Alex Olteanu
Substack Writer (Freelance) at DataCamp
Bucharest, Romania
Summary
🤩
Rockstar🎓
Top SchoolAlex Olteanu is a versatile technical writer and former data science instructor with nine years of experience translating complex AI, Python, and data topics into clear, engaging content. Based in Bucharest, he has led editorial programs at DataCamp, authored dozens of viral AI and tutorial pieces, and grew a Substack to 5,000 subscribers in under six months. His background in theoretical philosophy informs a lucid, analytical style that blends storytelling, filmmaking sensibility, and rigorous data thinking—skills he applies to AI-generated media writing at Leonardo.Ai and freelance projects. An active contributor to data-focused repositories, he’s produced reproducible analyses such as a comparative study of Fandango movie ratings, demonstrating hands-on data-cleaning and visualization chops alongside editorial leadership.
9 years of coding experience
9 years of employment as a software developer
Bachelor's degree, Theoretical Philosophy, Bachelor's degree, Theoretical Philosophy at Universitatea din BucureștiERASMUS Scholarship, Philosophy, ERASMUS Scholarship, Philosophy at University of Rome Tor Vergata
Romanian, English, Italian
Stackoverflow
4,058reputation
234kreached
10answers
14questions
jupyter-notebook
top-5%
jupyter
top-5%
ipython
top-5%
scikit-learn
top-5%
python
top-5%
Github Skills (20)
data-visualizations10
pandas10
data-visualisation10
jupyter-notebook10
data-visualization10
data-analysis10
jupyter9
python9
ipython9
scikit-learn9
data-cleaning9
html6
normal-distribution6
floating-point6
anaconda6
Programming languages (3)
Github contributions (5)
dataquestio/solutions
Apr 2018 - Apr 2021
Solutions for projects.
Role in this project:
Data Scientist
Data ScientistContributions:34 commits, 16 PRs, 24 pushes in 3 years
Contributions summary:Alex's primary contribution involves analyzing movie rating data from Fandango, specifically examining whether Fandango's rating system has changed over time. They performed data cleaning, explored the distribution of ratings using kernel density plots, and calculated summary statistics such as mean, median, and mode. Their analysis compared the distribution of ratings for movies in 2015 and 2016.
mircealex/Movie_ratings_2016_17
Apr 2017 - Apr 2018
Contains a dataset with movie ratings for some of the most popular movies for 2016 and 2017 (IMDB, Fandango, Metacritic, Rotten Tomatoes)
Contributions:20 commits, 19 pushes, 1 branch in 1 year
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