Davide Tissino

University Student, VC Trainee & Basketball Analyst

About Me Projects Resume

About Me

Hi, I'm Davide! I am currently finishing my BSc in International Economics and Management at Bocconi University, in Milan 🇮🇹. I am interested by the Start-ups and Venture Capital sectors, and have thus taken on a role as a Venture Trainee at Innovis VC, a European student organization at the intersection of start-ups, investors and students. I am also keen on computer programming and data analytics. Being an avid basketball enthusiast, in my free time I enjoy creating data visualizations and web applications focusing on NBA basketball 🏀.
I am interested in pursuing professional opportunities within the analytics sector in startups or established organizations.
Check out some of my projects below

Alternatively, you can scroll through Databall, my Substack newsletter where I talk about basketball and data!

Stack

Programming languages for uni & side projects

Experience

Experience within startup & VC sectors

Skills

Web Scraping
Data Cleaning & Analysis
Data Visualization
Data Applications


Start-ups
Sourcing
Venture Capital
Investment Decisions
Business Planning
MS Office

Projects

📊 NBA Box Scores Query

Developed a Shiny App using R to display all NBA Regular Season and Playoffs box scores, scraped starting from the 1946-47 season. Includes a player filter and a text filter to sort the table according to the desired conditions. It is sufficient to write the stat name, followed by an operator and the value.

🎨 Data Visualization Gallery

A collection of the NBA data visualizations that I have created thus far in my free time. All the visualizations were developed in R, with the main packages used being: rvest and httr for web scraping, tidyverse for data cleaning and ggplot or shiny for final visualization. Each image is linked to the corresponding tweet on my personal account.

🔥 Shot Chart Analyzer

Working on a Shiny App to showcase NBA players' shot charts for the season and/or their career. Should include a series of visualization options, including showcasing the player's own makes or misses, comparing the player's efficiency from different areas of the court to league average or visualizing the player's difference in shot distribution in various periods of his career.