Welcome to this repository!

I hold a M.S. in Physics and an Advanced M.S.c. in Financial Risk Management. I'm learning about Data Science techniques using Python and R (and I'm fascinated by it!)

I'm learning Data Science techniques mainly from online ressources such as blog posts, learning platforms such as Coursera, Dataquest.io, Datacamp and others (Kaggle, DataWorld, etc.). The training exercises are gathered in the Courses folder for reference.

Are also gathered here some projects applying the ML techniques learned throughout the courses. Here is a summarized list of techniques I had the opportunity to apply on various datasets:

• Data acquisition: using API's, Web Scraping, SQLite, PostgreSQL
• Statistics: Hypothesis testing, Regularized Linear & Logistic Regression (Lasso, Ridge, ElasticNet)
• Supervised Machine Learning: Decision Trees, kNN, Random Forests, SVM
• Unsupervised Machine Learning: K Means & Hierarchical Clustering, Dimension reduction (PCA, TruncateSVD, NMF)
• Deep learning/Neural Networks: Forward/Backward prop, ReLu, Gradient descent, Keras, optimization methods
• Natural Language Processing: Text tokenization using NLTK
• Network analysis with NetworkX

See the Projects for more detailed information.

I'm using the Anaconda Python distribution and Jupyter Notebooks to present the projects. If you wish to run them on your own station, here is the yml file. Instructions on how to create an virtual environment and install it can be found here. For the most useful Conda commands, see here.

I'm also enjoying learning how to build interesting data visualisations using Tableau. Click here to access my profile page on Tableau public.

To account for the various trainings followed, I'm gathering all the certificates earned through hard labor in the Certification folder.