Data Analysis Methods
Code: 106220Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Science, Technology and Humanities | OB | 2 |
Contact lecturer
- Name :
- Xavier Roque Rodriguez
- Email :
- xavier.roque@uab.cat
Teaching staff (external to UAB)
- Ana Arribas Gil
- Laura Medialdea Marcos
Group languages
You can consult this information at the end of the document.
Prerequisites
There are none.
Objectives
This course deals with basic ideas on Probability and Statistics, with the objetive of providing the necessary tools and concepts that allow analysing and managing quantitative information.
Learning outcomes
- Explain some findings from the forefront of science in terms that are accessible to students without in-depth knowledge of the subject matter.
- Make estimates of order of magnitude and avoid common fallacies and errors in the use of numerical information and in the interpretation of scientific results (diagnostic tests, clinical trials, etc.).
- Make competent use of software for analysing, synthesising and transmitting quantitative information, especially through graphs and computer graphics.
- Collect and interpret data to substantiate the conclusions drawn, including, where necessary, a reflection on social, scientific or ethical matters.
- Apply the main statistical distributions, the concept of regression to the mean and the basic notions of statistical inference to specific problems.
- Analyse data rigorously to draw conclusions from them.
- Explain the basic mathematical concepts and gain familiarity with mathematical reasoning.
- Formulate and apply programming models and languages to basic problem solving in statistics and probability.
- Summarise the fundamentals of data management and analysis technologies, and tools for representing information.
Contents
Introduction: data, information, knowledge
Where to find information: resources, research techniques, reliability
Numeric alphabetisation: percentages, magnitude orders, linearity and non linearity
Graphical information representation techniques and scientific visualization
Spreadsheets as tools for basic data management and representation
Correlation and causality. From data to theory
Discrete correlation: the classification problem. Sensibility and specificity. Bayes theorem
Signal and noise: random phenomena. Binomial, normal and Poisson distributions
Continuous correlation: regression to the mean
Introduction to inferential statistics: surveys and clinical trials
Fundamentals of programming for data analysis
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Essay supervision | 4.25 | 0.17 | 1, 2, 3, 4, 5, 8 |
| Practical lectures | 16 | 0.64 | 1, 2, 3, 5, 8 |
| Study and essay writing | 94.75 | 3.79 | 1, 3, 4, 5, 6, 8 |
| Lectures | 33 | 1.32 | 2, 3, 6, 7, 9 |
Theory: Theory classes with materials available on the web.
Practices: Problem classes. Computing classes using statistical software.
Group tutorials for resolution of problems, doubts etc.
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Final exam | 45% | 2 | 0.08 | 1, 2, 4, 5, 6, 7, 8 |
| Group final essay | 15% | 0 | 0 | 2, 3, 4, 5, 6, 8 |
| Essay 1 | 20% | 0 | 0 | 1, 6, 7, 9 |
| Essay 2 | 20% | 0 | 0 | 1, 6, 7, 9 |
Continuous evaluation
Two written tests counting 40% of the final grade.
Group project counting 15% of the final grade.
Final exam
End of course exam counting 45% of the final grade.
In the extraordinary exam, the student will sit a new written exam and will receive a grade equal to the maximum of the exam grade or a weighted average of the exam (50%) and coursework (50%) in the same way as the usual convocation.
Single assessment
Students who opt for the single assessment system will have to take two written tests (50%) and an exam (50%), on the indicated date.
This subject allows the use of AI technologies as an integral part of the submitted work, provided that the final result reflects a significant contribution from the student in terms of analysis and personal reflection. The student must clearly (i) identify which parts have been generated using AI technology; (ii) specify the tools used; and (iii) include a critical reflection on how these have influenced the process and final outcome of the activity. Lack of transparency regarding the use of AI in the assessed activity will be considered academic dishonesty; the corresponding grade may be lowered, or the work may even be awarded a zero. In cases of greater infringement, more serious action may be taken.
Bibliography
Basic references
C. Criado Pérez. La mujer invisible. Descubre cómo los datos configuran un mundo hecho por y para los hombres. Barcelona: Seix Barral, 2020.
D. Huff. Cómo mentir con estadísticas. Barcelona: Crítica, 2015.
D. Peña y J. Romo. Introducción a la Estadística para las Ciencias Sociales. Madrid: Mc Graw Hill Interamericana, 1997.
I. Portilla. Estadística descriptiva para comunicadores. Pamplona: Editorial EUNSA, 2004.
Additional references
D. Rowntree. Statistics Without Tears. London: Penguin Books, 2018.
G. Klass. Just Plain Data Analysis. Lanham, MD: Rowman & Littlefield, 2012 (2nd. ed.).
Software
No specific software is required.
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 |
|---|---|---|---|---|
| (TE) Theory | 20 | Spanish | first semester | morning-mixed |
| (PAUL) Classroom practices | 20 | Spanish | first semester | morning-mixed |