Data Viz Week 2

Professor had us look into the Data Viz Society 2019 Survey Data and derive some insights from it. I know that the professor wants us to do much of the visualizations using Python, but to be honest I became a little rustier than I anticipated over the holidays and had to relearn much of the functions which I don’t have enough time for. It was ok to use seaborn or matplotlib functions but I only know some visualization functions on those.

It was a hectic week as usual for our manufacturing line. Learning the Python codes again was tedious and frankly I don’t have the patience to deal with so many things at the moment. So I had to use a little bit of both Python and Excel to bring about the data visualizations below.

I used Python to clean the data and Excel to generate the graphs. There was a lot of unorganized data so Python really helped me clean those answers in questionnaire forms (multiple choices and answers). I then exported them to csv file where I performed the visualizations using Excel.

I explored the data and gotten some good insights as to the demographics of the Data Viz Society. I learned that much of the respondents were of at least Bachelor’s degree holder, with more respondents citing they have Master’s degrees. Just goes to show that data visualizations skills are highly visible on more advanced degrees. The decline of the level of schooling once it reached doctorate can be attributed to the environment they are in right now (most master degree holders are in the corporate or business setting while PhD holders are in the academe).

Also, much of the respondents regardless of degree say that they have learned or practiced their data visualization skills through work related activities and projects. This goes to show that most of us learn along the way while doing our work. It says a lot about the academic setting not being able to accommodate that need from their students to learn data visualization.