A Summer as a Data Scientist
By: Braden Riggs
Tags: Programming, Python, Technology, Data Science, Internship Experience

What did I learn from these projects?

I learned a range of technical skills, but the non-technical skills are a little more digestible to discuss. Photo by Branko Stancevic on Unsplash

Beyond the ubiquitous skills that everyone working from home had to develop, I picked up a number of skills that are crucial for a data scientist, skills that you just don’t learn in a traditional classroom setting.

I was thoroughly impressed with the level of respect and independence I was offered despite my limited experience and age. The program respected my ability to learn, problem-solve, and adapt, whilst always ensuring I had a helping hand should I need it. This environment was instrumental in helping me learn and grow as a data scientist. It is also one of the key elements of a good internship that many other programs miss. My peers who have, or are currently interning with other companies have often felt claustrophobic and patronized, not offered the level of independence they need to learn and grow within the program. GSI’s internship really breaks the status quo in this way. I was given the responsibility of a full-fledged data scientist to the degree that I was even making decisions that shaped the development and outcome of the project. This encouraged me to learn and grow, but was paced in such a way that I didn’t have to over-exert myself.

Since GSI was able to break the mold encountered in a more conventional internship, I was able to develop and utilize a range of skills vital to the data scientist role. In some cases I was left to solve a variety of problems beyond the scope of my education with the option of a safety net, should I struggle too much. A few of the non-technical skills I picked up included:

Google is your friend : In many cases, I was working on problems and projects out of the scope of my previous experience. This meant I was encountering a slew of issues that I didn’t know how to solve. Since I was encouraged to solve these issues independently, I had to approach these problems in a resourceful and creative way. This is a valuable skill to hone when working in any job or any environment.

: In many cases, I was working on problems and projects out of the scope of my previous experience. This meant I was encountering a slew of issues that I didn’t know how to solve. Since I was encouraged to solve these issues independently, I had to approach these problems in a resourceful and creative way. This is a valuable skill to hone when working in any job or any environment. Communication is key : One of the key skills a data scientist must have is the ability to communicate. In my case, I had to learn how to quickly and concisely communicate issues whilst also conveying key details to a variety of colleagues. I was encouraged to write these medium articles as a way to refine this skill.

: One of the key skills a data scientist must have is the ability to communicate. In my case, I had to learn how to quickly and concisely communicate issues whilst also conveying key details to a variety of colleagues. I was encouraged to write these medium articles as a way to refine this skill. Keep things straightforward : Some problems I encountered could be solved in a variety of ways. The challenge was picking the solution that was both simple to explain and effective. In many cases, I or others would be returning to the code that I had written weeks or months later. The portions of the project that included simple and intuitive code helped us solve problems faster and more effectively.

: Some problems I encountered could be solved in a variety of ways. The challenge was picking the solution that was both simple to explain and effective. In many cases, I or others would be returning to the code that I had written weeks or months later. The portions of the project that included simple and intuitive code helped us solve problems faster and more effectively. Simplify the problem : On occasion, parts of various projects would break. Going back to troubleshoot these problems was always difficult as there were many moving parts and things that could go wrong. One strategy George encouraged me to use was to approach these problems one step at a time rather than try to tackle the whole suite of issues at once. Whilst this sounds intuitive, there were many times when I had to catch myself and focus on incremental improvements rather than fixing everything at once. This strategy ensures that you aren’t overwhelmed when solving a problem and often leads to more elegant and robust solutions as opposed to a jury-rigged solution.

: On occasion, parts of various projects would break. Going back to troubleshoot these problems was always difficult as there were many moving parts and things that could go wrong. One strategy George encouraged me to use was to approach these problems one step at a time rather than try to tackle the whole suite of issues at once. Whilst this sounds intuitive, there were many times when I had to catch myself and focus on incremental improvements rather than fixing everything at once. This strategy ensures that you aren’t overwhelmed when solving a problem and often leads to more elegant and robust solutions as opposed to a jury-rigged solution. Enjoy what you do: During the summer this was my full-time job, and my first full-time salaried job. I learned that enjoying your work goes a very long way towards improving the quality of your experience, your productivity, and overall well being. It is hard to find out what you like to do, especially in such a multifaceted field like data science, but it is important to do so because it will be a significant portion of your experience.

As I have clearly expressed, I have thoroughly enjoyed my time working at GSI and working as a data scientist. I wanted to write this blog to reflect on just how lucky I am to have had this amazing opportunity and to summarize the things that I have learned and accomplished over the past three months. I think it is important to retrospectively dwell on experiences such as this as it helps solidify the skills I have picked up and the experiences I’ve had. Using this adventure as a guideline, I feel much more confident about entering the professional world, following my graduation this upcoming June. I have a better understanding of the topics I want to further explore this academic year and the areas of data science that I am going to be interested in pursuing further. Whilst I am sure every university student aims to have a summer internship, I would encourage any college-level readers to strive that extra bit harder to attain one as there are a plethora of skills and experiences that you just can't find within the classroom.

If you are interested in reading more of my work or learning about GSI Technology, I’ve included some links below to check out. Furthermore, feel free to follow me if you enjoy my writing as I will be diving into more data science topics over the next few months.