As promised in the previous post, I continue listing the numerous MOOC offers about Data Science, also on the basis of the feedback received.
It is worthwhile to mention that Data Analysis and Statistical Inference offers its labs through another valuable platform: DataCamp. Using this platform is like having an instructor at your side, explaining the exercise and giving you feedback. Alternatively, you can complete the exercises just using Rstudio and submitting your scripts.
On the other hand, the Data Science Specialization team has developed an innovative tool for learning R interactively: the swirl package. The idea is learning by doing, and it's fairly simple to get started with R using it. From Rstudio, all you need to do is to install the package, typing:
then, start swirl:
And you will be in the learning environment. You have to select a course that you want to follow and the rest is really self explanatory. The learning sessions are conveniently not too long.
The University of Washington also offers a course: Introduction to Data Science, that includes the basic techniques of data science as well as databases, MapReduce, Hadoop, SQL and NoSQL. It also covers elements of statistical modelling and machine learning, as well as communication of results, and Graph analysis. The recommended (but not compulsory) textbook is "Mining of Massive Datasets". The programming assignments entail Python and SQL besides the usual R. According to the opinion of a colleague that has reviewed it, the word "Introduction" in the title is misleading, since it covers a whole lot more than an introduction.
Stanford University offers a course called Machine Learning, that is also in my watching list as I plan to review it in detail when I'll be more advanced in my studies.
Another course on Machine Learning is Learning From Data, offered by Caltech through edX. This course is currently closed but the material of the 2014 course can be consulted and studied at your own pace. It covers from the basic theory to algorithms and applications. The video lectures are also on YouTube.
Finally, a couple of more courses, both offered by Stanford, that can surely be complementary to those already mentioned, are: Statistical Learning and Introduction to Databases.
I think that with this outstanding offer, I have no excuses left for procrastination, even when I'm not at home - did you know? Coursera has an App for Android!