This repository is meant as a store for all programming related projects I'll be doing at ITU. Each course has its own folder, with subfolders for each week of the course that include things like exercise solutions.

Course reference:

Shorthand	Course name	Description
ALDAS	Algorithms and Data Structures	Computers help us compute things: To sort alphabetically the entries in a telephone directory; to compute the next frame of a video game; to find the seats available on an airplane. However, there are faster and slower ways to compute things. To be an effective programmer, you must know not only how to make a computer compute things, but how to efficiently compute things.This course provides the basic algorithmic tools indispensible for every software developer. Topics covered are among others complexity analysis, big-O, algorithmic problem solving techniques including divide-and-conquer, concrete algorithms and data structures for search trees, sorting, hashing, graphs, shortest paths.
APSTA	Applied Statistics	The course intends to give the student tools to identify and solve statistical problems in practice, occurring in data-analysis. The subjects covered in the course include: probability spaces, random variables, conditional and joint probability, independence, expectation, variance, correlation and covariance, simulation of random variables, law of large numbers, central limit theorem, explorative data analysis, statistical models, bootstrapping, maximum likelihood estimation, confidence intervals, hypothesis testing.
FIYEP	First Year Project	The course consists of a series of full-fledged Data Science mini-projects from start to finish, including the initial memo, technical translation of the problem, some methodology decisions, implementation, evaluation, and translation of the results back into non-technical language. Through this course students will gain experience with online collaboration using platforms such as GitHub and Overleaf. First Year Project 1 First Year Project 2 (currently private)
NEANA	Network Analysis	This course is about understanding and analyzing networks in various contexts, such as social relationships, the Internet, and more. It will teach students computational tools to become network scientists and enable them to solve practical network problems, especially in social networks.
INDBS	Introduction to Database Systems	The course covers fundamental techniques for developing data management and data analytics applications.
MALEA	Machine Learning	This course gives a fundamental introduction to machine learning (ML) with an emphasis on statistical aspects. In the course, we focus on both the theoretical foundation for ML and the application of ML methods.