5 Tips for Getting Started as a Data Scientist

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startby Angela Guess

Roger Huang recently wrote in Dataconomy, “Over the past year, we’ve talked to a lot of aspiring data scientists. The one question we hear a lot is: ‘I’m an X major who wants to be a Data Scientist. Where do I start?’ So we’re sharing 5 important tips that will help you get on the path to Data Science excellence (and with some further reading on each topic). If you have your eyes set on the Data Science job market, these tips are crucial. (1) Develop an area of technical analytic expertise. Start with a solid foundation in statistics. Once you’ve built this expertise, learning Advanced Statistics, Machine Learning, or Natural Language Processing could come in handy. If you are still in school, take courses in these subjects. If not, develop expertise in any one of these areas, and try to be conversant in a few others.”

Huang continues, “(2) Build an affinity for code. Hacking skills might be even more important than formal systems development here. As an entry-level Data Scientist, a lot of your work will be to take lousy data and put it in a form that can be analysed. And it will be different with each data set you work on. Learning Python or R will serve you well for multiple Data Science applications… (3) Learn to tell a story (with data). First, learn basic statistics. Second, be able to express your brilliant analysis in a way that normal people can understand. Your clients and colleagues won’t always understand what terms like “p-value” mean. You need to properly explain your results, their significance and their credibility (why SHOULD someone trust them) in a way that is straightforward and easy to understand for non-data scientists. Visualization techniques can be helpful in these instances.”

Read more here.

Photo credit: Flickr/ tammikibler

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