What better way to learn what Data Science is other than to ask Google? Google says, in big bold letters:
Data science is the study of the generalizable extraction of knowledge from data, yet the key word is science.
How cryptic of you, Google. On the other hand, IBM does a slightly better job:
A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytics and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.
What does U.C. Berkeley have to say?
A recent study by the McKinsey Global Institute concludes, “a shortage of the analytical and managerial talent necessary to make the most of Big Data is a significant and pressing challenge (for the U.S.).” The report estimates that there will be four to five million jobs in the U.S. requiring data analysis skills by 2018, and that large numbers of positions will only be filled through training or retraining. The authors also project a need for 1.5 million more managers and analysts with deep analytical and technical skills “who can ask the right questions and consume the results of analysis of big data effectively.”
- What is Data Science? By Mike Loukides: This free Kindle book is a great introduction to the field.
- Big Data by Viktor Mayer-Schönberger: A more comprehensive book if you want something to add to your reading list. We think it errs on the side of being a bit too optimistic (shoot us an e-mail if you’d like to talk about this!), but it is an informative read.
- Computer Science Theory for the Information Age by Hopcroft and Kannan: a discussion of the theoretical aspects of data science, which are often overlooked.