Sarah is transitionig into data science after ten years as an educator.

“Last year I started thinking long-term - what do I want? To stay in teaching, switch to administration? The result was I knew I didn’t want to continue in the education space. I loved the classroom but wanted a change.

I had done some Computer Science in high school and fell back in love with coding, and did intro to data analysis and loved the data visualization, math and functions. UVA [University of Virginia] didn’t have data science when I was there, and I’ve needed a lot of experience and exploration to identify gaps and determine where to go. My takeaway? You need to do everything! Bootcamps, online code academies, coursera – nothing is perfect but they all brought something else to the table.“

I was delighted to come across a LinkedIn posting several months ago from Sarah mentioning her transition into data science after ten years as an educator, mostly teaching middle school math.  First up was excitement - both for Sarah to enter a field that’s been so enriching for me personally, and offers so much to those who build the right skills - and for the practice to have another talented, thoughtful practitioner (one with an educator’s eye to boot).  The numbers vary but it’s quite clear - we need people who can do this work.  The more, the merrier.

Second up was curiousity.  I very fortunately began doing analysis work in college, and have seen plenty of folks grow into data-centric roles from STEM backgrounds as organizational needs have demanded it, and personal interests and aptitude enabled it.  What I didn’t have insight to was the journey of a larger transition.  With vibrancy and humor Sarah walked me through her experience of the data science journey.  It looks like the wild west out there.  The term “drinking out of a firehose” comes to mind.  It also looks like an environment where there’s a bounty of quality educational content and people willing to lend a hand.  An environment where if you’re able to put in the time and have the aptitude, you can build some real skills.


Practical Statistics for Data Scientists;
Andrew Bruce, Peter Bruce

Python for Data Analysis;
Wes McKinney


  • Masters in Teaching; MFA, Creative Writing
  • ~10 Years Teaching Middle School
  • Code Academy, Intro to Data Analysis; Data Science Specialization Course, University of Michigan and Coursera
  • Python, pandas, SQL, Jupyter Notebook;
  • Github: @shhudspeth -

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