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The importance of an internship

University start Although I’ll mainly focus on why a Computer Science student needs to intern during his studies, probably this applies to all students in general. When I was a 2nd-year student, I realized that the knowledge I get from my university courses is beneficial but not enough for the job market. Sometimes, the way academia approaches modern software is too stale, and as a result, computer science students are not ready to get a new software engineering job after graduation....

September 21, 2021 · 4 min · Menelaos Kotoglou
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Republishing and maintaining Profanity-check

As previously posted here: https://dev.to/koti/updating-an-important-but-stale-python-library-3o6i, me and Dimitry cooperated on updating a Python library important for the smooth operations of his company. Unfortunately, it seems that its original author never accepted our previous Pull Request to revive the profanity-check package. As a result, Github issues were kept being opened by other developers asking about the library and issues they had while using it. What surprised us the most, was this one: https://github....

January 31, 2021 · 2 min · Menelaos Kotoglou
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The Power Of Study Groups

Undoubtedly, the Covid-19 crisis has changed students’ habits a lot. Old habits, such as hanging out, drinking beers, watching movies and even studying together have almost torn apart since most countries are experiencing the second Coronavirus wave. As a student, I decided to take advantage of the time gained from the situation to work on myself, my skills and my academic performance. Especially when the University’s workload increases, keeping yourself disciplined and focused on your goals becomes even harder....

November 11, 2020 · 2 min · Menelaos Kotoglou

Updating an important but stale Python library

The project is based on a “profanity-check” library created by Victor Zhou. You can read more about it here and find it online here: https://github.com/vzhou842/profanity-check. Firstly, we installed the library in a virtual environment and experimented with different samples. We tested the model with an internal dataset consisting of 850 tweets retrieved through Twitter’s sampling API then labeled manually. This produced the following results: Confusion Matrix ActualPredicted Not Profane (0) Profane (1) Not Profane (0) 703 14 Profane (1) 93 39 Accuracy Score: 87....

July 1, 2020 · 3 min · Menelaos Kotoglou