SUMMARY 
My main fields of interest are the particle and accelerator physics, a high knowledge of data analysis and statistics, and different programming languages like C++, Python. The relation with other people, the communication and the teamwork are my strengths, in fact, during my studies at the university and all jobs I've always given my best, encouraging myself to learn from my mistakes and continuously improve.

WORK EXPERIENCE
CERN, Geneva, Switzerland Mar 2018 — Aug 2018
Technical Student
I worked on the optics of the transfer line LINAC3-LEIR and LEIR-PS and on the beam intensity from LEIR to LHC. I created different programs, with MadX to simulate the optics along the transfer lines and with Python to analize the beam intensity in different points of the accelerator for the transmission efficiency. In the end, I found new values forthe optics in the transfer line that result in a better beam transmission and efficiency.

CERN, Geneva, Switzerland Mar 2016 — Dec 2016
Technical Student
I worked on the Beam Position Monitors calibration for CLiC. This calibration has been made creating, with MATLAB, different programs for data acquisition and analysis.

EDUCATION 
Master's degree in High Energy
Physics
Sep 2016 — Present
University of Pisa, Pisa, Italy
My courses are focused to the study of the experimental and theoretical particle physics,
expanding themself in all fields that concern them. Use of different programming
languages, for job as data analyst and data scientist, like C++ and Python.

Bachelor's degree in Physics Sep 2013 — Sep 2016
University of Pisa, Pisa, Italy
Studies on all fields of physics, theoretical and experimental. Quantum, classical and
statistical mechanics, electromagnetism, particle interactions, physics of matter.

LANGUAGES 
Italian: Native
English: Fluent

SKILLS 
Signal processing, Data display, Electronics, Algorithms, Problem Solving, Phyton, Scikitlearn, Databases, Artificial Intelligence, Critical Listening, Critical Reading, SQL, MS
Office, Data Analysis, Numerical Analysis, Data Science, Statistics, Machine Learning,
Frameworks, Jupyter Notebook, Root C++, Data Engineering, Unstructured data, Data
Analytics, Projects, Maths, Strategy, Engineering, Creative, Teamwork, Development,
Lead, Direct, Management, Design, Big Data, Graduate, Solutions, Application, Team
Player, Comunication, Operations, RStudio, Watson Studio, Zeppelin, Matplotlib,

CAREER GOALS 
Physics, Science, Research, Technology, Data Analyst, Data Scientist, Data Engineer

QUALIFICATIONS
Data Science Orientation
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner has a good understanding of why data science, artificial intelligence
(AI) and machine learning are revolutionizing the way people do business and research
around the world. They have general knowledge on what data science is today.

Open Source Tools for Data Science
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner has demonstrated their skill and understanding of how popular data
science tools such as the Jupyter Notebook, RStudio, Zeppelin and Watson Studio are
used, as well as the advantages and disadvantages of each tool.

Data Science Methodology
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner has demonstrated a thorough understanding of the different stages that
constitute the data science methodology, which is instrumental to solving any data science problem.

Python for Applied Data Science
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner has the core skills in Python such as critical data structures,
programming fundamentals and experience with core libraries for data science. They can
apply this knowledge to work with data and develop applications for data science. The
individual also has sufficient Python knowledge to work with Python libraries.

Databases and SQL for Data Science
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner understands relational database concepts, can construct and execute
SQL queries, and has demonstrated hands-on experience accessing data from databases
using Python-based Data Science tools like Jupyter notebooks.

Data Analysis with Python
Issued by Coursera, Authorized by IBM, Released on June 2019
This badge earner has the core skills in Data Analysis using Python. They can readily
clean, visualize and summarize data using Pandas. Using Scikit-learn, the earner can
develop Data Pipelines, construct Machine learning models for Regression and evaluate these models

Machine Lerning with Python
Issued by Coursera, Authorized by IBM, Released on June 2019
The badge earner has demonstrated a good understanding and application of machine
learning (ML) including when to use different ML techniques such as regression,
classification, clustering and recommender systems. The individual has acquired the skills
to use different machine learning libraries in Python, mainly Scikit-learn and Scipy, to
generate and apply different types of ML algorithms such as decision trees, logistic
regression, k-means, KNN, DBSCCAN, SVM and hierarchical clustering.