Courses

Welcome to the courses section of my portfolio. Here, I list the educational programs I have completed to expand my professional (data science) skills. While I completed many different courses throughout my professional journey (for example as part of my Bachelor’s and Master’s degrees) here I want to focus on those that are relevant for data science.

Self-Directed Learning

Machine Learning in Production

by DeepLearning.AI and Stanford Online, USA
February 2025

Skills: Model deployment, error analysis, data quality improvement, project scoping;

Certificate

Overview
This course covered the four key stages of a machine learning project: Scoping, Data, Modeling, and Deployment, with a strong focus on practical guidelines for real-world applications.

Scoping: Strategies for identifying impactful, addressable problems that drive business value.
Data: Ensuring high-quality data through label consistency, data augmentation, feature engineering, and proper train/dev/test splits.
Modeling: Establishing strong baselines, conducting error analysis, and auditing model performance.
Deployment: Managing data and concept drift, implementing deployment patterns, and setting up effective monitoring systems.

Advanced Learning Algorithms

by DeepLearning.AI and Stanford Online, USA
February 2023

Skills: Neural networks, decision trees, Random Forest, XGBoost;

Certificate

Overview
This course covered neural networks, decision trees, and tree-based ensemble methods such as Random Forest and XGBoost, along with essential machine learning strategies. I gained a deep understanding of how these algorithms work and built them from scratch using Python. Additionally, the course explored key hyperparameters and best practices for optimizing model performance.

Supervised Machine Learning: Regression and Classification

by DeepLearning.AI and Stanford Online, USA
February 2023

Skills: Linear and logistic regression, machine learning basics;

Certificate

Overview
This course began with linear regression as a fundamental example of regression, followed by logistic regression as an introduction to classification. Along the way, key machine learning concepts were covered, including bias-variance tradeoff, dataset splitting (train/dev/test sets), and other foundational principles essential for building and evaluating models.

Academia

I completed the following courses as part of my PhD program:

Elementary Data Mining

by Dr. Christian Borgelt, Paris-Lodron-University of Salzburg, Austria
March - June 2025

Skills: Theoretic understanding of the data mining process, kNN, Naive Bayes;

Overview
The Elementary Data Mining lecture provides a foundational overview of extracting meaningful knowledge from data. It covers the full Knowledge Discovery in Databases (KDD) process, from understanding data to building predictive models. Core modeling principles are introduced, including model classes, fitting techniques, error sources, and validation strategies.
Students learn key algorithms such as k-Nearest Neighbors, decision and
regression trees, and Bayesian classifiers (naive, Gaussian, and tree-extended). Additionally, the course introduces unsupervised learning through basic clustering methods, including k-means and hierarchical clustering, equipping students with a broad toolkit for data-driven analysis.

Probability Theory

by Prof. Arne Bathke, Paris-Lodron-University of Salzburg, Austria
March - June 2025

Skills: Theoretic understanding of probability theory, approaches to solve probability problems;

Overview
Introduction to elementary methods of stochastics and probability theory
with a strong emphasis on modeling. Discrete and continuous models, random variables, distributions, expected value, variance, moments, conditional probabilities, independence, important inequalities, convergences, and limit theorems.

Exercises for Probability Theory

by Jonas Beck, Paris-Lodron-University of Salzburg, Austria
March - June 2025

Skills: Ability to solve probability problems;

Overview
Exercises to the lecture “Probability Theory” held by Arne Bathke, described above.

I completed the following courses as part of my master program:

Programming with Perl

by Prof. Leila Taher, TU Graz, Austria
March - June 2023

Skills: Perl programming;

Overview
In the beginner’s Perl course I took, I learned the core concepts of programming through Perl’s syntax and unique strengths. I started with the basics—variables, data types, and input/output—before moving on to control structures like loops and conditionals. One of the most interesting parts was working with regular expressions, which allowed me to efficiently search, match, and manipulate text. I also got hands-on experience writing functions, subroutines, organizing code into packages, and using modules, which taught me how to structure more complex and reusable scripts. Overall, the course gave me a solid grasp of both Perl and essential programming principles.