As computer science students, we wanted to dive into an analysis of our subject and what we should learn as students. Students could use our graphics as a guide for what are important things to learn and to know before a technical interview.

What it does

It can take large texts and calculate the frequency of words. It also creates helper files that can be used to generate word clouds to help visualize this data.

How we built it

We collected data from over 300 problems on LeetCode and broke down this data into smaller words in order to calculate the frequency occurrence of each word. We then created a word cloud to help visualize our insights.

Challenges we ran into

It was not possible to scrape our data from our sources automatically, so we did this manually and did not have as big of a sample as preferred instead.

Accomplishments that we're proud of

Creating good visuals to display the data that we gathered.

What we learned

We learned how to better utilize jupyter notebooks to create data science projects and new techniques for both collecting and analyzing data.

What's next for Exploring Computer Science

We could like to gather more data and use natural language processing to gather more insight into what students can learn to become better candidates for software engineering internship positions.

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