Data Science

Specific technical experience

Statistics Probability, Statistical tests, Hypothesis testing
Exploratory data analysis imputation, PCA, t-SNE
Network analysis networkx
Clustering K-Means, K-Medioids, GMM, DBSCAN
Classification/regression Multilinear and logistic regression, KNN, Random Forest, LightGBM, XGBoost
Timeseries (S)ARIMA, LSTM, (Neuro)prophet, mSSA
Deep learning ANNs, CNNs, GNNs
Recommendation systems ALS, SVT, matrix factorisation
Packages statsmodels, scikit-learn, tensorflow, keras
Programming languages Python, R

Work examples from academia and real life

Below are a few academic and real examples showcasing the work that we can undertake. Although most of the work that we do is confidential, in some cases our clients allow us to publish results from the work with them. You will find examples from the standard data science use cases. In most cases, we are providing the code through github.

Please click on a tile to start.