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How I Became a Data Scientist: With Jeremy

There are lots of people out there wondering how to transition from whatever field they’re in now into the exciting world of Data Science, so I thought that I’d throw my hat into the ring and describe how I went about becoming a data scientist. Checkout this video to see more about Jeremy’s life as a Data Scientist.

Jeremy Mitchell, Data Scientist, Data Mettle, Becoming a Data Scientist
Jeremy Mitchell Data Scientist

My Life as a Space Physicist

I started my career as a space physicist. My research was all about trying to figure out how astrophysical shock waves work. Basically, shock waves happen when you’ve got objects traveling faster than the speed of sound (or some other wave). So just like the sonic boom in front of a jet, or the bow wake in front of a boat. The shock wave’s job is to slow the fluid down. On Earth, that’s easy: there are millions of collisions among the atoms and molecules in the air/water/whatever that can slow the fluid down. In space, there are (almost) no collisions, so where do the shock waves come from?

I don’t want to go into answering that too much here. Instead, I’ll talk a little bit about how I studied it. There’s a big shock wave in the solar wind between the Earth and the Sun (because the solar wind is moving so fast, and the Earth is blocking its way). I used a bunch of different spacecraft, each of which crossed over this shock wave from time to time. This meant that sometimes I could see what was happening on different parts of the shock wave at the same time, and see if there were any large scale effects.

The relevant part here is that I needed to get large data sets from the spacecraft, prepare the data, compare the different datasets, and then use them to build physical models. That’s pretty similar to what I do now! The important part here is all the work needed to carefully collect, understand, and calibrate the data. Once I’d done that, I could use the data to build physical and mathematical models. The final step is validating those models, often meaning the process starts again!

Becoming a Data Scientist

How did this help me become a data scientist? Easy. The process is almost exactly the same: I would gather, clean and understand the data, use the data to build models, and then validate the models. Of course, I was now building statistical or machine learning models for marketing or operations optimisation for a large supermarket, but the process was remarkably similar. And just as much fun!

So what skills helped me make the switch? I’d say these (in no particular order):

  1. Lots of programming experience.
  2. Mathematical and statistical modeling knowledge.
  3. Knowing how to handle that much data.

Of course, there are lots of things that are very different too, so I’d add a fourth point

  1. Being open to learning the ropes in a very new environment.

Although this can be a challenge, personally I found it one of the best parts of becoming a data scientist, and it was nice to learn that there are so many interesting problems out there to get our teeth stuck into!

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