M2_Data_Projects 💻

Projects in data analysis and data science in which I participated during my M2 . In these projects I learned to:

• Understand the problems and translate them analytically.
• Define the rules to structure the data.
• Do analysis and exploration of data with C++ and Python (NumPy).
• Do Data analysis using classical statistical models.
• Do comparison of the data with Monte Carlo models.
• Elaborate graphs to present the results with ROOT and Python.
• Communicate the results of the project thanks to Dataviz tools.

Specifically:

Situation 1: 💁

• Project in cosmology & astrophysics.
• Build algorithms for data analysis.
• Context: Planck mission.
• Task: Reconstruct clusters in patches of Planck maps. Separate the signals.
• Action: Linear combination.
• Result: Planck enables us to identify new clusters and superclusters.

Situation 2: 💁

• 5 month internship in particle physics.
• Large scale data analysis.
• Context: ATLAS experiment in the LHC.
• Task: The main task was the characterization of isolation for photons. Data and Monte Carlo simulations were compared for and the agreement between them was quantified.
• Action:
• Result: Signal distributions were obtaind with the data driven by bg sub-method. Tight isolation efficiencies and purities were measured. Data-MC agreements were estimated as shifts in the isolation distributions.

For more reference, you can read the reports of each project here, and here, as well as see the respective final presentations here, and here.

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