Starting a career in data science

December 29, 2015
By

(This article was first published on R on Rob J Hyndman, and kindly contributed to R-bloggers)

I received this email from one of my undergraduate students:

I’m writing to you asking for advice on how to start a career in Data Science. Other professions seem a bit more straight forward, in that accountants for example simply look for Internships and ways into companies from there. From my understanding, the nature of careers in data science seem to be on a project-to-project basis. I’m not sure how to get my foot stuck in the door.

I am expecting to finish degree by Semester 1 2016. In my job searching so far, I have only encountered positions which require 3+ years of previous data analysis experience and have not seen any “entry-level” data analysis positions or graduate data positions. What is the nature of entry level recruitment in this industry?

Any help would be greatly appreciated.

Regards,
Aran

Here is my reply.

Hi Aran. The best thing to do is go to the data science meetups and talk to people in the industry. (See this blog post.) The Melbourne data science meetup group has about 3000 members and they run a job board.

Also, just do a google search for “Data Science Jobs in Melbourne”. Even if the employers say they want experience, in practice there is a shortage of people available so they often have to settle for someone with less experience.

LinkedIn lets you filter on experience. For example, here are entry level data science jobs in Melbourne. Because different names are used, you should also search for statistician, data analyst, data mining, machine learning, analytics, etc.

One great way to get experience is to compete in a few kaggle competitions. That way you can prove you have the skills before you get offered a job.

Another thing that will help is having completed (or at least started) the Coursera data science specialization. That will fill in some gaps in your training, especially on the computing side. (For people with computer science backgrounds, it will help fill in the statistical modelling gaps.)

Good luck!

If any readers have some further advice, please add comments below.

To leave a comment for the author, please follow the link and comment on their blog: R on Rob J Hyndman.

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