Data Analysis
A.Y. 2024/2025
Learning objectives
The course aims to provide students with the basic skills for collecting, treating and analysing information in the form of data. More in detail, students will learn how to find and download data on politics, society, economics and law using some of the most important online data hubs, how to build a dataset, and how to analyse - that is, describe, summarise and present - the information contained in a dataset using statistics.
Expected learning outcomes
After taking this course, students will be able to find and download data from the internet, to organise these data in the form of a dataset, and to use the software Microsoft Excel to perform descriptive statistical analysis, including the creation of tables and graphs to summarise and present the information contained in a dataset.
Lesson period: First trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Course syllabus
This short course aims to provide students with the basic tools and the skills for retrieving, analysing and visualizing data. The course focuses on descriptive statistics, which will be illustrated from both a theoretical and practical viewpoint. In-class activities to practice how to perform data analysis will be conducted using Microsoft Excel.
The main topics that will be presented during the course include:
- Operationalization and measurement (from concepts to variables)
- Frequency analysis through tables and graphs
- Measures of central tendency, of variability, and of position
- Basic techniques for conducting bivariate analysis (e.g. contingency tables, correlation)
The main topics that will be presented during the course include:
- Operationalization and measurement (from concepts to variables)
- Frequency analysis through tables and graphs
- Measures of central tendency, of variability, and of position
- Basic techniques for conducting bivariate analysis (e.g. contingency tables, correlation)
Prerequisites for admission
There are no preliminary requirements different from those requested for the registration to the bachelor's degree.
Teaching methods
During each meeting, the first part of the lecture will be devoted to illustrate the various topics that will be treated during the course (please refer to the syllabus for a list). The second part of each meeting (about one-third of the available time), in turn, will be devoted to in-class activities aimed to practice together what will be presented and discussed during the first part.
Attendance is strongly recommended. Students are invited to bring their own laptop to actively participate during the in-class practical sessions. Exercises will be conducted using Microsoft Excel.
Slides, handouts with notes, and exercises for practicing at home will be shared on a weekly basis (after each lecture) on myAriel.
Further material to familiarise with Microsoft Excel will be made available on myAriel.
Attendance is strongly recommended. Students are invited to bring their own laptop to actively participate during the in-class practical sessions. Exercises will be conducted using Microsoft Excel.
Slides, handouts with notes, and exercises for practicing at home will be shared on a weekly basis (after each lecture) on myAriel.
Further material to familiarise with Microsoft Excel will be made available on myAriel.
Teaching Resources
Slides, handouts with notes, and exercises for practicing at home will be shared on a weekly basis (after each lecture) on myAriel. Further material to familiarise with Microsoft Excel and to deepen the study of the topics discussed during the course will be made available on myAriel, too. Both attending and non-attending students are encouraged to study using both slides, handouts, exercises, and the relevant additional material that will be made available on myAriel.
Assessment methods and Criteria
A written exam based on multiple choice questions and short exercises. The final grade will be "passed" or "not passed".
For attending students: Attending students have the possibility to divide the programme of the course in three parts. There will be two mid-term tests on the first and second parts of the programme, respectively, and a final test on the third part of the programme at the end of the course.
For non-attending students: Non-attending students can take the exam only in one of the official exam sessions and the exam will focus on the full programme.
For attending students: Attending students have the possibility to divide the programme of the course in three parts. There will be two mid-term tests on the first and second parts of the programme, respectively, and a final test on the third part of the programme at the end of the course.
For non-attending students: Non-attending students can take the exam only in one of the official exam sessions and the exam will focus on the full programme.
INF/01 - INFORMATICS - University credits: 3
Basic computer skills: 20 hours
Professor:
Cassani Andrea
Educational website(s)
Professor(s)
Reception:
Wednesday, 5:30 p.m., by appointment via email
Room 2, Department of Social and Political Sciences (1st floor, Building 1)