INTRODUCTION TO DATA ANALYTICS
- Overview
- Assessment methods
- Learning objectives
- Contents
- Delivery method
- Teaching methods
- Contacts/Info
In order to successfully attend the course it is recommended to possess a
good knowledge of the notions of statistics covered in the course of
“Statistica per l’Economia”.
The exam consists of two parts: a theoretical exam (50% of the final
mark) and a practical exam (50% of the final mark).
-The practical exam is a computer-based project. It consists of the
analysis of a dataset using the techniques illustrated during the course,
and on the interpretation of the obtained results.
-The (general) theoretical exam is a written exam (open questions). It
aims at assessing the knowledge on the techniques introduced in the
course, also with respect to the quantities that can be obtained using a
software.
-Alternatively, students can give two partial exams instead of the general
theoretical exam.
-The final grade is obtained by combining the grades taken in the
different parts.
-Assessment methods are the same for attending and not attending
students.
Modern graduates need to use data to a much greater extent compared
to their past counterparts. Data management and data analysis are
becoming more and more relevant in any field. In this course, students
are introduced to big datasets, and gain an applied understanding of the
most relevant techniques of multivariate data analysis. The key goal of
the course is to illustrate methods useful to analyze and summarize the
most salient features of large data sets with respect to both the variables
and the cases. The course features hands-on classes, where the
application of each technique is discussed using real dataset and the
statistical software R.
At the end of the course student will be able to:
- Individuate the technique most suitable to simplify relevant information
in a dataset with reference to a specific goal of analysis.
- Justify the adoption of a specific type of analysis and the choices made
during the analysis.
- Compare the results obtained using different approaches, evaluate the
stability of results.
- Prepare data for the analysis.
- Analyze data using a statistical software.
- Interpret and critically analyze results, emphasizing the most relevant
conclusions both from a technical and from an interpretative point of
view.
The course focuses on the following main topics:
- Description and summary of multivariate data.
- Multivariate linear regression model.
- Logistic regression model.
- Model selection
- Variables reduction: Factor Analysis.
- Finding groups in data and Cluster Analysis.
- Discriminant analysis
- Empirical application to real data on economic and financial topics using
R software
The course is articulated into different types of teaching methods:
- Classes introducing the most relevant theoretical concepts relative to
each technique.
Non ci sono differenze tra studenti frequentanti e studenti non
frequentanti.
Testi di riferimento - Dispense e slides messi a disposizione su E-learning
Altre informazioni L’orario di ricevimento è pubblicato sulla home page del docente; si
prega di verificare eventuali avvisi di variazione. Il docente riceve
comunque al termine di ogni lezione oppure, qualora l’orario di
ricevimento ufficiale non fosse fruibile dallo studente, anche su specifico
appuntamento, previa richiesta via e-mail all’indirizzo
chiara.gigliarano@uninsubria.i.
- Classes discussing the appropriate application of the technique with
reference to a specific problem and set of data.
- Exercises. Hands-on classes (in lab). Students are guided to the analysis
of a real set of data using the statistical software R.
- Discussion (interactive class activities).
Active participation in the lessons is strongly recommended.
The office hour takes place by appointment, upon request via e-mail at
valerio.lange@uninsubria.it