CS109 Data Science

With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. It’s not on an interactive platform, like Coursera or edX, and doesn’t offer any sort of certification, but it’s definitely worth your time and it’s totally free.

I think the true highlight of the course are the various Labs (aka homework assignments) in the course, that walk through exercises in statistical learning through Jupyter notebooks.

I'd recommend a brilliant, free online Python/Data Science course in the form of Harvard's CS109. Check out the syllabus, it's pretty darn thorough in terms of application & theory of the entire data science process(import, wrangling, exploration, prediction, and communication). Big plus, it uses Python and relevant statistical/machine learning concepts.

This is the flagship course of the program that covers almost the entire breadth of the practice of data science in a very pragmatic way. It is hard to say which part of this course was the best - the lectures, the labs, the homework assignments - everything was just awesome. The workload was very high, though.

Harvard's CS109 course is quite good and uses Python. It covers quite a bit of data cleaning and the data science part of things.

I have some experience as a Business Analyst so it did refresh some basic concepts. But I did not learn anything new. I have been doing the MOOC for the past 2 weeks now and kudos to Prof for designing such a wonderful resource and more importantly making it available free of cost.
I would really urge you to try the assignments. They are amazing. I have completed 2/3 of the first one. I am already feeling good with functional and basic Python. Also just watching the lectures will never help. Especially graduate level lectures. It never does. These are just guidelines to help you along the way. Google and Stackoverflow will be your best friends if you are truly to appreciate these lectures. Go ahead and Google even the jokes or other references you don't get. It will pay off getting in the lecturers head.

I would recommend this course to everyone who is looking for a course that covers a wide range of very ‘hot’ topics: collection and preparation of data, Machine learning (Regression, SVM, Trees, Bayesian approach, Clustering, Random forests, PCA etc.), analysis of networks and visualisation. It does not go deep into details of all the algorithms but provides a lot of practical knowledge to get started.