Before paying for a course, it’s a good idea to check what been published out there — there’s a decent amount of high-quality free content. This is what I did when I started learning Python for data science. I checked the curriculum of paid data science courses and then searched all the stuff related to Python.
After taking over 10 courses, I selected the best 4 free courses I took to learn Python for data science. The 4 courses cover most of the stuff I included in “The 4 Stages of Learning Python for Data Science” (especially stages 1, 2 and 4)
Although you won’t get a nice certificate after completing any of these courses, the knowledge you will acquire is priceless.
This 4-hour Python beginners course covers all the basic stuff you need to learn in Python before learning libraries used in data science. This is a crash course in Python — when I took this course I had zero knowledge about programming. The course will show you how to set up Python, teach you how to print your first “Hello World”, and explain all the core concepts in Python.
Please, do not skip the fundamentals of Python and jump to the essentials of Python for Data Science. Remember that Python is a programming language that has applications not only in data science but in a lot of fields. Some Python for Data Science courses might skip core Python concepts that might come in handy in the future.

Some of the topics covered in this course that are frequently used in data science are:
  • Variables & Data Types
  • Lists, Dictionaries, Tuples, Nested Lists
  • Functions, Return Statement, If Statements
  • For Loops, While Loop, Nested Loops
  • Try / Except, Reading and Writing to Files
  • Python Interpreter, Modules, Pip Classes
  • Objects, Object Functions, Inheritance
In addition to that, there are practical exercises where you would put all the concepts learned into practice. You will build a basic calculator, guessing name, translator, and a multiple-choice quiz. On top of that, the instructor has good knowledge of Python, clear explanation, and engaging delivery.
This one is more like a playlist than a course; however, you will find more useful lectures in this playlist than in some paid courses out there.

The first 8 videos in this playlist make a 10-hour full-length course of data analysis. It starts with the most popular data collection technique that data scientists used to build datasets — web scraping. Then you will learn data analysis libraries such as Pandas, Numpy, Matplotlib, and Scikit-learn. On top of that, the playlist includes cool projects that will help you get hands-on experience. All of this is carried out on Jupyter Notebooks, which is data scientists’ computational notebook of choice.
  1. Some of the datasets used in this course to teach the libraries mentioned before are:
  2. Pokemon Dataset
  3. FIFA 20 Dataset
  4. Amazon Product Review Dataset
  5. Sales Data
So if you want to have fun analyzing Pokemon and FIFA datasets or build a machine learning model using product review, consider watching this playlist.
I watched the first 8 videos of the playlist and that helped me build a strong foundation on Python for data analysis.
This is a short yet useful 2-hour NLP course anyone interested in the field of Natural Language Processing should watch. NLP is a branch of artificial intelligence that allows machines to understand human language. Data scientists used NLP techniques to interpret text data for analysis.
This introduction to NLP covers the following topics:
Pre-processing techniques (tokenization, text normalization, and data cleaning)
Machine learning techniques (topic modeling, word embeddings, and text generation)
Python libraries for NLP (NLTK, TextBlob, spaCy, and gensim)
I like this short course in particular because it’s highly engaging and includes cool exercises like spam email classification and sentiment analysis of tweets. Of course, to do all of this you will need to have at least a basic understanding of data analysis libraries such as Pandas and Scikit-learn.
Machine learning & Artificial Intelligence with Tensorflow 2.0 Course
This is a 7-hour Tensorflow course designed for Python programmers who wish to learn machine learning (ML) and artificial intelligence (AI) with TensorFlow. TensorFlow is one of the best libraries available for working with Machine Learning on Python. It makes machine learning model building easy for beginners and professionals alike.

In this course, you will find 8 modules that cover fundamentals topics in ML & AI. To name a few:
  • Core Learning Algorithms
  • Neural Networks with TensorFlow
  • Deep Computer Vision — Convolutional Neural Networks
  • Natural Language Processing with RNNs
  • Reinforcement Learning with Q-Learning
I like this course because the instructor has good knowledge of machine learning and artificial intelligence and has a clear explanation. Also, the course it’s engaging, contains valuable information and coding examples. All of this makes learning TensowFlow easier.

I’m currently taking this free course and I’ve been learning plenty of new things so far and I hope to apply them in coming projects.