I hold a bachelor's degree in Management and Computer Science from LUISS and I am currently a student of the Data Science Master's Degree @Sapienza.

Through university and (a lot of) self-studying I have a solid background in data science, from simple ETL to modelling. In particular, I have in-depth knowledge of:

• R for machine learning, modelling, statistics, reporting (R Markdown) and data manipulation, exploiting the tidyverse ecosystem far more than the base language.
• Python for scripting, manipulation, modelling and web scraping. Specifically, my experience revolves mostly around Pandas, NumPy, scikit and Tensorflow. Experience with Tensorflow has been both with Keras and more low-level APIs.
• KNIME Analytics Platform and KNIME Server, now Business Hub, due to university projects and work experience. Specifically, I am L1, L2 and L3 certified.
• Relational paradigm for databases and SQL.

I am a former Data Science Intern @KNIME, the software company behind KNIME Analytics Platform (and its enterprise version), a popular and powerful low-code tool to perform data science tasks, at every level. As an employee, I developed KNIME native low-code approaches for the Word2Vec complete pipeline and I also developed a fast new Python-based Word2Vec node based on Tensorflow, using a mix of low-level APIs (mainly for the pre-processing) and Keras for the modelling steps. The code for the node is publicly available in one of my repositories, at this link.

Until recently, I also was a Teaching Assistant in Statistical Methods for Data Science and Laboratory, one of the main courses, spanning two semesters, in the Data Science Master’s Degree @Sapienza, dealing with an introduction to Probability Theory before delving into Frequentist and Bayesian Inference. My interests are mainly in probability theory (and statistics) for stochastic processes and stochastic calculus, a field of mathematics related mainly to real analysis.

For all things Data Science related you can contact me with:

• LinkedIn
• Mail