Academic year
2018/2019 Syllabus of previous years
Official course title
Course code
CT0427 (AF:248800 AR:136461)
On campus classes
ECTS credits
Degree level
Bachelor's Degree Programme
Educational sector code
2nd Semester
Course year
Go to Moodle page
This course belongs to the interdisciplinary educational activities of the Data Science curriculum of the Bachelor in Informatics. The course aims at providing students with the basic instruments of statistical inference and data analysis. The objective of the course is to develop skills to solve statistical questions arising in technology, science, medicine, economics and business. Special attention will be paid to the integration of methodology with computational tools through the R language. The achievement of the educational objectives of the course will provide the student with the tools to learn more advanced data science methods.
Regular and active participation in the teaching activities offered by the course and in independent research activities will enable students to:
1. (knowledge and understanding)
-- acquire knowledge and understanding regarding basic inferential and data analysis methods
2. (applying knowledge and understanding)
-- synthetize and model phenomena characterized by variability and uncertainty
-- identify and fit statistical models for prediction
-- use statistical software for manipulation, synthesis and analysis of data
3. (making judgements)
-- correctly evaluate the outcomes of statistical software analyses
Students are assumed to have reached the learning objectives of the course Probability e Statistics ( although it is not formally required to have passed the examination. It is important that the students have a solid familiarity with the main properties and operations involving discrete and continuous random variables.
The course program includes presentation and discussion of the following subjects:

1. Descriptive statistics
2. Graphical data summary
3. Point estimation
4. Interval estimation
5. Hypothesis testing
Methods will be illustrated with simulated and real data using the R language (
- Baron M (2014). Probability and Statistics for Computer Scientistis. Second Edition. CRC Press. Chapters 8-9-10
- Additional readings and materials distributed during the course through the Moodle platform
The achievement of the course objectives is assessed through a written exam. The exam consists of four exercises designed to measure
1. the theoretical knowledge of the course topics,
2. the ability to apply them for solving real data problems.
The maximal score for each exercise is 8 points. The final score is the sum of the scores of the four exercises. A total score exceeding 30 corresponds to 30 with honors. During the written test the use of books, notes, or electronic media is *not* allowed.
Conventional theoretical lectures complemented by exercise classes and discussion of case studies. Teaching material prepared by the lecturer will be distributed during the course through the Moodle platform. The statistical software used in the course is R (

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development

Definitive programme.
Last update of the programme: 09/01/2019