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Summary

I'm currently working as a Data Engineer in the Quality Datalab Team at Tesla Berlin Gigafactory.

I'm deeply interested in Data Engineering and Distributed Systems, I'm always looking for new and interesting ways to efficiently collect, process and visualize huge quantities of data and extract powerful insights from them.

In the past, I was the vice president of the student association Polimi Data Scientists (in short PMDS), where I founded and kickstarted the projects section, a team that is now cooperating with multiple companies on several data-related projects.

I started coding in high school when I was 16, and over the course of the years, I created and took part in a number of projects from many different fields, ranging from frontend and backend web development, going to augmented reality, data stream analytics, and data pipelines modeling.

Some of these projects were done as part of my study curriculum, others just out of personal interest or as part of the PMDS projects team, here you can take a look at them (or at least the ones I was allowed to make public).  

Projects

  • Football Tweets Streaming Pipeline :Streaming data pipeline to extract and analyze real time football data from Twitter
  • Football News Batch Data Pipeline: Batch data pipeline to scrape and analyze data from Football news websites
    • Football News Scraper : Web scraper realized with scrapy that downloads the latest article titles from the most famous italian and english news websites
    • Football News Analytics : Python scripts used for downloading football news data from S3,do various analytics jobs on them and then store the results in a remote mongoDB instance
    • Football News S3 Utilities : Utilities for generating and uploading automatically S3 files and directories used for football articles data analysis
  • Distributed Systems Projects : Design and implementation of Distributed Systems for large scale noise data collection/cleaning and for IOT smart thermostat controllers
  • Preference Selection Automatic Cut : About Automatic Dendrogram Cut integration for preference based Computer Vision Model Fitting algorithms
  • PoliMi Data Scientists : The Data Science Association of Politecnico di Milano, I've held the Vice President title in the year 2022
    • Covid Tweets Analytics : Scraping, Statistical and Sentiment Analysis on Covid related Tweets and Public Data
  • Team-Classifier-PyTorch : CNN built using PyTorch and Transfer Learning to classify players belonging to 8 different teams from the UEFA Champions League
  • Kaggle Competitions : A collection of the Kaggle competitions I've taken part to
    • Titanic : Code for the Kaggle Titanic Challenge, classified in top 10%
    • Digit Recognition : Code for the Kaggle Digit Recognition Challenge, classified in top 15%
  • Spalla Data Visualization : Web application built with React and Redux to load and visualize data coming from a student survey conducted at my high school
  • Flask Uni REST API : REST API built with Flask which allows authentication and basic CRUD operation on a Mongo database containing mock university data
  • Gamified Marketing : Web application developed with Java, JEE and JPA to simulate a gamification process in an ECommerce Website

Alessandro Messori's Projects

appas-web icon appas-web

interfaccia web per gestire gli utenti della applicazione di istituto

flask-uni-api icon flask-uni-api

REST API built with Flask which allows authentication and basic CRUD operation on a Mongo database containing mock university data

mtds-projects icon mtds-projects

Design and implementation of Distributed Systems for large scale noise data collection/cleaning and for IOT smart thermostat controllers

spalla-data-visualization icon spalla-data-visualization

Web application built with React and Redux to load and visualize data coming from a student survey conducted at my high school

spark icon spark

Apache Spark - A unified analytics engine for large-scale data processing

team-classifier-pytorch icon team-classifier-pytorch

CNN built using PyTorch and Transfer Learning to classify players belonging to 8 different teams from the UEFA Champions League

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