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Informatics Tools for Statistics

Code: 104849
Credits: 6
2026/2027
Degree programme Type Course
Applied Statistics FB 1

Contact lecturer

Name :
Joaquim Roé Vellvé
Email :
joaquim.roe@uab.cat

Teaching staff

Jose Maria Mondelo Gonzalez

Group languages

You can consult this information at the end of the document.

Prerequisites

Because it is a first year course and in the first semester, it has no prerequisite.

Objectives

The main objectives of the subject are the following:

  • Familiarize oneself with the use of an Computer Algebra System or calculating manipulator. This manipulator must be considered as an everyday tool when studying the rest of the subjects.
  • Learn how to structure and write scientific texts with the LaTeX word processor.
  • Familiarize oneself with the concept of statistical package. In particular, create and transform databases and get used to work environments in graphic mode and command line.
  • Learn how to use a command line operating system, taking advantage of their power to merge, separate or extract data from files or file sets.
  • Introduce oneself to the formalization of algorithms using a programming language.

Learning outcomes

  • CM02 (Solve problems using structured programming, designing suitable algorithms.) Solve problems using structured programming, designing suitable algorithms.
  • CM04 (Programme algorithmic solutions to solve problems within a context linked to statistics.) Programme algorithmic solutions to solve problems within a context linked to statistics.
  • KM05 (Recognise typical structures of advanced programming languages (variables, loops, arrays, lists, dictionaries, tuples, etc.), functions and classes.) Recognise typical structures of advanced programming languages (variables, loops, arrays, lists, dictionaries, tuples, etc.), functions and classes.

Contents


  1. Brief introduction to computing. Computing resources at the University available to use in the course.

  2. Textprocessor (LaTeX): Structure of a TeX file. Edition and compilation. Mathematical formulas. Floating objects.

  3. Statistical packs (R): Work environments. Declaration of variables. Creation, obtaining and manipulation of databases. Descriptive tools Graphic environment.

  4. Computer Algebra System (Sage): numerical and algebraic calculations. Function graphs. Resolution of equations. Definition of functions. Lists, sets and successions. Logical programming, iterations and procedures.

  5. Operating system (Bash): The console. First instructions and obtaining help. Manipulation of files. 

  6. Programming (Python): Introduction to Python.

Learning activities and methodology

Title Hours ECTS Learning outcomes
Preparing for the exam on a computer algebra system 25 1 CM02, CM04, KM05
Preparing for the exam on LaTeX 12.5 0.5
Preparing for the programming exam 32 1.28 CM02, CM04, KM05
Preparing for the exam on the statistical package 25 1 CM02, CM04, KM05
Practice sessions 47 1.88 CM02, CM04, KM05

The practice sessions are held in computer rooms or classrooms prepared for the use of laptops.

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

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Exam on document preparation (LaTeX) 0.13 1 0.04 CM04
Computer algebra system exam 0.26 1.5 0.06 CM02, CM04, KM05
Statistic package exam 0.22 1.5 0.06 CM02, CM04, KM05
Programming exam (operating system and Python language) 0.22 1.5 0.06 CM02, CM04, KM05
Final exam 0.43 3 0.12 CM02, CM04, KM05

Continuous evaluation activities provide a grade; to pass the subject this grade has to be greater or equal than 5, and the mark obtained on each subject block has to be greater or equal than 3. A time of 4 hours is reserved to re-evaluate any exam that the student failed.


In this course, the use of Artificial Intelligence (AI) technologies is not permitted at any stage. Any assignment that includes AI-generated content will be considered a breach of academic integrity and may result in a partial or full penalty to the activity's grade, or more severe disciplinary sanctions in serious cases.

Bibliography

As all work is done on computers, the main source of information will be the help of the programs that are used. In addition, as a complementary bibliography we recommend the following online resources.

  • Tobias Oetiker, Hubert Partl, Irene Hyna and Elisabeth Schlegl. The not so short introduction toLaTeX2E (or LaTeX in 139 minutes). https://tobi.oetiker.ch/lshort/lshort.pdf
  • W.N. Venables, D.M. Smith and the R Development Core Team: An introduction to R.https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
  • GNU Bash manual, https://www.gnu.org/software/bash/manual/
  • Python Software Foundation, The Python Language Reference, https://docs.python.org/3/reference/
  • The Sage Reference Manual, https://doc.sagemath.org/html/en/reference/

 

Software

SageMath, R, Python, LaTeX and GNU/Linux.

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
(PLAB) Practical laboratories 1 Catalan first semester afternoon
(PLAB) Practical laboratories 2 Catalan first semester afternoon
(PLAB) Practical laboratories 3 Catalan first semester afternoon