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October 24, 2014

Back to school with Coursera

Are you ever done with learning? Got your diploma and think: "I am set for life!"? I think we can all agree that that is not how the world works anymore. You might leave school or university with a great knowledge base, but once you start working on your career, you realize that there is still so much left to learn.

That's why I decided to use this past summer to start brushing up on some fundamental statistical knowledge and skills. To do this I used Coursera. Coursera is "an education platform that partners with top universities and organizations worldwide, to offer courses online for anyone to take, for free" (source: Coursera website). The service offers more than 400 courses during the year created by the world's top universities and institutions. You decide how much you get out of a course. You can just tune in to see the lecture videos. You can complete weekly quizzes about the material. You can contribute to the community discussion board. If you have some money to spare, you can even sign up for an official certificate to indicate that you completed all the required assignments for that course.

Johns Hopkins University offers a Data Science specialization, consisting of nine courses and a capstone (if you decide to go for the paid certificate track). They offer the courses each month, so you can complete the entire specialization in nine months (or less, if you decide to do more than one course at a time). Starting out with an introductory course on the Data Scientist's Toolbox, the specialization takes you an a journey that ends with an introduction to Practical Machine Learning and Developing Data Products. Going through all the courses will give you a great primer on all that Data Science has to offer.

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So why did I start following the courses? Even though I already have experience with most of the topics discussed in the specialization courses (the exception being machine learning), I thought it would be a great way for me to refresh my knowledge on these topics. Another reason for taking theses courses, is that they are offered by a university I never attended. The data analysts or statisticians running these courses probably have a different perspective on some aspects of Data Science than the professors that have taught me so far. I was hopeful that this discrepancy would take me out of my comfort zone and teach me some new perspectives and insights on the basics of Data Science.

I am happy to report, after following the first two courses The Data Scientist's Toolbox and R Programming, that all my expectations and hopes have been met. I've learned how to work with Github, do cool new things with R, and I got to see how other people (teachers and fellow students) work and think. The courses were challenging, but not impossible to complete next to a full-time job. The teachers are very personable, and actually write a brilliant blog on the world of data science, Simply Statistics. I recommend you start reading right now. Their pieces are critical, current, and inventive.

For November, I've signed up for their next course, Getting and Cleaning Data. I am also venturing out of my Johns Hopkins comfort zone to see what else Coursera has to offer. How about An Introduction to Sustainable Development from Columbia University? Or a course that has just started on The Bilingual Brain from the University of Houston?

In a word, Coursera has offered the world a great opportunity for continuous learning, and I feel that everyone should at least see if there's anything out there that's of interest to them. It is never too late to start learning something new, and with these MOOCs you can connect with people around the globe. If that isn't awesome, I don't know what is...

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