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data science qualification

  • 4 September 2019
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what is the minimum qualification required to be a data scientist ?

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@Alan_H, maybe you have some thoughts on this?
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Hi Shibu!

I work as a data scientist at Coursera and hopefully I can shed a little light on this :)

The real answer is that it depends on what a particular company is after. Some companies have strict requirements for education or experience that is hard to get around. However, many employers are starting to be more open to less traditional backgrounds, so here I will focus on the actual skills that data science jobs typically require:

  1. Experience with real-world data projects. The best way to demonstrate your ability to do the job is to show how you have done it in the past. Past work projects or coursework (e.g. Coursera!) are obviously good for gaining experience, but my advice is to actually get your hands dirty with a real dataset that interests you. Whether it's economics, entertainment, sports, etc., try to take a real world data set and use it to build a model to either make predictions or answer questions. My first big projects with data were using football play-by-play data to create win-probability models. This is not only a great way to hone your skills and create a portfolio (e.g. on github), but also helps you understand if this is a job you will really enjoy doing full-time. Being able to ask creative questions of data is a KEY SKILL that is hard to develop without hands-on experience.
  2. Expertise with a scripting language: Python or R. Almost all data scientists use these technologies and at a minimum it would be good to understand how to manipulate data and do some level of statistical modeling / machine learning
  3. Familiarity with databases. You don't need to be a SQL expert, but most companies store their data in SQL databases and it is important to understand how to access and join data. It's pretty easy to learn the basics
  4. Statistical inference knowledge OR Machine Learning chops. Most data scientists are either very familiar with inference (e.g. understand bias, controlled regression techniques, causal inference) or machine learning (e.g. scikitlearn algorithms, prediction, validation). It's great to have a good understanding of one of these fields so that you can quickly apply the concepts to any number of problems. There are tons of great Coursera courses that teach you these basic fundamentals, and then it's up to you to practice applying them as much as you can!

Hope that was helpful!
--Alan

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