If you have a Masters or PhD in Economics and are looking for a career in Data Science, you have come to the right place. We love it when Economists come through our Data Incubator because we know they have the skillset to succeed. For example, Blake Boswell (right) did his Master’s in Economics at Johns Hopkins University, completed the Data Science Fellowship at The Data Incubator and now works at Cornerstone Research.
We’ve found that Economists have extensive training in articulating complex ideas, something students from other disciplines can oftentimes lack. We can give Economics students a fuzzy question and they answer it with Computer and Data Science, and then convert it back into comprehensible words a non data expert could grasp. This is a very important skill to have.
Most data scientists don’t approach problems like Econometricians. In data science there is no unifying theory, the goal is to predict outcomes given the data – not to use data to estimate model parameters as Econometricians do. Both approaches have their merits, but predictions take precedent in industry. Nonetheless, your training as an Economist will help you to avoid drawing some inappropriate conclusions from data, where many Data Scientists wouldn’t think through to how the deep structural changes can undermine predictions.
3 Building Blocks To Being A Great Data Scientist
- Engineering – Many econometric classes teach how to deal with small data sets (in terms of number of observations and features), but modern data science requires you to be adept with large data sets (both wide and long data) and this requires strong engineering chops. An ability to write code is imperative, and we recommend learning Scraping, SQL, Data Frames, Machine-Learning, and Visualisation.
- Maths and statistics – Econometrics cares about inference, whereas a lot (though not all) data science applications care about prediction. It is a different set of intuitions and we recommend learning both. Econometrics specifically teaches how to view data and results through a hyper-critical lens, which can be very helpful in the field, however it doesn’t teach the data processing skills necessary to be a good data scientist. These you will need to learn.
- Common business sense – To analyze and understand the data in the most effective applications for the company you work for, you need to have a grasp of the company itself. This doesn’t mean you need an MBA, but common sense in business practice is important.
More Resources To Help You Prepare
- As a Data Scientist you will need to be well versed at efficient numerical computation. To learn more on this, read our article to get high level overview of this skillset.
- Here is a more general guide for anyone looking to kickstart their data science career, which we recommend you check out.
- And if you’re curious to learn more about what is involved in being a data scientist, click through to read about the task we give our Fellows to build and deploy data science apps to prove to employers that they’re ready for the industry.