From Philosophy to Data Analysis, a project with Python and Google Trends.

Since I was little, I have been fascinated by knowledge. Like every child, I always asked why things happened. But there was something in me that wanted more. When I read my first philosophical text, a dialogue by Plato, at 12, I knew that my destiny would be linked to philosophy.

My bachelor's degree is in philosophy, for those who don't know me. And within philosophy, I specialized in epistemology, philosophy's most "scientific" part.

During my specialization, I began to study various topics related to the theoretical part of data and how we manage data in our scientific models. I hope to tell you more about it soon.

Several months ago, I discovered the "practical" part of data and fell in love (even more) with data and its applications. Since then, I have worked hard to improve my capabilities as a data analyst.

In this context, I want to share some of the results I obtained from a small project using Python and Google Trends. In the project, I analyzed the data provided by Google Trends about the interest in the term "philosophy" from 03/19/2024 to 03/26/2024 in the United States.

If you want to see the complete project, you can do so using the following link:

https://www.kaggle.com/code/alejocercato/philosophy-googletrend

According to Google Trends, this was the distribution of interest in the term "philosophy" during the last week.

Thanks to pandas and matplotlib I was able to discover:

1. What are the times of day with the most interest in philosophy.

2. What is the day with the most interest in philosophy.

(At least for the last week).

Although just barely, the day with the most interest in philosophy was Wednesday. And within Wednesday, the most popular time for philosophy was 10 pm. And that's why I chose this day and this time for my publication!

Future project expansion

In the future I plan to extend the project and add more layers of complexity. Some ideas I want to implement are:

- extend the analyzed time range.

- extend the spatial region analyzed (perhaps to the entire world).

- add other trends of interest, for example of philosophers or specific philosophical topics.

- compare interest in philosophy with interest in data analysis to see if there is any correlation.

Do you have any interesting suggestions or ideas to add to the project? I read you in the comments. Thank you for your time and support.

A big greeting,

The Data Philosopher