INTRODUCTION TO DATA ANALYTICS
- Overview
- Assessment methods
- Learning objectives
- Contents
- Bibliography
- 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.
According to how the health emergency will evolve in the coming months, the assessment methods illustrated above may undergo variations (particularly in relation to the duration of the test). Students will be promptly informed during the course.
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
- Slides and notes available on e-learning
- Further readings:
1. De Lillo A., Argentin G., Lucchini M., Sarti S., Terraneo M.
“Analisi Multivariata per le Scienze Sociali”, Pearson
Education.
2. Fabbris Luigi ”Statistica multivariata. Analisi esplorativa dei
dati”, McGraw-Hill.
The course is articulated into different types of teaching methods:
- Classes introducing the most relevant theoretical concepts relative to each technique.
- 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.
Information about the office hour is available on the Professor's home page.