Do a search for “data scientist” in a job search engine, and you’ll find the title is one of the hottest in IT today. With Indeed.com reporting 15000% growth for the role, is the sky the limit for these so-called data scientists?
According to the Harvard Business Review–yes. Thomas Davenport and D.J. Patil deemed “data scientist” as the sexiest job for the 21st century in the October 2012 edition of the HBR Magazine. The two authors equate data scientists to the quants of Wall Street in the 80s and 90s and the search engineers of the 2000s. Meaning these highly analytical, academically-trained programmers and innovators will be tasked with managing the next-great data frontier–website data, social media data, and information that’s all-around pretty darn…Big.
So what does it take to become a data scientist? My colleague Michael Koploy recently interviewed three data professionals to glean their career advice for aspiring data savants. They suggested the following:
- Focus on gaining advanced degrees in any mathematics-focused field, such as (but not limited to): applied mathematics, machine learning, computer science, physics, MIS, or econometrics.
- Even after your academic career ends, continue to keep up-to-date on research within computer science and mathematics. There are a number of great journals to read, such as the IEE PAMI or the Journal of Machine Learning Research.
- Successful transition from academia to the business world is often correlated to your aptitude in non-technical skills, including project management, understanding the business’ priorities and effective communication.
- Learn how to effectively delegate tasks, work alongside programmers (rather than hard-coding everything yourself), and lead teams as well as you can manipulate a 300 terabyte database.
- Read books about business. They can be focused on management, business success, or even history, but just attempt to gain a better understanding of how your industry works and what it takes to be successful against competition. A book such as Davenport and Jeanne Harris’ Competing on Analytics is a great start, as the resource famously ushered in the Era of Analytics.
- Continually learn new technologies, languages and skills. Data scientists commonly use an assortment of technology system and programming languages to conduct their everyday tasks, so a weakness in your toolbelt is a decided disadvantage. There are a number of online resource, such as Big Data University, that provide free courses to help you gain more tools.
- Participate in open-source projects and competitions to test your data manipulation and visualization skills in the real world. Kaggle, for example, allows participants to compete for both respect and cash among the data community. These competitions and projects are crucial to continual development of skills, and learning how to apply to them to business’ data problems today.