By Katherine Bernstein
As a second year math and computer science major at Boston University, I have taken a few computer science, calculus and statistics courses. While I find many of my classes fascinating (some much more than others), I always wonder in the back of my mind, “how could this possibly be used in the real world?”
While I desperately wanted some sort of job experience in one or both of those fields, I had convinced myself that I didn’t know enough yet to really contribute to, or land a related job. For this reason, I wasn’t planning on applying for summer internships.
Despite my doubts, I continued to look for an internship because I wanted to not only “get my foot in the door,” but I also wanted to have a clearer viewpoint into what kind of work I might want to pursue after college.
I reached out to a family friend who is a computer programmer, and asked if he knew of any internships that I could potentially apply for as a math and computer science major. He told me about data science, an interdisciplinary field that involves data inference, algorithm development, and computer programming in order to solve analytically complex problems. As someone who enjoys puzzles, statistics, and coding, data science greatly stood out to me as a subject I was eager to learn more about. He generously gave me a list of companies with in-house data science departments, including DialogTech. After sending out my resume and cover letter, I was more than pleased to receive an email from Tim Hoolihan, Sr. Director of Data Science and Analytics at DialogTech, asking for a phone interview.
I had many different goals for my summer internship. I wanted to learn what it is like to work in an office environment, have more experience coding and working with new Python libraries, truly understand what data science is and how it fits into DialogTech, and feel like I am able to contribute to the company and accomplish something big.
When I first got to DialogTech, I was slightly overwhelmed with an abundance of new information: platforms, libraries, and terms that I was very unfamiliar with. During my first week, I will never forget how I struggled with cloning and creating a new repository on GitHub. I was so determined to get it right without asking for help that I kept guessing, which got me into a deeper and deeper mess. When I finally asked my coworker for help, it took us almost an hour to get things back to the way they were before I started. From then on, I learned to not hesitate to ask my coworkers for help when I needed it. They were always eager to help and give me suggestions, in a way that challenged me to figure things out on my own as well.
One of the best parts about working with the data science team was that I was able to participate in all of the team meetings; from our daily stand-up meetings, to code share, to more general meetings where we discussed the team’s next steps. I got to hear the ins and outs of how the different models were implemented, as well as what they did and how they were going to be utilized by the company.
About a month into my internship, I attended a week long orientation training. While each training session was not directly connected to data science, it was interesting to understand what each department in the company did. Learning about marketing strategies and competitors gave me a new perspective into the company’s goals, and how data science and analytics as a whole is a central component. This gave me a newfound appreciation for the data science team. It also impassioned my responsibility of creating tutorials for DialogTech employees interested in data science, and introducing them to some of the concepts that create game changing models.
Over the course of my internship, I learned many machine learning techniques. I learned how to visualize data, perform topic modeling on transcripts, use a plethora of different Python libraries, receive data using ElasticSearch and S3 in AWS, create a machine learning classification model and analyze its accuracy, and much more.
A large part of my work this summer involved doing research on as many topics as I could. There were many times when I was overwhelmed by constantly hearing terms I did not understand. I realized quickly, however, that everything seemed a lot more difficult and unattainable until I knew what it was. As the summer unraveled, I felt like I was putting more and more pieces of the data science puzzle together. Writing tutorials on topics like data visualization, word processing, and simple classification models, I was able to understand why each method was important and how to use it. Words like “GridSearch,” “NLP,” “ElasticSearch,” “AWS,” “GPU,” and “Pipeline” finally started to make sense to me.
The most important lesson I learned during this internship experience is that it isn’t always about what you know going into the internship, but about how willing you are to learn something new and work to understand it. Whenever I had a hard time implementing a method, I was tempted to ask for help straight away rather than working it out myself. Spending time trying to figure things out on my own or by using online resources forced me to really understand what every line of code in my models were doing. This self-reliance, combined with my previously mentioned experience with GitHub, taught me why teams are important — everyone brings a strength to the table.
Thanks DialogTech! I enjoyed my time here very much and had a lot of fun learning about data science and machine learning. DialogTech has an extremely inviting and welcoming culture, and the company was a pleasure to work for. I was extremely grateful that I was given the responsibilities and treatment of any other employee. It was a great internship, especially thanks to my team!
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