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From Teaching to Data Science

How and Why I Transitioned from Teaching Elementary School to Data Science

Photo by Author
Photo by Author

Be What You Want to Be

When I first started browsing career choices, I almost immediately turned away from Data Science because of the requirements listed in the job postings:

  • Coding Experience in Python, SQL and/or R
  • Masters or a PhD in computer science, engineering, math, or statistics
  • 5+ years of experience as a Data Scientist

At the time, my coding skills were super limited, my Masters degree had nothing to do with data science, and my career experience revolved around fighting inequities for Black and Brown children as an elementary school teacher. Already buried in student loan debt, getting another Masters degree did not seem like the best approach. I deemed a career in data science a far-fetched dream.

But one afternoon, while wrapping up our Social Studies unit on "Barrier Breakers", like Katherine Johnson, Misty Copeland, and Sonia Sotomayor, the lesson’s objective I planned for my students echoed back to me, boldly chanting:

"You can be whatever you want to be."

I loved Teaching, and I loved the purpose behind it. But I also wanted to explore ways to make a difference beyond my classroom. Already exposed to the impact data had on closing gaps in reading and math skills for my students, I was eager to use data science as a powerful tool to make a change.

Though I did not qualify for a data science job just yet, I had to believe in myself and put in the work, similar to the barrier breakers that came before me. Here’s how I used my experience as a teacher and gained additional technical skills needed to transition over to a field in data science.

Transferrable Skills

A great teacher wears many hats on a given day- we are entertainers, counselors, reporters, professional shoe-tiers, just to name a few. Because we do everything, I thought of ways my teaching experience can boost my candidacy amongst the pool of data scientists. Below is a list of a couple of teaching skills that transfer over to the data science profession.

  • Presenting to a non-technical audience – Teachers and data scientists alike must communicate information in a clear and concise manner to an audience that’s well-versed in topics outside of data science, may it be Roblox or ROI’s. I spent the majority of my day breaking down concepts, like fractions, to 6-year olds. If my first graders got excited sharing how they "partitioned their pancakes into halves, thirds and fourths" on their free time in the cafeteria, I feel confident that I can talk through the relevance of a z-score with business stakeholders.
  • Math Knowledge – Though elementary, a large chunk of my day revolved around math, including data collecting and analysis. My favorite math unit, hands down, has to be the first unit in the 3rd grade curriculum, "Data and my Class Community." In the first six weeks of school, my students learned how to collect, graph and interpret data. Each group came up with a question, something they wanted to learn about their classmates. They went around collecting that data, created a bar graph or pictograph based on the data they collected, then analyzed their results. At the end of the unit, not only did my students learn what the class’s favorite magical creature was, they learned how math is relevant to them.
3rd Grade "Data and my Class Community" Projects - Photo by Author
3rd Grade "Data and my Class Community" Projects – Photo by Author
  • Data-Driven Analysis -The primary reason why I wanted to enter the field of data science: to use data to make better decisions. Prior to every unit, we assessed what students already knew in reading, writing and math. Based on those assessments, we figured out which topics we’d need to cover more in depth, or what we could quickly review. For students falling far behind, we held small group interventions and targeted the skills they had yet to acquire. Because we used data, we were able to monitor their progress and expedite their growth. I’m so proud to say that every student falling behind in reading and math got to grade level by the end of the year, and students already on grade level came out even stronger than before.

Gaining New Knowledge

Though a teacher wears many hats, coding is not one of them. In order for me to successfully transition to a career in data science, I needed to develop my technical skills. I started off small, first testing to see if a career in data science was right for me. Then the more I learned, the more invested I became. I listed out my steps below, and I would definitely recommend going in this order too, given the time and financial investment for each.

  • Codecademy, $40/month – Codecademy’s Data Science Career Track built my foundation for data science, it’s where I went immediately after attending data science information sessions. I had NO coding experience at all, but Codecademy walked me through how to code in python and SQL, then gave me the opportunity to write the code out myself. It could seem elementary at first, but I really appreciate the simplicity of its curriculum. Once I gained enough coding skills, the site presented projects that put my coding skills to the test. I will admit too that I still refer to Codecademy even now for refreshers or when I’m introduced to something new.
  • Community College, free potentially, but definitely cheaper than a university— I personally paid $800 because I lived right outside San Francisco’s border line. Taking classes at City College of San Francisco filled a lot of knowledge gaps and gave me a nice refresher on topics essential to data science. In one semester, I took Multivariable Calculus, Linear Algebra, and Statistics and Probability. It was a challenging, math-loaded semester, but fun nonetheless. If you plan on joining a bootcamp, I especially suggest taking the math courses in advance if you haven’t already. The bootcamp’s curriculum covers a semester’s worth of content in one day. It doesn’t give the learner enough time to digest the material. If your local community college offers other data science related classes too, I definitely would recommend taking those as well. The more you learn, the more prepared you are.
  • Bootcamp, $15,000 – If you’re considering a career in data science and have no technical background, I would definitely recommend joining a bootcamp. It is a big financial investment, yes, but a bootcamp helps you develop the skills you need for a career in data science, and offers the support you need to do so. I’m currently enrolled in Flatiron’s part-time online program. At first, I was worried because I learn so much better in person, but the online program still offers that community feel, which helped a lot. At flatiron, you’ll have 5 projects that you can add to your profile. These projects are great because you gain so much experience from it- you’re coding, analyzing results, presenting analysis and recommendations to business stakeholders, and presenting your code to a technical audience. You’re gaining hands-on experience as a data scientist, which you can talk about to potential employers when the time comes.
  • Internship, free – At the time of writing this, I’m just starting my internship at Apple as an MVT Data Science Analyst. I will say everything I’ve mentioned above, both transferrable skills and new knowledge, helped prep me for my interview process. I’m excited to use what I learned through my bootcamp and Codecademy to build and create models that can be applied to real world problems. An internship can also be a great networking opportunity. I’ll write more about this later as my journey continues.

Conclusion

Transitioning out of teaching and into data science has been a long journey, but a thrilling one to say the least. I’ve been able to gain a lot of skills through Codecademy, community college, and my bootcamp, and know that my skillset will only grow with more experience. Although I miss teaching from time to time, I’m excited to use my data science skills to work on projects that make a difference.

So for those of you that don’t have a technical background, don’t fret. If there’s a will, there’s a way. Just remember, you can be whatever you want to be.

If you’re transitioning into the data science field, I’d love to know, how did you use your experience to help you in your journey?


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