This is a continuation of our short series ‘Why I Became Involved in Data Science’. Click here to read the Part 1 stories.
Saba Teserra, Data Scientist
I guess I always liked to find the structure in a mess, although early on I thought that meant matching strings and counting words. Somewhere along the way, I learned more about the distribution of words in text and their linguistic significance to syntax and semantics; that made unstructured natural-language data processing all the more interesting to me.
I probably would have been a physicist or mathematician, had I not been concerned about the scarcity of jobs in those fields. But data processing is no less interesting. I started my journey as an information scientist at Addis Ababa University; after I earned a Masters of Science in Information Science, the university hired me as a lecturer. I was involved in teaching, research, and project supervision in the areas of software engineering and information retrieval; the latter was more interesting to me.
Systems that answer questions and simplify data visibility—such as classification, clustering systems, question answering, and noisy-channel systems—fascinate me. Determining the distance and proximity of data, overlaps and fuzziness, direct and tangential relations… these are all like art.
Because of my engineering background, numbers were not a problem. But I did stumble over natural languages, those intricate, perplexing and sometimes-paradoxical products of human mind; they seem easy when you speak or hear them, but when you analyze them, they are hard and opaque. For this, I decided, I need a doctorate. And I went for it, with a computational linguistics program in Germany.
Upon completing my degree, I flip-flopped on the idea of working in academia. Though I worked as a visiting scholar here and there, I knew I had to go into industry, if I wanted to choose where I lived. Today, I am in the midst of Silicon Valley, doing data science.
Colby Pettit, Senior Data Engineer
My passion for technology began while I was in elementary school during the 1990s. Every other day our class had computer lab time, where we practiced keyboarding skills and played educational games such as The Oregon Trail, Reader Rabbit, and Super Solvers OutNumbered. For me, computer lab was the best thing next to recess. This experience opened up a whole new world for me of learning math, science, history, and other subjects. I always considered this “fun learning,” since it was exciting, interactive and greatly provided intrinsic motivation.
Over the years and through experience I became more knowledgeable in technology. I learned not only the programs, but also the machines they operated on. I began troubleshooting my family’s computers whenever they had technical issues. Through trial and error I learned so much that I was a walking Google Search engine bursting with technical information. Soon after that, I began teaching and sharing this information with everyone around me; I found I was passionate about teaching people, helping them not only with technical issues but also providing knowledge they could use to become more self-sufficient when dealing with technology.
Several years later, while in college, I was introduced to the world of data warehousing (DW) and business intelligence (BI). At the time, I wasn’t exactly sure what those subjects truly meant, nor the value they could provide. Although I learned about them in class, I couldn’t apply the knowledge until I started an internship as a BI developer; by applying DW/BI solutions and gaining real-world experience, I realized I was doing what I had always enjoyed doing: making those around me smarter and more informed.
After college I engaged in several contracts with organizations that exposed me to different industries, professionals, and technology. Through those, I was introduced to the fields of data engineering and data science, and discovered I could take data to the next level and transform it into something even more meaningful and actionable.
Data science is aligned with my career goals and personal enjoyment. I’ve always been passionate about technology, and being able to educate and inform when it comes to a specific business question feels like my true calling.
Advice for Becoming a Data Scientist
By Simon Hughes, Chief Data Scientist
As you can see, there are many paths to becoming a data scientist. You can take a master’s course in data science and predictive analytics at a local university, or attend a data-science boot camp if offered in your area. Or, alternatively, try to develop the role within your current organization by taking on some data science-related side projects. We’ll cover this more in detail in a follow-up article.