How Does An Astronomer Become A Data Scientist?

I have been asked this question by several junior colleagues, so I thought my answer might be valuable to a broader audience. In particular, these young scientists were keen to learn how their skills might be transferable to another field. Now, such career changes are not new of course. When I worked at Steward Observatory in Tucson, one of my colleagues went to work on Wall Street on what is now called Analytics. I even stepped out of astronomy for four years to work on two Earth Sciences missions at Goddard Space Flight Center (and enjoyed the work too).

For early career astronomers looking for advice, I think you can do no better than look at the posts made by Jessica Kirkpatrick, who obtained a PhD in Astronomy and then became a data scientist at Microsoft/Yammer, and I understand she has since taken a position as Director of Data Science at the education start-up InstaEDU.

The term “Data Scientist” is extraordinarily broad. For example, the post “What is a Data Scientist?” describes some of the Data Analyst roles a Data Scientists may play:

  • Derive business insight from data.
  • Work across all teams within an organization.
  • Answer questions using analysis of data.
  • Design and perform experiments and tests.
  • Create forecasts and models.
  • Prioritize which questions and analyses are actionable and valuable.
  • Help teams/executives make data-driven decisions.
  • Communicate results across the company to technical and non-technical people.

Scientists turn out to be good candidates for these kinds of jobs because the skills required overlap with those a scientific researcher.  Some of the advice Jessica gives if you want to pursue a data scientist job is, in summary:

  • Learn a standard language – Python, Ruby, Java, Perl, C++ as well as R. Not IDL!
  • Learn databases – SQL, Hadoop/MapReduce and Hive. 
  • Make sure you can handle complex statistics problems.
  • Tech companies want Resumes not CVs (read the post if you aren’t sure of the difference).
  • Learn to be a good communicator.
  • Tech companies want you to increase their value – lear efficiency and accuracy
  • Do an internship.

You may also find the links at useful.

There are many videos on youTube – here are a couple I liked.




Finally, here is  a long video on “Data Science and Statistics: different worlds?”:

This entry was posted in astroinformatics, Astronomy, Career Advice, careers, computer modeling, computing videos, Data Management, Data mining, Data Science, databases, High performance computing, informatics, information sharing, programming, R, Scientific computing, software engineering, software maintenance, software sustainability, statistical analysis, statistics, Uncategorized and tagged , , , , , , , , , , , , , , , , , . Bookmark the permalink.

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