As an Accounts Payable intern, most of my days are spent in the main office. Because the coop uses their own accounting system, there was a bit of a learning curve, but by week seven I can pretty much be self-sufficient with anything that needs to be done.
For my first project, I spent about a week and a half on three spreadsheets linking account numbers to meter numbers and profit centers. I created these spreadsheets for utilities, phone, and internet. Hopefully these will create an efficiency boost because they were extremely time-consuming to link, but we needed to spend time creating it. From my understanding, this will help speed up the process of coding reoccurring expenses at the beginning of the month so that our month end can be closed in a more timely fashion.
My internship is different from the rest of the interns because as a high school student, I’m not necessarily trying to get a job right now, but instead exploring and building an understanding of the financial and analytical side of a successful business. To better understand how data is used, I am working this summer on the entire process, from collecting data to applying it. I’ve started the process with data collection on Tuesday mornings. I collect tissue samples from two fields every week.
I recently met with Branden Collingsworth, the director of the data science team. This really opened my eyes to how related math and coding are. Branden explained that without coding, the amount of data open to us is just numbers and it’s too much for people to comprehend. There has to be a way to organize the data into a format that is understandable or that creates the ability to see trends and patterns. The possibilities with coding online are almost endless.
Branden showed me tons of free online resources to learn how to code. He recommended the program “r” just because of the amount of resources and the community associated with “r” that can help beginners understand coding for themselves. There were multiple ways to learn including a book that I ordered along with online tutorials. There was a massive list of pre-made coding segments that just need data inputs and minor tweaks to get exactly what you need. Although he recommended “r” there are people on his team that don’t use that language, the people that don’t use “r” use python, which he said is a good option too. His overall message was just to learn how to code. If you have the ability to code, it opens up so much more for you, especially in the data science field.