Statistics and Data Analysis

Objectives

In this course we will learn how to distill scientific knowledge from experimental data, a process that relies on statistical methods. We will learn the basics concepts of Probability and Statistics (in their Frequentist and Bayesian frameworks). In addition, we will study and practice several particular statistical methods and data analysis techniques usually used in the fields of High Energy Physics, Astrophysics and Cosmology. To that aim, we will learn and practice the use of modern statistics and analysis software tools.

Skills

  1. Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
  2. Use mathematics to describe the physical world, select the appropriate equations, construct adequate models, interpret mathematical results and make critical comparisons with experimentation and observation.
  3. Use the adequate software, programming languages and computer packages to research problems related to high energy physics, astrophysics and cosmology.
  4. Work in a group and take on responsibility, interacting professionally and constructively with other people with complete respect for their rights.

Learning outcomes

  1. Apply data analysis techniques to problems in the areas of particle physics, astrophysics and cosmology, as well as other close but different areas.
  2. Learn how statistical analysis software works.
  3. Use Monte Carlo techniques to model real problems of physics.
  4. Work in small groups to solve problems of data analysis.

Content

Part 1: Basic concepts on probability, statistics and Monte Carlo techniques
Part 2: Python for Statistics and Data Analysis
Part 3: Parameter estimation, Hypothesis test and Unfolding
Part 4: Bayesian Statistics

Prerequisites

For the Python Bootcamp (part 2), it is needed to bring a personal laptop with a running installation of Python 3. Install Python 3 with the Anaconda installer. In this way, your Python distribution will contain all the associated packages needed for this course. Follow these steps:

  1. Download the Anaconda installer for Python 3 here https://www.anaconda.com/download/
  2. Follow the installation instructions - both GUI or terminal versions work fine. If prompted, select the option to add the new anaconda directory to your path. The use of GNU/Linux is highly recommended.

Details

Semester 1
Itinerary HEP, ASTRO
Type Mandatory
ECTS 9
Hours 56

Teachers

Bibliography

G. Bohm and G. Zech; “Introduction to Statistics and Data Analysis for Physicists”, 3rd Edition, 2017, Verlag Deutsches Elektronen-Synchrotron (available on-line https://s3.cern.ch/inspire-prod-files-d/da9d786a06bf64d703e5c6665929ca01 )
F. James; “Statistical Methods in Experimental Physics”, 2nd Edition, 2006, World Scientific
G. Cowan; “Statistical Data Analysis”, 1998, Oxford University Press
A. Gelman, J. B. Carlin, H. S. Stern, et al. “Bayesian Data Analysis”, 3rd Edition, 2013, CRC Press

More Information

Course Guide in PDF