Course Topics
- Time series analysis, modeling and forecasting - Dimensionality reduction techniques: principal component analysis - Identifying underlying structures: factor analysis - Unsupervised learning: distance-based clustering algorithms - Model validation and re-sampling - Applications with the software R and the programing language Python

Propaedeutic courses
Prerequisites It is highly recommended both Mathematics for EPE and Statistics for EPE.

Teaching format
Lectures and laboratory sessions.

Educational objectives
ILO (Intended Learning Outcomes) ILO 1 Knowledge and understanding: ILO 1.1 basic knowledge of data management and computer programming for statistical and econometric analysis of socio-economic data; ILO 1.2 knowledge of the technical vocabulary of the subjects in this learning area. ILO 2 Ability to apply knowledge and understanding: ILO 2.1 ability to manage simple databases and carry out analysis of socio-economic data with the support of software; ILO 2.2 ability to use quantitative methods to solve problems in the economy; ILO 2.3 ability to read, write and communicate in the technical language of quantitative methods in the three official languages of instruction ILO 3 Making judgement ILO 3.1 Acquisition of the capacity for judgement and of the methodological tools useful for the critical analysis of data, sources, assumptions and implications of scientific practice, of the political, ethical and legal context within which economic phenomena are set and with which they interact ILO 4 Communication skills ILO 4.1 Proficiency (oral and written) in Italian, German and English, including translation between these languages. Intercultural competence. Conceptual awareness, synthesis and written expression, particularly in the drafting of scientific or science-based documents ILO 5 Learning skills ILO 5.1 Promotion of critical thinking and analytical skills to focus on complex problems in their long-term dynamics and the variety of their implications, including ethical ones

Additional educational objectives and learning outcomes
Knowledge and understanding of multivariate data and time series data and several techniques to analyze them and get information on the phenomena of interest. Applying knowledge and understanding of advanced quantitative methods to describe and analyze economic and social phenomena through statistical software. Making judgments on models and statistical tools useful for advanced data analysis. Communication skills in presenting in a correct and concise way methods and results of a statistical analysis. Learning skills of different statistical methods useful in data science.

Assessment
Written exam with theoretical questions, problem-solving exercises, and interpretation of analysis results in R or Python. A voluntary midterm (ILOs 1-5) and obligatory final exam (ILOs 1-5) , both written. The midterm grade can be rejected in which case you will take the full final exam (ILOs 1-5). This exam format applies to both attending and non-attending students.

Evaluation criteria
Attending and non-attending students 100% written exam consisting of theoretical questions and data analysis tasks. The final grade will be a weighted average of the written midterm exam (50%) and the written final exam (50%). Students who do not take the midterm or reject their midterm grade will be given a longer exam that will count for 100% of the final grade. Criteria for written exam: correctness and clarity of answers, knowledge and understanding of statistical methods, ability to interpret outputs and to correctly use formal code.