<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/the-periodic-table-of-data-science/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://www.datacamp.com/community/blog/data-science-periodic-table"> DataCamp Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>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 <a target="_blank" rel="nofollow" href="http://www.oreilly.com/data/free/2016-data-science-salary-survey.csp">2016 Data Science Salary Survey</a> by O’Reilly, the 2017 Magic Quadrant for Data Science Platforms by <a target="_blank" rel="nofollow" href="http://www.gartner.com/technology/home.jsp">Gartner</a>, and the <a target="_blank" rel="nofollow" href="http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html">KD Nuggets 2016 Software Poll</a> results, among other sources. The categories in the table are not all mutually exclusive.</p>
<p>Check out the full periodic table of data science below:</p>
<p><a target="_blank" rel="nofollow" href="https://i2.wp.com/s3.amazonaws.com/assets.datacamp.com/blog_assets/Data-Science-Periodic-Table.png?ssl=1"><img alt="" src="https://i2.wp.com/community.datacamp.com.s3.amazonaws.com/community/production/ckeditor_assets/pictures/466/content_screenshot_2017-04-12_17_29_56.png?w=456" data-recalc-dims="1"></a></p>
<h2>Navigating The Periodic Table of Data Science</h2>
<p>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. </p>
<p>The table puts the data science resources, tools and companies in the following 13 categories: </p>
<p><strong>Courses: </strong>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 <a target="_blank" rel="nofollow" href="http://www.coursera.org/">Coursera</a> and <a target="_blank" rel="nofollow" href="http://www.edx.org/">Edx</a>, and much more!</p>
<p><strong>Boot camps: </strong>this section includes resources for those who are looking for more mentored options to learn data science. You’ll see that boot camps like <a target="_blank" rel="nofollow" href="https://www.thedataincubator.com/">The Data Incubator</a> or <a target="_blank" rel="nofollow" href="http://www.galvanize.com/">Galvanize</a> have been included. </p>
<p><strong>Conferences: </strong>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 <a target="_blank" rel="nofollow" href="https://user2017.brussels/">UseR!</a>, <a target="_blank" rel="nofollow" href="http://tc17.tableau.com/">Tableau Conference</a> and <a target="_blank" rel="nofollow" href="https://pydata.org/">PyData</a>.</p>
<p><strong>Data: </strong>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 <a target="_blank" rel="nofollow" href="https://data.world/">data.world</a>, <a target="_blank" rel="nofollow" href="https://www.quandl.com/">Quandl</a> and <a target="_blank" rel="nofollow" href="https://www.statista.com/">Statista</a>.</p>
<p><strong>Projects &amp; Challenges, Competitions: </strong>after practicing, you might also consider taking on bigger projects: data science portfolios, competitions, challenges, …. You’ll find all of these in this category of the Periodic Table of Data Science! One of the most popular options is probably <a target="_blank" rel="nofollow" href="http://www.kaggle.com/">Kaggle</a>, but also <a target="_blank" rel="nofollow" href="https://www.drivendata.org/">DrivenData</a> or <a target="_blank" rel="nofollow" href="http://www.datakind.org/">DataKind</a> are worth checking out!</p>
<p><strong>Programming Languages &amp; Distributions: </strong> data scientists generally use not only one, but many programming languages; Some programming languages like Python have recently gained a lot of traction in the community and also Python distributions, like <a target="_blank" rel="nofollow" href="https://anaconda.org/">Anaconda</a>, seem to find their way to data science aspirants. </p>
<p><strong>Search &amp; Data Management: </strong>this enormous category contains all tools that you can use to search and manage your data in some way. You’ll see, on the one hand, a search library like <a target="_blank" rel="nofollow" href="https://lucene.apache.org/core/">Lucene</a>, but also a relational database management system like <a target="_blank" rel="nofollow" href="https://www.oracle.com/index.html">Oracle</a>. </p>
<p><strong>Machine Learning &amp; Stats: </strong>this category not only offers you libraries to get started with machine learning and stats with programming languages such as Python, but also entire platforms, such as <a target="_blank" rel="nofollow" href="http://www.alteryx.com/">Alteryx</a> or <a target="_blank" rel="nofollow" href="https://www.datarobot.com/">DataRobot</a>. </p>
<p><strong>Data Visualization &amp; Reporting: </strong>after you have analyzed and modeled your data, you might be looking to visualize the results and report on what you have been investigating. You can make use of open-source options like <a target="_blank" rel="nofollow" href="https://shiny.rstudio.com/">Shiny</a> or <a target="_blank" rel="nofollow" href="http://matplotlib.org/">Matplotlib</a> to do this, or all back on commercial options such as <a target="_blank" rel="nofollow" href="http://www.qlik.com/us/">Qlikview</a> or <a target="_blank" rel="nofollow" href="http://www.tableau.com/">Tableau</a>. </p>
<p><strong>Collaboration: </strong>collaboration is a trending topic in the data science community. As you grow, you’ll also find the need to work in teams (even if it’s just with one other person!) and in those cases, you’ll want to make use of notebooks like <a target="_blank" rel="nofollow" href="http://www.jupyter.org/">Jupyter</a>. But even as you’re just working on your own, working with an IDE can come in handy if you’re just starting out. In such cases, consider <a target="_blank" rel="nofollow" href="https://www.yhat.com/products/rodeo">Rodeo</a> or <a target="_blank" rel="nofollow" href="https://github.com/spyder-ide">Spyder</a>. </p>
<p><strong>Community &amp; Q&amp;A: </strong>asking questions and falling back on the community is one of the things that you’ll probably do a lot when you’re learning data science. If you’re ever unsure of where you can find the answer to your data science question, you can be sure to find it in sites such as <a target="_blank" rel="nofollow" href="http://www.stackoverflow.com/">StackOverflow</a>, <a target="_blank" rel="nofollow" href="http://www.quora.com/">Quora</a>, <a target="_blank" rel="nofollow" href="http://www.reddit.com/">Reddit</a>, etc. </p>
<p><strong>News, Newsletters &amp; Blogs: </strong>you’ll find that the community is evolving and growing rapidly: following the news and the latest trends is a necessity. General newsletters like <a target="_blank" rel="nofollow" href="https://www.datascienceweekly.org/">Data Science Weekly</a> or <a target="_blank" rel="nofollow" href="https://dataelixir.com/">Data </a><a target="_blank" rel="nofollow" href="https://dataelixir.com/">Elixir</a>, or language-specific newsletters like <a target="_blank" rel="nofollow" href="http://www.pythonweekly.com/">Python Weekly</a> or <a target="_blank" rel="nofollow" href="https://rweekly.org/">R Weekly</a> can give you your weekly dose of data science right in your mailbox. But also blogging sites like <a target="_blank" rel="nofollow" href="https://www.r-bloggers.com/">RBloggers</a> or <a target="_blank" rel="nofollow" href="http://www.kdnuggets.com/">KD Nuggets</a> are worth following! </p>
<p><strong>Podcasts: </strong>last, but definitely not least, are the podcasts. These are great in many ways, as you’ll get introduced to expert interviews, like in <a target="_blank" rel="nofollow" href="https://www.becomingadatascientist.com/">Becoming A Data Scientist</a> or to specific data science topics, like in <a target="_blank" rel="nofollow" href="http://datastori.es/">Data Stories</a> or <a target="_blank" rel="nofollow" href="http://www.thetalkingmachines.com/">Talking Machines</a>!</p>

<p>Are you thinking of another resource that should be added to this periodic table?  Leave a comment below and tell us about it!</p>
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