Important notice
The course guide is provisional.
The PDF version of the course guide may take a few days to become available in the DDD.
Statistics and Data Analysis
Code: 44079Credits: 9
| Degree programme | Type | Course |
|---|---|---|
| High Energy Physics, Astrophysics and Cosmology | OB | 1 |
Contact lecturer
- Name :
- Abelardo Moralejo Olaizola
- Email :
- abelardo.moralejo@uab.cat
Teaching staff
- Marc Manera Miret
- Abelardo Moralejo Olaizola
- Carles Sanchez Alonso
- Jorge Carretero Palacios
- Pau Tallada Crespi
- Martin Borstad Eriksen
- Francesc d'Assis Torradeflot Curero
Group languages
You can consult this information at the end of the 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
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.
Learning outcomes
- CA04 (Adopt statistical techniques to solve data analysis problems, both in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.) Adopt statistical techniques to solve data analysis problems, both in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.
- KA03 (Identify data analysis techniques to problems in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.) Identify data analysis techniques to problems in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.
- SA04 (Critically evaluate experimental and statistical data applied to high-energy physics, astrophysics, cosmology, and particle physics, using appropriate analysis tools.) Critically evaluate experimental and statistical data applied to high-energy physics, astrophysics, cosmology, and particle physics, using appropriate analysis tools.
- SA05 (Apply Monte Carlo techniques to model real physics problems.) Apply Monte Carlo techniques to model real physics problems.
- SA06 (Apply data analysis techniques to problems in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.) Apply data analysis techniques to problems in the field of particle physics, astrophysics and cosmology, as well as in nearby but different fields.
- SA07 (Apply statistical analysis software from the field of particle physics, astrophysics and cosmology, as well as from nearby but different fields.) Apply statistical analysis software from the field of particle physics, astrophysics and cosmology, as well as from nearby but different fields.
- SA08 (To make appropriate use of statistical analysis software in the field of particle physics, astrophysics and cosmology.) To make appropriate use of statistical analysis software in the field of particle physics, astrophysics and cosmology.
- SA09 (Use specialized bibliographic sources, scientific articles, and digital resources in English to deepen the concepts of statistics and data analysis.) Use specialized bibliographic sources, scientific articles, and digital resources in English to deepen the concepts of statistics and data analysis.
Contents
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
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Study of theory and practical examples | 64 | 2.56 | CA04, KA03, SA04, SA05, SA06, SA07, SA08, SA09 |
| Discussion, workgroups, problem solving | 60 | 2.4 | CA04, KA03, SA04, SA05, SA06, SA07, SA08, SA09 |
| Lectures | 56 | 2.24 | CA04, KA03, SA04, SA05, SA06, SA07, SA08, SA09 |
- 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
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Resolution of a final, synthesis exam | 45% | 5 | 0.2 | CA04, KA03, SA04, SA06 |
| Resolution of class exercises | 50% | 40 | 1.6 | CA04, KA03, SA04, SA05, SA06, SA07, SA08, SA09 |
| Attendance and active participation to the lectures | 5% | 0 | 0 | CA04, KA03, SA04, SA05, SA06 |
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
* Students will solve the exercises posed in class outside of school hours. The evaluation may include an in-person session to discuss the results with the teaching staff
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.
In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of the assignment development, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must clearly identify which parts have been generated with this technology, specify the tools used, and include a critical reflection on how these have influenced the process and the final outcome of the activity. Lack of transparency in the use of AI will be considered a breach of academic honesty and may lead to a grade penalty for the activity, or more severe sanctions in serious cases.
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.
Course groups and languages
The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE
| Type of teaching | Group | Language | Semester | Shift |
|---|---|---|---|---|
| (TEm) Theory (master) | 1 | English | first semester | morning-mixed |