I was the same way starting my first Data Science project. I was given an outline, reviewed what was required for the end result (i.e. Github repo, slideshow, visualizations, and oral presentation of findings) and attended most of my study groups/office hours to make sure I was as prepared as best I could be. It seemed a little overwhelming at first; especially for someone not coming from any technical background. You will be told countless times while you’re progressing through the rubric that Google is your best friend, apparently nobody likes asking Jeeves about anything anymore. I thought, “Yeah-I’ll probably have to look up a random code I haven’t seen yet, but I’ve gone through all the lessons and feel pretty confident with my comprehension on the readings and understand the majority of the code and what it’s saying.” I was sure wrong! Completely overwhelmed, I searched for anything and everything; from basic for loops, to how to write up a Seaborn code to have a fancy interactive graph. I must of had ten tabs open at all times, sifting back and forth between my notebook and articles I found online. There’s so much to learn using Python and it’s always changing and evolving to become and remain the powerhouse of languages for Data Scientists.
This career change came about during the pandemic of 2020 soon after I lost my job of four years in sales and customer service. I was having a difficult time find an opportunity that peaked my interest, so there came a point where I had to start thinking outside what I was used to. I came across this application, Career Karma, developed by a few men in the computer science industry, that wanted a platform to give back to the digital world of current and up-coming engineers and developers. It was completely free so I figured, “Why not try this out and see what happens?”. This platform introduced me to other free apps like Grasshopper, Mimo, Sololearn, and Codecademy. These apps helped me get a basic understanding of such things as Python, SQL, and HTML. Career Karma also paired me up with a mentor who had either recently gone through a bootcamp similar to Flatiron School or was graduating soon and they helped me understand different career paths (i.e. Software Engineering, UI/UX, Cyber Security, and Data Science). At first, I was leaning towards software engineering, but after having a few Zoom meetings with my mentor, she encouraged me to look further into data science due to my comfort with arithmetic. She advised me to focus on python mainly because this language is used primarily within data science and not to worry about anything else at the time. Advice I now understand, as if you’re someone just dipping their toes into the computer science pool, you may be overwhelmed with all the languages.