In truth, the buzz began to build long before the start of 2013. An October 2012 article in the Harvard Business Review declared “Data Scientist: The Sexiest Job of the 21st Century." It goes on to describe the role in terms that place it somewhere between a data-swashbuckling Indiana Jones and super-sleuth, Sherlock Holmes.
While we’d never detract from such grand characterizations (these people are critical pioneers), it’s important to begin to define this data-driven, dynamo of a role in terms that make it practical, applicable and most importantly, actionable.
We’ve dug through our 2013 treasure trove, and found five great pieces of content that help dispel the illusions around the data scientist and reveal the role’s remarkable, real-world raison d’etre.
I realize that Twitterchats-turned-blogs can be challenging to follow but Hilary Mason’s bon mots and eye-opening enthusiasm make this one a must-read! Thanks for painstakingly transcribing this one, James!
Despite de-emphasizing the “science” in Data Science, Kobielus’ post offers a very simple and easy-to-follow set of best practices for ensuring that companies are getting the most accurate outcomes from Data Science.
If you aren’t following Revolution Analytics data Scientist, David Smith, on twitter and on his blog, you might be missing out on a vital Data Science voice. In this podcast, Smith, understandably rankled that his role is dismissed as “hype,” cuts through the conjecture with some astute case-making for the value of Data Science.
Admittedly, this archived episode of our live video chat show covers many topics, yet one topic is critical to any discussion about data scientists: Data Science Bias. Watch as Frank Fillmore, founder and president of the Fillmore Group and Thomas Deutsch, IBM Big Data program manager, bat this topic back and forth for a very thorough discourse.
This podcast features Bernard Marr, founder and CEO of the UK-based Advanced Performance Institute, which is one of the world’s leading research and advisory organizations on organizational performance. Listen as he gives advice on how to form a big data analytics strategy, and what to look for in a data scientist.
While 2012 was marked by high expectations and magical aspirations for the potential of data scientists, I’d say that 2013 saw those expectations become more realistic and possibly more demanding—making 2013 truly the Year of the data scientist.