My day tends to have three main focuses: sprint work, project planning/road mapping, and research.
Sprint Work — This work refers to most of the items I take on during a sprint. Sprint work can include fixing a bug in code, creating new functionality in a library, or running an analysis on some data based on customer input on an issue. Sprint work is my planned three weeks’ worth of work.
Project Planning and Road Mapping — I am the team lead for my group, and therefore spend time cleaning our backlog of work, adding new concepts, and road mapping what the months ahead look like. During this time, I am working with stakeholders and other team members to add information where needed and roadmap the next few months.
Research — If I am not doing one of the two above, then I am researching what is in the market, new techniques that have come out, and ways to solve problems that we are currently facing. As mentioned previously, data science is a revolving door of new ideas, concepts, and teachings. You will spend a lot of your time learning what has come out and if it applies to your work or not. My research can be either part of my sprint work or project planning. It depends on what I am doing that week.
Lesson: Your day will vary depending on the type of role you pick. Make sure you choose something that suits what you want to be doing in your position.
I didn’t initially choose data science; it chose me. I began college as an English major and studied for two years as one before transferring to another college and becoming a computer engineer. It was during my final year of undergraduate that I began to explore my love for data. I worked on a research project that required me to ingest data from sensors and user input for analysis. It was then that I thought I was initially interested in becoming an embedded engineer. I explored the idea for a while, took a few more classes for embedded programming and chip design, and then did an internship.
The more I got into looking at data for embedded system, the less I wanted to become an embedded engineer. Then I began my master’s in data science. As I took more classes and worked with different datasets, I began to enjoy big data challenges. Data science is an evolving field that lets you interact with people of all backgrounds. It offers new challenges each day, and with additional data comes additional problems to solve. I didn’t initially choose data science, it chose me, and I am glad it did.
Lesson: Life is not a straight forward path. There will be many twists and turns in it. You may not know where you will end up, and that is okay.