This repository holds introductory projects that were conducted to familiarize with Data Science and Data Analytics. Python and related libraries such as pandas, matplotlib and scikit-learn were primarily used. Each project is uploaded in a python notebook file format (.ipynb) Jupyter Notebook was the selected implementation environment.
flight_on_time.ipynb
What is the possibility of a flight being delayed? Could the airport of departure, or the airline be an indicator? Are there months with more delays than others? These are some of the questions examined by this project, whose aim is to provide experience with pandas and matplotlib libraries.
civil_resistance.ipynb
Replication of the findings of the book "Why Civil Resistance Works" by Erical Chenoweth and Maria J. Stephan. The book studies the effectiveness of nonviolent campaigns compared to violent campaigns. An opportunity to familiarize with the statsmodels library and handling statistics with Python
rosetta_stone_human_capital.ipynb
Another practice project with statistics in Python. It examines and replicates the study called 'A Rosetta Stone for Human Capital', by Dev Patel and Justin Sandefur. The study investigates the diferences in skills across countries.
greek_parliament_proceedings.ipynb
Can one tell the political party a parliament member belongs to, only by paying attention to the way they speak? This project attempts to answer this question by trying out different machine learning techniques, namely Naive Bayes Classification, Decision Trees and Neural Networks. It offered introductory hands-on practice with libraries scikit-learn, tensorflow and keras.