I hold a bachelor's degree in Management and Computer Science from LUISS and I have gratuated with highest honors from the Data Science Master's Degree @Sapienza. I am now a PhD Student in Data Science @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 statistical learning and probability theory for stochastic processes and stochastic calculus.
For all things Data Science related you can contact me with:
LinkedIn
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