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.