Statistics Consultancy
Code: 104877Credits: 6
| Degree programme | Type | Course |
|---|---|---|
| Applied Statistics | OP | 4 |
Contact lecturer
- Name :
- Llorenç Badiella Busquets
- Email :
- llorenc.badiella@uab.cat
Group languages
You can consult this information at the end of the document.
Prerequisites
Informatics Tools for Statistics
Linear Models 1
Linear Models 2
Advanced Modelling
Unsupervised Learning
Machine Learning 1
Aprenentatge Automàtic 1
Objectives
Consolidate and offer new tools to start a professional career as a Statistician
The course's approach covers the different fields of application of consulting:
- Health sciences
- Banking and insurance
- Sociological studies and surveys
Learning outcomes
- CM14 (Propose the statistical model needed to analyse data sets belonging to real studies.) Propose the statistical model needed to analyse data sets belonging to real studies.
- KM17 (Recognise the statistical models for the analysis of data with different structures and complexities that frequently appear in different fields of application.) Recognise the statistical models for the analysis of data with different structures and complexities that frequently appear in different fields of application.
- SM16 (Select appropriate sources of information for the statistical work.) Select appropriate sources of information for the statistical work.
- SM17 (Discuss scientific articles in which the analysis of a study of the different areas of application is considered.) Discuss scientific articles in which the analysis of a study of the different areas of application is considered.
- SM18 (Refine the information available for subsequent statistical processing.) Refine the information available for subsequent statistical processing.
- SM19 (Analyse complex data, whether this is due to their characteristics or their size.) Analyse complex data, whether this is due to their characteristics or their size.
Contents
General Content
- The Statistician Profession
- SoCE
- CV
- Professional career
- Continuing studies
Practical aspects of the profession
- Report structure
- Work meetings
- Project briefing
- Study protocol
- Sample size determination
- Budgets
- Timeline/Schedule
- Project structuring
- R / SAS / Python software
- Communication of results
Review of competencies in statistical techniques
- Advanced models
- Cluster analysis
- Machine Learning, Data Science and Statistics
- Big Data
- Missing data imputation
- Causal inference
- Bayesian statistics
Complementary technical competencies
- Advanced reports with R Markdown
- Advanced graphics with R (Data Storytelling)
- Web applications with R-Shiny (Sample size)
- Web tutorials with R-learnr
- Complex procedures (Compare Groups Project)
- Version control with Git and GitHub
Learning activities and methodology
| Title | Hours | ECTS | Learning outcomes |
|---|---|---|---|
| Practical sessions | 30 | 1.2 | CM14, KM17, SM16, SM17 |
| Theory | 30 | 1.2 | CM14, KM17, SM16, SM17, SM18, SM19 |
| Practical cases | 15 | 0.6 | CM14, KM17, SM16, SM17 |
The course will follow the following working methodology:
- Theoretical classes
- Practical software sessions
- Evaluation of practical case studies
Assessment
Continuous assessment activities
| Title | Weight | Hours | ECTS | Learning outcomes |
|---|---|---|---|---|
| Consulting group work - Individual contribution | 20 | 12.5 | 0.5 | CM14, KM17, SM16, SM18, SM19 |
| Individual Project 2 | 15 | 12.5 | 0.5 | CM14, KM17, SM16, SM17 |
| Group project 1 | 15 | 12.5 | 0.5 | CM14, KM17, SM16, SM17 |
| Consulting group work | 20 | 12.5 | 0.5 | CM14, KM17, SM16, SM18, SM19 |
| Group project 2 | 15 | 12.5 | 0.5 | CM14, KM17, SM16, SM17 |
| Individual Project 1 | 15 | 12.5 | 0.5 | CM14, KM17, SM16, SM17 |
The course will be assessed through the resolution of different practical cases, carried out in groups or individually.
- Individual practical cases
- Sample size and simulations
- Kaggle
- Group practical cases
- Advanced graphics / web applications
- Technical article
- Group work
- Report
- Oral presentation
- Participation in assessment activities
- Contributions
- Questions
Single Assessment
This course does not provide for the possibility of single assessment.
Not Assessable
If the student has not submitted more than 60% of the weighted assessment activities, it will be considered that there is insufficient assessment evidence and the grade assigned will be Not Assessable.
Use of AI tools
For this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively in autonomous activities for support tasks (bibliographic or information searches, text correction, and statistical programming correction). Students must clearly identify which parts have been generated using this technology, specify the tools used, and include a critical reflection on how these have influenced the process and final outcome of the activity. Lack of transparency in the use of AI will be considered a breach of academic integrity and may result in a partial or total penalty on the activity's grade, or more severe sanctions in serious cases.
Bibliography
Cabrera, J.; McDougall A. (2002). Springer-Verlag New York.Statistical Consulting
Statistical Rules of Thumb - Gerald Van Belle - Wiley Series in Probability and Statistics
Common Errors in Statistics (and How to Avoid Them) - Good, Hardin - Wiley
SAS and R: Data Management, Statistical Analysis, and Graphics - Kleinman , Horton - Chapman and Hall
SAS for Mixed Models, Second Edition - Little et al - SAS Publishing
Software
SAS, R, Python, Latex, Markdown, RShiny
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 | 1 | Catalan | first semester | afternoon |
| (SEM) Seminars | 1 | Catalan | first semester | afternoon |