Project completed in week 1 (28.09.-02.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I started the first day of the bootcamp in the comfort of my home! Due to the COVID-19 restrictions, the other 14 students and I were split into two groups, alternating daily between online and on-site attendance. It’s not the best experience, but so far it went pretty well. The first day started at 9:30 sharp with a short welcome and information presentation from our project manager and teachers.
We then introduced ourselves, and I was surprised at how diverse our Stochastic Sage cohort was, including people with backgrounds in Physics, Math, Finance, and Social Sciences, from coding newbies to experts. Another surprising fact was the even split between Linux, Mac, and Windows users! I’m curious whether this will change by the end of the course…
Soon afterwards, the actual coding (and bugs) started – right from the basics. One day we spent several hours on writing a FizzBuzz function. On another day, two hours only for cloning two repos and creating a branch. Another class on how to use the command line. And several session of setting up Python and Jupyter Notebooks.
Then we dived into descriptive statistics and data visualization with pandas and seaborn. Our challenge for the week was to create an animated scatterplot with matplotlib/seaborn and imageio to illustrate the relationship between life expectancy and fertility rate of world’s countries from 1960 to 2015, based on data from Gapminder datasets. Here’s the result:
Friday Lightning Talk
Each week, we get a main dataset and several tasks to apply the concepts learned throughout the week. On Fridays, we present in 5 minutes a particular finding from our weekly challenge project, a chart, new library, (un)solved bugs, or anything that is worth sharing and helpful for others. This Friday, I chose to talk about five new bash commands for checking the installed Python libraries, their versions and dependencies.
|to list all installed libraries||pip list|
|to list only outdated libraries||pip list -o (or –outdated)|
|to list only the latest / up to date libraries||pip list -u (or –uptodate)|
|to show all information about a library||pip show
|to list all libraries installed in a specific environment||conda list -n