When learning something new these days one is presented with an overwhelming amount of sources to choose from: books, courses, articles, documentation, stackoverflow, etc. Finding relevant information at appropriate level may be as hard as learning itself.

While discovering Python and Data Science, I’ve tried a few resources with various level of success and satisfaction. In this post I’d like to compile a list of learning resources that I found helpful for a beginner, and a little past beginner level.

Coursera: Python for Everybody Specialization

Link: Python for Everybody Specialization

This is a great course for absolute beginners taught by Charles Severance, Associate Professor, University of Michigan. No previous knowledge is required. It has a good flow, it’s easy to follow and as in many courses on coursera there are practical exercises and quizzes. Unfortunately, there’s no follow up to learn Python at a more advanced level.

Python Courses on Pluralsight

If you wish to deepen your Python knowledge, I can recommend to look for courses on Pluralsight. For instance, “Python Fundamentals” and “Python – Beyond the Basics” by Austin Bingham and Robert Smallshire

These courses are well taught and structured. The only thing I found missing when compared to courses on coursera were practical exercises and auto-grading. I believe the examples do help the learning process and keep one motivated.

Data Science Courses on Coursera

Data Science is a vast topic, and there are multiple specializations on Coursera related to it. The same University of Michigan offers Applied Data Science with Python Specialization. If you simply want to learn how to use existing data science libraries in Python without understanding how things work and why they do what they do, this might be a course for you.

For me, this wasn’t enough. I feel strange not understanding what’s happening, even when things seem to be working. That’s why I’ve enrolled in Machine Learning course from Stanford University, taught by Andrew Ng, and can genuinely recommend it. There’s a lot of math in it, and no Python (the course uses Octave and Matlab), but you learn what the ML algorithms actually do.

Other resources

DataCamp.com

DataCamp has a number of data science related courses. They come with interactive practice examples built-in straight in the course time line which is neat. However, I found them a little too simple, perhaps, I’ve approached them too late and should’ve tried before other courses. So if you’re looking for something easy to begin your data science journey - these courses might be for you.

kaggle.com

A nice source of datasets to play with, plus one can enter a competition. I personally haven’t participated yet, but I’m very much looking forward to it. Also, you can find some interesting tutorials there. For example, “Titanic Data Science Solutions” - a tutorial by Manav Sehgal, describing in detail an approach to solving a problem of predicting surviving of passangers based on available training set.

Pandas Cookbook by Julia Evans

Great set of pandas tutorials by Julis Evans, I’d say it’s a must-read: Julia Evans’s Pandas Cookbook.

Stackoverflow

And, of course, whenever you have some specific questions - StakOverflow remains the number one place to go to look for answers.