Data Science is one of the fastest-growing fields.
Here at Factored, we know only too well that the demand for skilled machine learning engineers, data analysts, and data engineers is on the rise.
But how are people entering the Data Science industry? What are the necessary skills and qualifications to do so? Is there no one-size-fits-all formula for becoming a Data Science expert?
While computer science is typically seen as the obvious way to forge a path into Data Science, at Factored we have exceptional engineers and analysts whose studies didn’t resemble computer science in the slightest.
Here’s why a computer science degree isn’t the only way into a successful Data Science career.
The Biological Engineer.
You don’t need to study Computer Science, Math, or other more “typical” routes in order to land a career in Data Science. In fact, Adriana C. studied Biological Engineering.
She figures it was her class on Biomolecular Modeling, where she had to do some programming and which she truly enjoyed, that eventually led to her interest in Data Science. “That was the first time I realized I wanted to do something involving analyzing stuff, finding patterns, evaluating data and variables, and creating a conclusion or decision based on my findings.”
At the beginning of her Data Science career, Adriana migrated out of necessity more than interest. But she knew a bit of Python and that she loved scratching underneath the surface and working with the information she found. It wasn’t until later that she understood that was what data analytics was all about. “Other than that, it was mostly the soft skills I had gathered that took me to where I am today: perseverance, studying lots and fast, being at peace with failure, team work, and being agile.”
When she finally decided to make the change, she looked for every web course out there that could guide her through her journey to becoming a data analyst. Here are a few of the courses she tried:
- Python and SQL courses at Codecademy
- Git courses at Udacity
- Free scrum certification online
- Data analytics with Google courses on Coursera
- Free tableau courses at Udemy
- Data Science concepts with IBM on Coursera
- IBM Data Science certification on Coursera
Adriana feels that her background in engineering was useful as she already understood how algorithms worked. And, at the end of the day, she believes the transition was totally worth it; she loves programming and says that learning Python was nearly therapeutic for her. It has taught her to face her mistakes, deal with frustration, and accept that not everything is going to be perfect from the start.
Laura T. came to Data Science from an academic and research career in physics. After pursuing a Master’s degree in Particle Physics and the Cosmos, she decided to jump into Data Science. Why? Because she saw an opportunity to use the analytical and technical skills she was trained for—like statistics, programming, and analytical thinking—in a field where career opportunities are so much broader than what they are in academia. However, she never lost sight of what she felt most passionate about: spotting patterns and understanding them with data.
The transition was smooth. In physics, she had to perform statistical analyses on a bunch of data to be able to better understand possible hidden patterns in the universe. In Data Science, instead of scientific data, she was now working with business data (financial, marketing, and so on). She found this to be the most challenging part, as she needed to quickly grasp the jargon of each department and understand important business metrics.
Ultimately, she’s glad she made the jump. Not only is Laura doing something she feels passionate about and putting all of her skills to use, she’s doing it in the service of others. Laura feels she’s contributing to a field and community that significantly improves our lives and allows us to better understand not only business, but other areas such as healthcare and science itself.
The Industrial Engineer
While studying to become an Industrial Engineer, Liseth G. took some programming lessons. She found that she really enjoyed them because it was as if she was solving a game or a puzzle.
When it came time to start her professional career, she didn’t know much about Machine Learning, but began to have contact with three related fields:
- Data Science: Her first programming challenge was creating a matrix for planning routes for the Bogotá bus system.
- Business Intelligence: She began working with the Finance team at General Motors to create a tool that helped the decision-making process.
- Statistics: Liseth decided to take a Micro-Master’s course in Statistics and Data Science with MITx.
The convergence of all of these fields and her curiosity about how Artificial Intelligence works provided Liseth with a starting point for her professional path to Data Science. Since each field she worked in up to that point always worked with data—from basic accounting to complex models for optimization—the transition was easy for her.
She’s now completely convinced of the power of data; “It can be used in every industry, and the demand for skilled people is only increasing,” she says. “Nowadays, we are more connected and are creating more data in our daily lives in texts, images, videos, and other formats. Companies need to process that data into something meaningful.”
Furthermore, Liseth believes that data knowledge is not only for companies, but that everyone should have some degree of knowledge about how data is processed; after all, we’re in a data-filled—and data-fuelled—world. If you don’t know anything about data, you could easily lose control of what you want to consume, or even believe.
Laura G. originally majored in Economics and Law. Most people who double majored in the same programs work for consulting agencies doing a mix of legal and financial advice. Especially since Laura came from a family of financial gurus, going into fiscal law and finance made the most sense.
However, by her second year, she was becoming disenchanted with her chosen majors. Motivated by her friends in the Math department, she took an Introduction to Theoretical Mathematics class to balance her very law-heavy semester. She absolutely loved it—so much so that she decided to minor in Theoretical Mathematics. From there, she enrolled in a Statistics class, which eventually put her on the path to Data Science.
Laura’s first brush with Data Science was anything but gentle, however. She didn’t realize the two courses she enrolled for assumed she had previous experience with Python—she hadn’t even seen any code up to that point. Despite the tough transition, it was absolutely worth it. She prepared a portfolio using the projects she had built for previous jobs and started applying to Data Science/analysis positions.
Laura has found that the need for contextual knowledge to build a story around data/metrics is very similar to the kind of argumentation necessary to practice law. The process feels very similar to her; in fact, having been a lawyer helps her build better stories with the data she analyzes. Today, Laura continues to love the challenge of the ever-changing field of Data Science. The demand for continued learning is something that she deeply enjoys, making it a fulfilling career.
No, Computer Science studies aren’t the only way to get into Data Science and enjoy a long and successful career in the field. In fact, in some ways skills learned in other fields can be even more valuable than obviously transferable skills from a computer science degree.
If you’re really enthused about pursuing a career in Data Science, it’s true that mathematics or computer science studies can help you—but they’re not the only path. Reach out to us for more guidance on how you can get on the right track for a fruitful and fascinating Data Science career.
Get in touch with our recruitment team today if you’re interested in pursuing a career in Data Science.