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Master Python programming by solving scientific projects

Name: Master Python programming by solving scientific projects
Rating: 4.7 (1583 reviews)

Learn scientific coding in Python from a warm-blooded scientist. Each video includes hands-on solved practice problems!

Created byMike X Cohen

Last updated 8/2026

English

English [Auto],Turkish [Auto],

What you'll learn

Python
Scientific programming
Data visualization
Time series analysis
Modeling
Regular expressions
Spectral analysis
Filtering
Data clustering
Gradient descent
Text processing
Data projects
Data animation

Coding Exercises

This course includes our updated coding exercises so you can practice your skills as you learn.

Course content

34 sections • 168 lectures • 34h 52m total length

Prerequisites and how to rock this course6:58
Identify prerequisites and course structure, including foundations and projects, and maximize learning by coding along, using downloadable resources when provided, and embracing flexible problem-solving.
Code a Sierpinski triangle!12:29
Turn pseudo code into working Python code by coding a Sierpinski triangle in this lecture. Build a 10,000-point fractal plot using NumPy and Matplotlib in Colab or Jupyter.
Python via Google colab5:52
Local Jupyter notebooks via Anaconda5:38
Using the Q&A forum5:17
Index of functions in the course4:38
Discover how to use the downloadable function index resource to view course sections and alphabetized functions, and learn to generate the index with Python.

Variables17:35
Math operators10:48
Master Python math operators across ints and floats, including addition, subtraction, multiplication, division, powers, and order of operations. Explore string overloading, concatenation, repetition, and floating point versus real arithmetic.
Printing and inputting12:04
Learn python printing and inputting by mixing numbers and strings, converting with str and float, and computing the length of side C via the Pythagorean theorem.
Lists17:13
Learn how to create and manipulate Python lists, including integers, strings, and nested lists; use in for membership, + for concatenation, * for repetition, and methods like append and sort.
Tuples7:51
Booleans16:49
Master booleans, true and false, and Python comparisons, including equality tests with double equals. Convert numbers and strings to booleans with bool(), and practice a Pythagorean triplets exercise.
Dictionaries14:44

Inputs and outputs5:26
Python libraries (numpy)12:43
Python libraries (pandas)15:18
Use pandas data frames to store data and inspect with head. Build a dictionary-based data frame with integers 0 to 10, squares, and log values, and handle log of zero.
Getting help on functions8:58
Learn to get help on functions in Python using docstrings, help, and online searches. See examples with sum, NumPy linspace, and inspect for learning function behavior.
Creating functions19:36
Global and local variable scopes11:17
Discover how local variables inside a function differ from global ones in Python. See how scope and name binding decide which variable value is used, with practical examples.
Classes and object-oriented programming12:57
Explore object oriented programming by using classes and objects, including constructors and methods, to manage data in an experiment example, and apply it to a board game tracking exercise.

If-else statements27:01
For loops17:31
Continue7:23
While loops19:56
Master while loops in Python, covering boolean conditions, toggles, and break logic, contrast with for loops, and note when to use while loops in machine learning and optimization tasks.
Initializing variables23:09
Master python programming by solving scientific projects. Learn variable initialization with lists and numpy, using zeros or full, and compare list append, numpy append, and numpy initialization to boost speed.
Function error checking and handling17:42
Develop robust Python practices by enforcing numeric inputs with isinstance checks, raising clear errors, converting numeric strings to float when possible, and using try-except judiciously.
Multiple inputs with zip10:07
Single-line loops (list comprehension)16:32
Broadcasting in numpy21:13
Explore numpy broadcasting to add a vector to each matrix row, compare with for loops, and reshape, tile, and dot to broadcast across columns.

fprintf and f-strings23:13
Plotting dots and lines13:56
Subplot geometry15:56
Learn to lay out multiple plots in one figure using subplot and subplots with various geometries like 1x2, 2x1, and 3x3, and to populate them via loops and axis objects.
Making the graphs look nicer24:56
Adding annotations17:26
Seaborn16:13
Images18:51
Export plots in low and high resolution9:46
Sierpinski pseudocode, part II1:27
Translate the Sierpinski pseudocode from the pink triangle exercise into Python, building the fractal design from scratch without peeking at prior code.

IMPORTANT: Download course materials3:59
Download course materials to access project folders, including helper and solution notebooks, and learn to run Python projects locally with Anaconda or on Google Colab by uploading to Google Drive.
Strategies for solving these projects13:23
Master Python programming by solving scientific projects using five strategies, from real-time code along to from-scratch coding, with a pause-and-code workflow and learning tips.

Requirements

Know how to use a computer!
Motivation to learn Python coding

Description

Unleash Your Python Skills With Real World Scientific Projects

Welcome to "Master Python Programming by Solving Scientific Projects". If you're searching for a course that takes a fresh, hands-on approach to learning Python while solving real-world scientific problems, you've found the right one. This course isn't just about learning a list of Python functions. It's about getting knee-deep into Python's capabilities, understanding its quirks, and leveraging it to tackle fascinating projects.

Why Choose This Course?

Python is a dynamic language, widely used in the scientific community. But you're probably thinking, "Why should I choose this Python course out of hundreds available on Udemy?" Let's cut to the chase and focus on what makes this course unique:

Problem Solving Approach: This course doesn't just teach Python; it reinforces the learning with an array of scientific projects that you might encounter in your academic, professional, or personal life. This strong focus on project-based learning equips you with hands-on coding experience. You'll learn how to think like a programmer and apply your skills in practical situations.
Transparency: I’m not a Python fanatic who views it as the perfect language. I acknowledge that, like every language, Python has its idiosyncrasies. During this course, I will not shy away from Python's annoying or confusing aspects. Instead, I’ll give you a realistic and comprehensive understanding of the language, including its strengths and weaknesses.
Broad Spectrum of Projects: From text processing to time series filtering, from simulating a brain circuit to plotting state-space trajectories, from biomedical signal processing to cryptocurrency investing, this course brings a wide range of projects to the table. Each project is meticulously crafted to ensure you gain the maximum knowledge and practical skills from it.
Interactive Community: In the course Q&A, you'll have the chance to interact with me and your fellow students. Here, I discuss Python coding strategies, data types, scientific coding best practices, and more. Sharing your own clever code solutions and learning from others' experiences will further enhance your learning journey.
Coding with ChatGPT: Gain insights on how to use ChatGPT, a sophisticated AI language model developed by OpenAI, to assist you with boilerplate code and debug your scripts. This interactive feature makes coding more intuitive and efficient, especially when you're stuck with bugs or need a quick solution.

What should you do now?

Take a look at the preview videos to get a glimpse of my teaching style and the course content.
Check out the reviews of this course. The positive feedback and experiences shared by students will give you a good sense of what to expect.
Also, do check out the reviews of my other courses to understand my dedication and passion for teaching.
Embark on this exciting journey and join today! Together, we will dive deep into the world of Python, solving intriguing scientific problems along the way. This course is more than just a learning experience—it's an adventure through the realms of Python and scientific coding.

Who this course is for:

Total beginners to Python
(optional) some experience in other languages (e.g., MATLAB or R)
Interest in using Python for data, science, engineering, physics, biology

Master Python programming by solving scientific projects

What you'll learn

Explore related topics

Coding Exercises

Course content

Introductions6 lectures • 41min

------------ Part 1: The basics ------------1 lecture • 1min

Data types7 lectures • 1hr 37min

Indexing and slicing2 lectures • 20min

Functions7 lectures • 1hr 26min

Flow control9 lectures • 2hr 41min

Text and data visualization9 lectures • 2hr 22min

A brief aside on sharing code1 lecture • 3min

------------ Part 2: The projects ------------1 lecture • 1min

Download all course materials2 lectures • 17min

Requirements

Description

Who this course is for: