This article was first posted by Carla Gentry here. Carla is currently owner and data scientist at Analytical Solution.She has worked in the field of data science for over 15 years, and has worked for Fortune 100 and 500 companies such as Johnson & Johnson, Hershey, Kraft, Kellogg’s and Firestone. She is today ranked amongst the top 10 Big Data Pros to follow on Twitter
I did a webinar with Kalido and enjoyed it tremendously, they were kind enough to give me a summary of the webinar, thanks Kalido http://www.kalido.com/
My Favorite Quote from the Webinar “Big Data needs Data Science but Data Science doesn’t need Big Data” Carla Gentry aka @data_nerd
Data science has been around for decades, and it’s not just big data. I hear a lot of people clumping these two together like they go hand-in-hand, which I agree with to an extent. However, big data needs data science but data science doesn’t necessarily need big data. Most of the data a typical company handles on a daily basis or house internally is not big data. Even Facebook and Google break up or segment their data into workable pieces. Data science is big, small, structured, unstructured, messy, clean, etc… It’s more than just analytics. As a data scientist, you’ll become a liaison between the IT department and the C suite. You have to talk both languages and you have to understand the hierarchy of data, you can’t be just an architect or data expert.
What really matters in data science is the team effort and your role as a liaison. Your company has large amounts of data and you want to make sure your queries are correct. Whatever tool you use, make sure you have your data cleansed. You want to know that it’s normalized and indexed so that things run smoother. You want to be able to give insight, which requires knowledge of your audience. If your audience is the C suite of a multi-million dollar company, you’re going to need everything you have to back up your conclusions. Be able to prove it and be prepared for questions.
What sort of personality makes for an effective data scientist?
Definitely curiosity, I remember in college, my professors shut the door if they saw me coming because telling me that a2 + b2 = C2 was never enough. I wanted to know why. So the biggest question in data science is “why?” Why is this happening? If you notice that there’s a pattern, ask “why?” Is there something wrong with the data or is this an actual pattern going on? Can we conclude anything from this pattern? A natural curiosity will definitely give you a good foundation.
For aspiring data scientists, where can they begin?
There are many positions you can get into to learn data science; it’s not just for data engineers. Personally, I started as a junior analyst. Everyone has to start at the ground floor but there are so many resources and open-source data places you can go to practice. Most IT departments aren’t going to give you access to their live database, but they may give you access to their development database where you can go in and practice. Any position that you get into, go tell your boss that you’re interested in becoming a data scientist. Sign up for courses, learn programming languages and learn business. You have to know about budgets and various business aspects, not just the analysis part and not just the IT part. Data science is a wonderful field, and I encourage anyone that has a curiosity about data analysis, hypothesizing, statistics, to give it a shot. Just know that it won’t happen overnight.
Carla Gentry is the owner and chief data scientist for Analytical Solution. Analytical Solution was founded with the aim of aiding companies without their own designated analytics department, and who need analysts on a per-project contract.
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Really like this distinction. I’m finding this article a year later, but it’s still true, and it’s less contrary to prominent messages now that the term ‘big data’ itself has cooled off. You also hint here at the need for interdisciplinary literacy to be a fully functional data scientist, and this is something I’m encouraged to hear more and more. Computers obviated file clerks, but they have forced every other role in business to look at their role differently. Not only is data science crucial to business, it’s fundamental. As we can collect and store information more completely, the process of making competitive decisions becomes much more rigorous. Would love your feedback on the API in my link. Great post.
Great tips. Really like your practical advice and personal stories. Friends at school used to call me Mr. Why 🙂