The Periodic Table of Data Science

This periodic table can serve as a guide to navigate the key players in the data science space. The resources in the table were chosen by looking at surveys taken from data science users, such as the 2016 Data Science Salary Survey by O’Reilly, the 2017 Magic Quadrant for Data Science Platforms by Gartner, and the KD Nuggets 2016 Software Poll results, among other sources. The categories in the table are not all mutually exclusive.

Check out the full periodic table of data science below:

You’ll see that on the table‘s left-hand section lists companies that have to do with education: here, you’ll find courses, boot camps and conferences. On the right-hand side, on the other hand, you’ll find resources that will keep you up to date with the latest news, hottest blogs and relevant material in the data science community. In the middle, you’ll find tools that you can use to get started with data science: you’ll find programming languages, projects and challenges, data visualization tools, etc. 

The table puts the data science resources, tools and companies in the following 13 categories: 

Courses: for those who are looking to learn data science, there are a bunch of sites (companies) out there that offer data science courses. You’ll find various options here that will probably suit your learning style: DataCamp for learning by doing, MOOCs by Coursera and Edx, and much more!

Boot camps: this section includes resources for those who are looking for more mentored options to learn data science. You’ll see that boot camps like The Data Incubator or Galvanize have been included. 

Conferences: learning is not an activity that you do when you go on courses or boot camps. Conferences are something that learners often forget, but they also contribute to learning data science: it’s important that you attend them as a data science aspirant, as you’ll get in touch with the latest advancements and the best industry experts. Some of the ones that are listed in the table are UseR!, Tableau Conference and PyData.

Data: practice makes perfect, and this is also the case for data science. You’ll need to look and find data sets in order to start practicing what you learned in the courses on real-life data or to make your data science portfolio. Data is the basic building block of data science and finding that data can be probably one of the hardest things. Some of the options that you could consider when you’re looking for cool data sets are data.world, Quandl and Statista.

Projects & Challenges, Competitions: after practicing, you might also consider taking on bigger projects: data science portfolios, competitions, challenges, ….