To facilitate your own exploration of “What is Data Science?”, here is a list of web sites. It is in no particular order. Feel free to suggest others in the comments (the goal is not to be comprehensive but to collect those with the best value for your reading time):
- Wikipedia article Data Science. Good overview, including criticism of the term “Data Science” and links to related subject areas.
- Forbes article by Gil Press, A Very Short History of Data Science. Has many good links to additional resources (some included below).
- O’Reilly Radar article by Mike Loukides, What is data science?. Good discussion of the context in which Data Science is practiced.
- New York Times article by Claire Cain Miller, Data Science: The Numbers of Our Lives. For popular consumption but concise. Discusses educational opportunities.
- Harvard Business Review article by Thomas H. Davenport and D.J. Patil, Data Scientist: The Sexiest Job of the 21st Century. Whether directly or indirectly, this popular 2012 article may be one reason why you are interested enough in Data Science to be visiting this web site! Despite the hyped title, it does a good job of describing the intangibles of Data Science and what drives good Data Scientists.
- Oracle blog by Jean-Pierre Dijcks on Sep 29, 2014, The End of the Data Scientist Bubble… Provocative — be sure to read the comments and a rebuttal blog post in Data Science Central by Mirko Krivanek on October 1, 2014 (and its comments). This is worth reading for the philosophical question of the value of the human and how much of Data Science can be automated. Also on how to deploy Data Science well or poorly. My personal take (to be fleshed out in a blog post soon) is that a Data Scientist is like a plumber. Just as a plumber applies tools that have been around a long time to deliver water, a Data Scientist applies a variety of tools in an interdisciplinary, problem-focused context, to deliver actionable insight into real-world questions that lead to real-world outcomes. The mechanical engineer who designed the plumber’s pipe fittings may be extremely competent, but unless he or she also practices plumbing then he or she is a mechanical engineer not a plumber. The technical school instructors who taught the plumber may know all about plumbing, but unless they also practice plumbing then they are instructors not plumbers. The plumbing engineer who produced the design for the building in which the plumber is working may know more about hydraulics than the plumber, but unless he or she practices plumbing then he or she is a plumbing engineer not a plumber. In the same way, a Data Scientist is simply someone who practices Data Science — not someone who specializes (however competently) in a tool or field of practice related to Data Science. Different Data Scientists will have strengths in different areas, such as Machine Learning or Visualization; but that is different from saying that a Business Analyst or a Machine Learning Algorithm Developer or a Visualization Expert is necessarily a Data Scientist. No disrespect is intended. On the contrary, they are all fields that are worthy of respect, and none are intrinsically better than the others. A Data Scientist is simply someone who practices Data Science. [Ha! That turned into a blog post… look for an update as a separate post.]
- Revolution Analytics blog post from Sept. 28, 2011, Data Science: a literature review. Wonderful jumping-off point to links that are themselves more jumping-off points.
- Presentation by Harlan Harris for the Data Science DC meetup group from Sept. 26, 2011, What is “Data Science” Anyway? This excellent slide deck shows a variety of perspectives. Referenced in the above Revolution Analytics post.
- WhatsTheBigData blog post from April 26, 2012, A Very Short History of Data Science . Another good overview with lots of links.
- KDNuggets post by Anmol Rajpurohit, Mar 27, 2014, interview with Paco Nathan, Is Data Scientist the right career path for you? Candid advice. The tone of the interview is more important than the specifics. Has good discussion of the reality that Data Science is practiced within the context and culture of organizations, for better or for worse.
- Data Science Central blog post by William Vorhies on October 23, 2014, Prescriptive versus Predictive Analytics – A Distinction without a Difference?. A good question for you to ponder. In my opinion, the “Descriptive Analytics” → “Predictive Analytics” → “Prescriptive Analytics” sequence that you will see from time-to-time is less useful than it may seem. In my opinion, any difference between “predictive” analytics and “prescriptive” analytics is less about the kinds of analytics than it is about how the analytics are applied. And that is really about how the Data Science is practiced.
- Data Science Central blog post by Vincent Granville on January 16, 2014, Six categories of Data Scientists. Interesting, has good links to related articles.