I’ve just now finished my first week in the Data Science Immersive, and am pleasantly surprised with the experience. Since I am mostly self-taught in Python and data analytics, I was worried I would fall behind. Luckily, my instructors, student success leader, and Outcomes coach have been very helpful in setting me up for success.
I am enjoying completing the labs because while they are a bit tedious at times, the repetitive nature of the questions allows me to retain more than I would have otherwise from just a lecture or group work.
My biggest worry when joining the course was that Zoom would be a hassle and not conducive to learning. This worry is basically completely dispelled because of the very intuitive use of break rooms and Slack, which I have always been a huge proponent of back when I was on the board of a student organization in college, so it was nice to see some familiar apps and the new features that I can take advantage of.
I am very worried about my first quiz since I ran out of time and panicked on almost all of the questions. It was mostly frustrating that a line of code on the quiz did not work even though a very similar line of code had worked in the ladder challenge lab. I wish I had more time to troubleshoot, which also means I am probably spending more time than I should on difficult questions in the labs. While efficiency is good, I have to get used to working on a stricter deadline.
In today’s lesson on creating different types of visualizations, it was interesting to see how toggling one variable can completely change how the data is seen, which in turn can change the conclusions drawn from the data. I’ve found that Python is not very beginner-friendly when it comes to visualizations in the way that Power BI or Tableau are. Software created specifically for creating visualizations usually have a lot of safeguards and playroom, while in Python, even if the code is readable, it can be hard to tell what went wrong if you aren’t as experienced.
Even though I prefer Tableau overall for how dynamic the dashboards can be, Python does have its advantages. Python visualizations are much more easy to digest and export, and are definitely eons ahead of whatever is going on with Powerpoint or Sheets.
I’ve started to think on my capstone project, and am considering covering streaming services, either in music or television. I’ve always wondered about what Spotify’s gradual rise to the forefront meant for Apple Music over the last five years in particular. Since the market has only recently gotten competitive, developers are rushing to impress consumers with new features like Apple’s inclusion of spatial audio, which was reported this month by industry insiders, or Spotify’s curated playlists.
Other data I’m interested in exploring is the NYC mayoral elections, especially since this is the first time that the city has ever had ranked choice voting. By the nature of this ballot system, I’m predicting that there will be a close call between Adams and Wiley since they represent both sides of the political spectrum within the Democratic party. I guess we’ll see if my prediction was correct by the next few blog posts!