If you’re interesting in becoming a data scientist, here are a few points to guide your career and make yourself stand out in a group of candidates.
Focus on Academics--Before and After School
Data scientists develop hypotheses, test scenarios and analyze their findings on a daily basis. Understanding a variety of mathematical theories, programming languages and advanced algorithms will aid individuals in their pursuit of becoming the next-great data scientist.
“The solution to a problem may be hidden in a particular machine learning algorithm or a traditional statistical model,” says Krishna Gopinathan, COO of Global Analytics Holdings. “Individuals experienced in various domains and working with different problems will be the ones who succeed.”
- Obtain advanced degrees in mathematics, physics, machine learning, computer science, economics, applied mathematics, or relevant disciplines.
- Augment weaknesses in your knowledge of computer programming and statistics with relevant coursework. A well-rounded individuals in terms of academics has the greatest potential as a data scientist.
- After exiting academia, keep up-to-date on relevant research and findings by subscribing to academic journals, such as the IEEE PAMI and the Journal of Machine Learning Research.
Learn the Language of Business
Data scientists are tasked with more than completing advanced reports and wading through data. These individuals will be asked to analyze databases that were once though too large and too incomplete to be managed. At the same time, data scientist have to ensure they don’t get swallowed-up by the data and keep the business’ objectives top-of-mind.
“Working in a commercial environment is just different than academia,” says Michael Griffin, founder and CTO of Adlucent. “You have to be able to produce something that makes a difference very quickly.”
- Learn how to collaborate with others. Project management is one of the most important skills for a successful data scientist, as you’ll commonly collaborate with a team of developers and programmers to accomplish projects. Read about how to effectively delegate tasks and lead teams to accomplish group goals.
- Read as much about personal development and general business principles as you do about research. Thomas Davenport and Jeanne Harris’ “Competing on Analytics” is an excellent start to learning how businesses are using data to beat-out the competition.
Flex Your Data Muscles
Data scientists use a wide array of tools to expertly manipulate Big Data. Jeff Hammerbacher, the former chief data scientist at Facebook, recounted that his team members would utilize Python, R and Hadoop to complete everyday tasks.
“Becoming an effective data scientist is all about playing with the data cards you’re dealt. The more tools you’ve mastered, the stronger your play,” says Gopinathan.
- Utilize online resources to gain more experience using languages and applications that you're most unfamiliar with. The Big Data University website, for example, provides free resources to learn more about MapReduce, Hive, Pig and others.
- Seek out open-source projects to collaborate with others on data projects and network with others within the community.
- Participate in data competitions such as Kaggle to test your skills “in the wild.” Not only can this boost your resume, but winning these competitions often offer cash prizes.