Top 10 Data Science Skills, and How to Learn Them

One of most popular posts this year came from Ferris Jumah, a data scientist at LinkedIn, who mapped the most popular skills of data scientists by scraping LinkedIn profile data. One of the common comments amongst data scientists who came across this post- as with most of our posts focused around data science skillsets- was “Surely, you can’t expect data scientists to have all these skills?”

Naturally, we don’t- every data science role involves a particular comibination of some of the skills, and anyone who had mastered all of the programming languages listed alone would be some sort of computing demi-God. Having said that, there’s always room for growth and expansion; thus, we’ve found 10 online resources to help you get acquainted with the 10 biggest skills in the Data Science Skills Network. Whether you’re a data science rookie or a seasoned professional, we hope this compiled list of some of the most excellent courses on the web proves to be useful.

1. Analytics– the SAS Enterprise Business Intelligence Course Leveraged more at the tech-curious businessman than the seasoned data scientist, this course is still undoubtedly worth investigating for anyone looking into getting started with business reporting in SAS. They offer courses for a wide variety of BI roles, from initial platform exploration to designing, tuning and maintaining OLAP cubes. See more

Taught by Stanford Professor, Baidu’s Chief Scientist, and all-round data science rockstar Andrew Ng, this course is indisputably the course to take if you’re looking to get in to data science. The course covers data mining, pattern recognition, supervised & unsupervised learning, and draws on multiple real-world examples and applications. Plus, it’s absolutely free- the 2015 sessions have yet to be announced, but if you’re interested in delving deeper into machine learning, it’s definitely worth adding this course to your watchlist. See more

3. Statistics– Google Tech Talks’ Stats 202 Like the Coursera machine learning class, this series of 5 hour-long talks is based on a Stanford class. Not only do you have the hallmark of a class designed by a world-class institution, you also don’t have to pay a cent to watch it. For anyone with a basic grounding in stats, these talks are highly recommended. Key topics covered include: exploring and visualizing data, association analysis, classification, and clustering. Additional complementary resources can be found here. See more

Part of Coursera’s 9-part Data Science Specialisation, this course is taught by Roger D. Peng of John Hopkins University.