15:52

016 Exercise The fundamental theorem of calculus.mp4

04:47

017 Area between two curves.mp4

12:41

018 Area between two curves exercise.mp4

14:40

019 Calculus bug hunt!.mp4

15:29

12 - Linear algebra

001 Python code for this section.html

001 pymath-linalg.zip

002 Row and column vectors.mp4

17:17

003 Adding and scalar-multiplying vectors.mp4

17:16

004 The dot product.mp4

16:01

005 Dot product application Correlation coefficient.mp4

12:26

006 The outer product.mp4

10:32

007 Matrix multiplication.mp4

17:07

008 Transposing vectors and matrices.mp4

14:26

009 Various special matrices.mp4

18:25

010 Matrix inverse.mp4

11:16

011 Matrix pseudoinverse exercise.mp4

10:09

012 Solving a system of equations.mp4

19:36

013 Visualizing matrix-vector multiplication.mp4

14:14

014 Eigenvalues and eigenvectors.mp4

14:55

015 Eigendecomposition Exercise.mp4

12:44

016 Singular value decomposition.mp4

12:57

017 SVD of Einstein exercise.mp4

12:57

018 Linear algebra BUG HUNT!.mp4

20:14

13 - Probabilities and histograms

001 Python codes for this section.html

001 pymath-probability-pythonCodes.zip

002 Histograms and probability densities.mp4

13:46

003 Probability exercise math functions.mp4

11:49

004 Virtual coin tosses.mp4

13:28

005 Exercise Virtual weighted dice.mp4

15:26

006 Building distributions from random numbers.mp4

18:21

007 Exercise Normalize any distribution to Gaussian.mp4

12:31

008 The central limit theorem.mp4

14:54

009 Exercise the central limit theorem.mp4

11:49

010 Joint probability distributions.mp4

15:31

011 Probability bug hunt!.mp4

10:11

14 - Number theory

001 MXC-pymath-numberTheory.zip

001 Python codes for this section.html

002 Counting perfect numbers.mp4

24:19

003 Euclids Pythagorean triplets.mp4

18:47

004 Fermats theorem.mp4

17:34

005 Plotting number sequences.mp4

15:36

006 Exercise condivergent sequences.mp4

13:07

007 Herons method of square roots.mp4

23:47

008 Exercise Herons mosquito spaceship #13.mp4

15:26

009 Smooth numbers.mp4

22:19

010 Exercise Smooth numbers.mp4

08:56

011 Number theory bug hunt!.mp4

14:31

15 - Bonus section

001 Bonus lecture.html

Description

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

Who is this 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

What You Need to Know?

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

More details

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?Â 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 toÂ mathematics.
Let me guess what you are thinking:
"But I donâ€™t know Python!"Â Thatâ€™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 Iâ€™m not good at math!"Â 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:Â 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.
LOTSÂ 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.
AÂ 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
...andÂ more!

Who is your teacher?
IÂ am Mike X Cohen, a former neuroscience professor (I left that job to focus full-time on teaching online). I'm a bestselling and highly rated instructor on Udemy. I've taught over 200,000 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:
*** â€˜Best teacher ever. I am a psychologist and I didnâ€™t have mathematical training as an undergrad, but the books and lectures of Dr. Cohen have been life savingâ€™
* â€˜What I REALLY like about Mike's style is that not only clear and direct, but he mixes in appropriate amounts of foreshadowing â€¦ to make it easier for me to connect the dots.â€™
* â€˜Mike X Cohen's courses are by far the best ones I've done in Udemy.â€™

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 offers 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

User Reviews

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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 120,000 students have watched over 7,500,000 minutes of my courses. 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 ProcessingPython programming: Master Python programming by solving scientific projects; Master Math by Coding in PythonApplied linear algebra: Complete Linear Algebra; Dimension ReductionSignal processing: Understand the Fourier Transform; Generate and visualize data; Signal Processing; Neural signal processing

Codestars • over 2 million students worldwide!

Instructor's 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.

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Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.