Master Math by Coding in Python from Udemy

You need to learn mathematics

Math is at the heart of all advances in modern computing, including data science, AI (artificial intelligence), deep learning, generative AI, machine learning, statistics, video games, and on and on...

If you want to study or work in these fields, or if you're just curious to learn more about these technical topics, you need to have a grasp of mathematics.

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. Python isn't just a coding language; it's a gateway to mastering math.

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. It's also a great way to get started on your adventures into data science, deep learning, and AI.

What do you get in this course?

Over 37 hours of instruction that includes Python coding, visualization, loops, variables, and functions.
Important Python libraries for data science and mathematics, including numpy, sympy, scipy, and matplotlib.
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.
A student of data science, machine learning, or AI who needs to improve their mathematics knowledge to understand algorithms.
Looking to transition from another field into AI-related fields like deep learning.
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, 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 250,000 students the foundations of scientific programming, data analysis, data science, 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’

‘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 offers a 30 day money back guarantee.

What's inside

Learning objectives

Most important: confidence in learning math!
Arithmetic
Algebra (1, 2)
Graphing
Trigonometry
Calculus

Linear algebra
Python programming
Python libraries including sympy, numpy, matplotlib, scipy
Formatting beautiful equations in latex
Data visualization
Integrating python, markdown, and latex

Most important: confidence in learning math!
Arithmetic
Algebra (1, 2)
Graphing
Trigonometry
Calculus
Linear algebra
Python programming
Python libraries including sympy, numpy, matplotlib, scipy
Formatting beautiful equations in latex
Data visualization
Integrating python, markdown, and latex

Syllabus

Introductions and installations

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

Using Python through Jupyter (installing Anaconda)

Using Python via Google-colab (no installation!)

How to download all course materials

Create a beautiful harmonograph!

Getting help in Python

How to use Udemy's course features (video playback, Q&A, notes, captions, etc)

Arithmetic

Addition, subtraction, multiplication, division

Using variables in place of numbers

Printing out equations in Jupyter notebook

Writing comments in Python

Exponents (powers)

Using for-loops to compute powers

Order of operations

Testing inequalities and Boolean data type

Using if-statements and logical operators

Absolute value

Remainder after division (modulus)

Create interactive math functions, part 1

Create interactive math functions, part 2

Create interactive math functions, part 3

Arithmetic bug hunt!

Introduction to Sympy and LaTeX

Intro to Sympy, part 1

Intro to LaTeX

Intro to Sympy, part 2

Printing with f-strings

Example: Use Sympy to understand the law of exponents

Sympy/Latex bug hunt!

Python data types

Numbers and strings

Lists and numpy arrays

Algebra 1

Solving for x

Solving for x: exercises

Expanding terms

Creating and accessing matrices with numpy

Exercise: Create a multiplication table

Associative, commutative, and distributive properties

Creating and working with Python lists

More on "slicing" in Python

Greatest common denominator

Greatest common denominator: exercises

Introduction to Python dictionaries

Prime factorization

Solving inequalities

Adding polynomials

Multiplying polynomials

Dividing by polynomials

Factoring polynomials

Algebra 1 bug hunt!

Graphing and visualization

Plotting coordinates on a plane

Plotting coordinates on a plane: exercise

Graphing lines part 1: start/end notation

Graphing lines part 2: slope-intercept form

Graphing rational functions

Plotting with Sympy

Plotting with Sympy: exercises

Course tangent: self-accountability in online learning

Making images from matrices

Images from matrices: exercise

Drawing patches with polygons

Exporting graphics as pictures

Graphing bug hunt!

Algebra 2

Summation and products

Differences (discrete derivative)

Roots of polynomials

Roots of polynomials: exercise

The quadratic equation

Complex numbers: addition and subtraction

Complex numbers: conjugate and multiplication

Complex numbers: division

Graphing complex numbers

Revisiting the quadratic equation with complex numbers

The unit circle

Natural exponent and logarithm

Find a specific point on a Gaussian

Exercise: A family of Gaussians

Graphing the complex roots of unity

Log-spaced and linearly spaced numbers

Logarithm properties: Multiplication and division

Arithmetic and geometric sequences

Orders of magnitude and scientific notation

Maxima and minima of functions

Even and odd functions

Algebra 2 bug hunt!

Graphing conic sections

Graphing parabolas

Creating contours from meshes in Python

Graphing circles

Graphing ellipses

Graphing hyperbolas

Conic bug hunt!

Trigonometry

Introduction to random numbers

Introduction to random numbers: exercise

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers Python libraries such as numpy, sympy, scipy, and matplotlib, which are essential for data science and mathematical computing

Aimed at complete beginners, which means learners do not need prior Python experience to get started with the course material

Offers a big-picture overview of beginner and advanced mathematics, from solving for 'x' to computing integrals to finding eigenvalues

Includes practical exercises and 'bug hunts' to reinforce learning and develop problem-solving skills in both math and coding

Teaches LaTeX for formatting equations, which is useful for writing reports and communicating mathematical ideas clearly

Requires learners to install Anaconda or use Google Colab, which may require some initial setup and familiarity with these environments

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Master Math by Coding in Python with these activities:

Review Algebra Fundamentals

Show steps

Reinforce your understanding of core algebraic concepts to prepare for the more advanced topics covered in the course.

Browse courses on Algebra

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Review basic algebraic operations and equation solving.
Practice factoring polynomials and simplifying expressions.
Work through example problems involving exponents and logarithms.

Review 'Python Crash Course'

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Solidify your Python programming skills to better understand the code examples and exercises in the course.

View Python Crash Course, 3rd Edition: A Hands-On,... on Amazon

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Read the introductory chapters covering Python basics.
Complete the exercises related to variables, loops, and functions.
Experiment with the code examples and modify them to deepen your understanding.

Coding Math Problems

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Practice translating mathematical formulas and concepts into Python code to reinforce your understanding of both math and programming.

Show steps

Choose a math problem from the course material or a textbook.
Write Python code to solve the problem.
Test your code with different inputs and verify the results.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Math Cheatsheet

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Compile a cheatsheet of important mathematical formulas, concepts, and Python code snippets covered in the course for quick reference.

Show steps

Review the course materials and identify key formulas and concepts.
Organize the information into a clear and concise cheatsheet.
Include relevant Python code snippets for each concept.

Build a Simple Calculator

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Develop a Python-based calculator that can perform basic arithmetic operations and solve simple algebraic equations.

Show steps

Design the calculator's user interface (command line or GUI).
Implement functions for addition, subtraction, multiplication, and division.
Add functionality to solve simple algebraic equations.
Test the calculator thoroughly with different inputs.

Review 'Linear Algebra and Its Applications'

Show steps

Deepen your understanding of linear algebra concepts to better grasp the mathematical foundations of data science and machine learning.

View Linear Algebra and Its Applications on Amazon

Show steps

Read the chapters related to matrices, vectors, and linear transformations.
Work through the example problems and exercises in the book.
Relate the concepts to the Python code examples in the course.

Visualize Math Concepts

Show steps

Create interactive visualizations of mathematical concepts using Python libraries like Matplotlib and SymPy to enhance understanding and retention.

Show steps

Choose a mathematical concept from the course (e.g., derivatives, integrals, or linear transformations).
Write Python code to generate a visualization of the concept.
Add interactive elements to the visualization to allow users to explore the concept further.

Career center

Learners who complete Master Math by Coding in Python will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses programming and statistical methods to analyze data and extract meaningful insights, often working with large datasets. This career role relies heavily on mathematical concepts, and this course helps build that foundation through the use of Python, a prominent language in the field. The course's focus on mathematics, including algebra, calculus, and linear algebra, makes it a useful foundation for a data scientist, as these concepts are at the heart of many data analysis algorithms. The Python libraries covered, such as numpy, scipy, and matplotlib, are essential tools for data science. The course's practical exercises and bug hunts also provide useful experience in writing and debugging code, a valuable skill for data scientists.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models, working to create systems that can learn from and make predictions based on data. A strong grasp of mathematics is critical for this role, because it is essential to understand the algorithms that drive machine learning. The course provides hands-on practice with Python, a cornerstone language for machine learning. The course’s focus on mathematical concepts like linear algebra, calculus, and statistics are particularly useful for any aspiring Machine Learning Engineer. The course’s coverage of Python libraries commonly used in machine learning, such as numpy, scipy, and matplotlib, further enhances its relevance to this field.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Researcher

An Artificial Intelligence Researcher pushes the boundaries of AI by designing and developing new algorithms and models. This career role involves an understanding of advanced mathematical concepts. The course's focus on core mathematical principles using Python as a tool provides a very good starting point for a career in AI research. The use of numpy, scipy, and matplotlib within the course’s curriculum also familiarizes students with tools that are frequently deployed in AI research. Furthermore, this course provides a strong foundation in mathematics and coding, both essential to AI research.

See salaries and explore the career path for Artificial Intelligence Researcher

Quantitative Analyst

A Quantitative Analyst, often called a 'quant', develops and implements mathematical and statistical models to analyze financial markets and make trading decisions. This career role requires in-depth knowledge of mathematical concepts, and this course helps build a foundation through practice with Python. Topics such as calculus, linear algebra, and statistics covered in this course, are directly relevant to what a quant does. Additionally, experience with Python libraries for scientific computing, also provided by the course, provides a useful tool for modeling and analysis in finance. This course will familiarize students with how coding and mathematical concepts can be synthesized to solve complex, real-world problems.

See salaries and explore the career path for Quantitative Analyst

Software Developer

A Software Developer designs, develops, and tests software applications. While not all software development requires a deep understanding of mathematics, familiarity with mathematical concepts is valuable for many types of software development, especially in areas involving graphics or scientific computing. This course helps build a foundation in the fundamentals of mathematics through coding in Python. The course's introduction to Python, a language used extensively in software, may be beneficial. The course covers numerous topics relevant to software development, such as loops, variables, and functions.

See salaries and explore the career path for Software Developer

Statistician

A Statistician collects, analyzes, and interprets data to inform decision-making. This career role relies on mathematical principles and statistical methods. This course could be helpful for a statistician, as it provides a practical approach to learning mathematical concepts through Python programming. While this course does not provide training in statistics per se, the mathematical foundational skills, and familiarity with Python libraries like numpy and scipy, all provided in this course, can be helpful. The course teaches students how to solve complex mathematical problems using code, which provides good practice for a statistician.

See salaries and explore the career path for Statistician

Financial Modeler

A Financial Modeler creates mathematical models to represent financial markets and instruments, which are used to forecast trends, evaluate risk, and make investment decisions. This career role requires a robust understanding of mathematics and statistics, and this course may be helpful for acquiring those skills. This course's hands-on approach using Python for mathematical operations provides a valuable skillset for financial modeling, and the course introduces important concepts such as algebra and calculus. The course’s exploration of data visualization and linear algebra are also useful tools in this role.

See salaries and explore the career path for Financial Modeler

Game Developer

A Game Developer creates video games, often working on the programming and technical components of gameplay. This course may be useful in the field of game development, because game development frequently involves mathematics, whether it is for physics engines, animation, or artificial intelligence. The course introduces concepts, such as linear algebra and trigonometry, which can be beneficial to game development. The course teaches mathematical concepts through Python, a language often used in game development, and this may be advantageous to the aspiring developer.

See salaries and explore the career path for Game Developer

Bioinformatician

A Bioinformatician uses computational methods to analyze biological data, often involving complex datasets. This career role utilizes tools and techniques from mathematics, computer science, and statistics. This course may be helpful for anyone pursuing this field, considering the course’s focus on Python, which is commonly used in bioinformatics. The course's emphasis on mathematical concepts like linear algebra, and its introduction of data visualization tools, are also potentially useful to bioinformaticians.

See salaries and explore the career path for Bioinformatician

Robotics Engineer

A Robotics Engineer designs, builds, and maintains robots and robotic systems. This career role relies on mathematical and scientific principles. This course may be useful because of it’s focus on coding and math, which is applicable to robotics engineering. The course's focus on Python programming, and its use of relevant math libraries, helps build a foundation, and topics such as linear algebra and calculus, covered in the course, are also potentially relevant to the field.

See salaries and explore the career path for Robotics Engineer

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to solve complex logistical problems. This career role benefits from a strong quantitative aptitude. The course may be useful due to it’s focus on key concepts in mathematics, and it’s use of Python. The course’s coverage of algebra and function modeling can be useful. Finally, a general comfort with applied mathematics and coding is helpful for this role, and this is something that the course fosters.

See salaries and explore the career path for Operations Research Analyst

Physics Teacher

A Physics Teacher educates students on the fundamental principles of physics. This role requires a robust understanding of math and science. This course may be useful in terms of applying mathematics to problems. The course teaches mathematical and computational skills that can be transferred to the classroom. The course’s discussion of math topics such as algebra, calculus, and linear algebra, provides a foundation in the subject matter which the teacher will communicate in the classroom. The course’s hands-on math via code can help teachers make their own classroom material.

See salaries and explore the career path for Physics Teacher

Math Teacher

A Math Teacher is an educator who instructs students in mathematics at the elementary, secondary, or post-secondary level. Though this role does not require programming, a Math Teacher may benefit from this course. The course covers many of the topics that a math teacher would be teaching, and it presents them in a way that goes beyond theory. For example, using Python to animate complex number multiplication, or visualize conic sections, could be adapted into their own classroom lessons, and be valuable to their students. The course may also build the teacher's confidence in the subject.

See salaries and explore the career path for Math Teacher

Research Scientist

A Research Scientist conducts scientific studies in a variety of fields, employing experimental design, data analysis, and mathematical modeling. This career role requires a strong grasp of mathematics and computation, and therefore this course may be useful. The course’s focus on a variety of mathematical concepts, paired with practice in Python, can be useful for those pursuing a career in research science. The course’s use of Python libraries like scipy and numpy, as well as its coverage of data visualization, are also potentially useful in a research context.

See salaries and explore the career path for Research Scientist

Engineering Consultant

An Engineering Consultant provides expert advice and solutions to engineering problems across various industries. This career role requires a strong analytical and problem-solving skills, as well as a firm foundation in mathematics. The course may be useful for an Engineering Consultant as it introduces core mathematical principles and encourages problem solving. The course’s use of Python and the application of mathematical concepts through coding can strengthen a consultant’s quantitative skills, which may be valuable for some clients.

See salaries and explore the career path for Engineering Consultant

Master Math by Coding in Python

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Syllabus

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Activities

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