Statistics and Data Analysis
Code: 44079 ECTS Credits: 9| Degree | Type | Year |
|---|---|---|
| 4313861 High Energy Physics, Astrophysics and Cosmology | OB | 0 |
Contact
- Name:
- Francisco Javier Rico Castro
- Email:
- franciscojavier.rico@uab.cat
Teachers
- Abelardo Moralejo Olaizola
- Carles Sánchez Alonso
- Jorge Carretero Palacios
- Pau Tallada Crespí
- Martin Borstad Eriksen
- Francesc d'Assis Torradeflot Curero
Teaching groups languages
You can view this information at the end of this document.
Prerequisites
It is needed a personal computer 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:
-
Download the Anaconda installer for Python 3 here https://www.anaconda.com/download/
-
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.
Objectives and Contextualisation
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.
Competences
- Solve problems in new or little-known situations within broader (or multidisciplinary) contexts related to the field of study.
- 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.
- Use the adequate software, programming languages and computer packages to research problems related to high energy physics, astrophysics and cosmology.
- Work in a group and take on responsibility, interacting professionally and constructively with other people with complete respect for their rights.
Learning Outcomes
- Apply data analysis techniques to problems in the areas of particle physics, astrophysics and cosmology, as well as other close but different areas.
- Learn how statistical analysis software works.
- Use Monte Carlo techniques to model real problems of physics.
- 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
Activities and Methodology
| Title | Hours | ECTS | Learning Outcomes |
|---|---|---|---|
| Type: Directed | |||
| Lectures | 56 | 2.24 | 1, 2, 3 |
| Study of theory and practical examples | 64 | 2.56 | 1, 2, 4, 3 |
| Type: Autonomous | |||
| Discussion, workgroups, problem solving | 60 | 2.4 | 1, 2, 4, 3 |
- Theory lectures including practical examples in the fields of High Energy Physics, Astrophysics and Cosmology
- Homework exercises to be solved by students alone or in small groups
- Discussion of problems during classes and tutorials
- Hands-on sessions on software tools for statistics and data analysis (in Python programming language)
- Explanation and discussion of sample code/algorithms in Python programming languages during classes and tutorials
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Assessment
Continous Assessment Activities
| Title | Weighting | Hours | ECTS | Learning Outcomes |
|---|---|---|---|---|
| Attendance and active participation to the lectures | 5% | 0 | 0 | 1, 2, 3 |
| Resolution of a final, synthesis exam | 45% | 5 | 0.2 | 1, 2, 3 |
| Resolution of class exercises | 50% | 40 | 1.6 | 1, 2, 4, 3 |
The evaluation will take into account:
- Attendance and active participation to the lectures
- Resolution of specific exercises along the course
- Resolution of a final exam
For those students not passing the course after the regular evaluation procedure, there will be a recuperation evaluation round consisting on a synthesis exam. There will be no threshold mark to be eligible for the recuperation evaluation round, other than the general requirement of having been evaluated at least for a 66% of the total qualification activities in the first round.
This subject/module does not foresee the single assessment system.
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
Software
We will introduce and make use of the Python programming language (see the "Prerequisists" section for installation details).
In particular, we will study and use the following Python libraries: numpy, pandas, matplotlib, scipy and scikit learn.
Language list
| Name | Group | Language | Semester | Turn |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | English | first semester | morning-mixed |