ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 30 Interactive Classroom: 20 Total: 51
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Statistics, Mathematical Analysis and Optimisation
Areas: Statistics and Operations Research
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable | 1st year (Yes)
The objective of this course is for students to learn the basic concepts of descriptive statistics, probability, statistical inference, and regression models, which will serve as a foundation for building advanced statistical models for data analysis.
UNIT 1. DESCRIPTIVE STATISTICS
1.1 General concepts.
1.2 Frequency distributions.
1.3 Graphic representations.
1.4 Characteristic measurements: position, dispersion and shape.
1.5 Two-dimensional descriptive statistics. Contingency tables.
UNIT 2. FUNDAMENTALS OF PROBABILITY
2.1 Random experiment. Events and sample space.
2.2 Assignment and definition of probability. Operations with events.
2.3 Conditional probability. Independence of events. Remarkable results.
UNIT 3. RANDOM VARIABLES
3.1 Discrete variable. Support, probability mass function and distribution function.
3.2 Continuous variable. Probability density function and distribution function.
3.2 Characteristic measures.
3.3 Main models of discrete and continuous distributions.
3.4 Central Limit Theorem.
3.5 Approximation of distributions.
UNIT 4. INTRODUCTION TO STATISTICAL INFERENCE AND PARAMETER ESTIMATION
4.1 Introduction to Statistical Inference.
4.2 Estimation for a population.
4.3 Estimation for two populations.
4.4 Estimation by confidence intervals.
UNIT 5. HYPOTHESIS TESTING
5.1 Introduction to hypothesis testing.
5.2 Testing procedure.
5.3 Tests for one population.
5.4 Tests for two populations.
UNIT 6. INTRODUCTION TO LINEAR REGRESSION
6.1 Two-dimensional descriptive statistics for continuous variables. Dispersion diagram.
6.2 Linear regression model.
BASIC BIBLIOGRAPHY
Agresti, A., e Kateri, M. (2021). Foundations of Statistics for Data Scientists: With R and Python. CRC Press, Boca Raton.
Devore, J. L. (2005). Probabilidad y Estadística para Ingeniería y Ciencias. 6a ed. México: International Thomson Editores.
COMPLEMENTARY BIBLIOGRAPHY
Mendenhall, W. M. e Sincich, T. L. (2016). Statistics for Engineering and the Sciences. CRC Press, Boca Raton.
Peña, D. (1991). Fundamentos de Estadística. Alianza Editorial, Madrid.
Peña, D. (1993). Estadística: Modelos y Métodos. Alianza Editorial, Madrid.
Ross, S. M. (2014). Introduction to Probability and Statistics for Engineers and Scientists. Elsevier, Burlington.
The recommended bibliography is available at the USC libraries.
Upon completion of this course, students are expected to achieve the following competencies and learning outcomes as outlined in the Bachelor's Degree in Artificial Intelligence offered by the Universities of A Coruña, Santiago de Compostela, and Vigo: CG2, CG4, CB2, CB3, CB5, TR3, CE1, CE5.
As learning outcomes, students should understand the basic foundations of probability, statistical inference, and regression models. They are expected to be able to describe a random event in one and/or two statistical variables, choose appropriate graphical representations, and apply suitable statistical techniques for each case. Furthermore, they should be able to justify the relevance of a statistical test or hypothesis test in a specific application. In addition, students should be capable of correctly designing sample eligibility criteria to address a real-world problem and should be able to properly validate and, if necessary, revise statistical models. Considering all of this, upon completion of the course, students should have the foundations to build advanced statistical models for data analysis.
Lectures (30 hours): Knowledge will be delivered using slides and blackboard explanations, including sample problem solving to support student work on exercise sets. In addition to the recommended bibliography, students will have access to supplementary materials through the USC Virtual Campus. The following competencies will be addressed in lectures: general competencies (CG4), basic competencies (CB2, CB3, CB5), and specific competencies (CE1).
Practical sessions in computer labs and/or laboratories (20 hours): Students will engage in guided problem-solving using software that supports the practical exercises introduced during the course. Outside of class, students are expected to work independently on exercises to consolidate their knowledge and tackle data analysis problems and programming tasks using the same software.
Competencies developed: general (CG2, CG4), basic (CB2, CB3), transversal (TR3), and specific (CE1, CE5).
Tutorials (1 hour): Tutorials aim to support students' learning progress. Activities during tutorials will help students gain a general understanding of the subject while identifying areas for improvement.
Competencies developed: general (CG4), basic (CB2, CB3, CB5), and specific (CE1, CE5).
The distribution of lecture hours (30 hours) and seminars (20 hours), by topic, is as follows, in one-hour sessions:
Unit 1. Descriptive statistics: 5 lectures, 4 seminars.
Unit 2. Foundations of probability: 4 lectures, 2 seminars.
Unit 3. Random variables: 8 lectures, 4 seminars.
Unit 4. Introduction to inference and parameter estimation: 5 lectures, 4 seminars.
Unit 5. Hypotheses testing: 4 lectures, 4 seminars.
Unit 6. Introduction to linear regression: 4 lectures, 2 seminars.
The assessment of the course will consist of continuous assessment and a final theoretical/practical exam. The weight of each component is detailed below:
Continuous assessment (30%): This will include two written in-person activities focusing on practical exercises and problems related to the course content. Dates for these activities will be announced in class and on the virtual classroom at least one week in advance. The continuous assessment grade will be the average of the two activity scores. If one activity is not completed, it will be scored as zero in the average. This grade will be retained for both exam opportunities within the same academic year.
Assessed competencies: CG2, CG4, CB2, CB3, TR3, CE1, CE5.
Final exam (70%): The final exam will consist of various theoretical and practical questions and exercises, which may include interpreting results obtained using the statistical software introduced in the interactive sessions.
Assessed competencies: CG2, CG4, CB2, CB3, CB5, TR3, CE1, CE2, CE5.
According to Article 1 of the USC Regulations on class attendance in official undergraduate and master's degrees, attendance will not have any specific impact on the course grade, nor will it be a requirement for passing the course or sitting the exams.
A student will be considered “presented” if they participate in activities allowing them to obtain at least 50% of the final grade. The weight of continuous assessment will remain the same in the second exam opportunity. For repeating students, the evaluation will follow the same structure, but no grades from the previous academic year (including continuous assessment) will be carried over.
In cases of fraudulent conduct during exercises or exams, the USC regulations on academic performance assessment and grade review will apply.
In this subject, the students have the following teaching given by the professors: 30 hours of expository teaching, 20 hours of practical sessions in the computer room and/or laboratory and 1 hour of tutorials. The students must dedicate, in addition, 60 hours to deepen the knowledge of the expository classes and 39 to the resolution of practical problems. During these hours, the acquired knowledge must be deepened, through the review of concepts, practice of problem solving and the consultation of the recommended bibliography.
Students are advised to have basic knowledge of Algebra, as it will facilitate understanding of the course content and progress in the proposed activities. Familiarity with basic computing tools for calculation or programming is also recommended.
Regular attendance at lectures and interactive sessions will greatly benefit students’ progress. It is also advisable to complete the activities proposed by the instructor, such as problem-solving, reviewing materials, and practical exercises, in order to consolidate learning. Students are also encouraged to make use of tutorials to clarify doubts and receive personalized guidance.
The course will make use of the Virtual Campus as the main platform for communication with students and for sharing materials.
The R statistical software will be used to carry out practical exercises and activities related to the course content (it can be downloaded for free at http://www.r-project.org/).
The primary language of instruction will be Galician.
Beatriz Pateiro Lopez
- Department
- Statistics, Mathematical Analysis and Optimisation
- Area
- Statistics and Operations Research
- Phone
- 881813185
- Category
- Professor: University Lecturer
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10:00-11:00 | Grupo /CLE_01 | Galician | IA.11 |
Thursday | |||
10:00-12:00 | Grupo /CLIL_02 | Galician | IA.13 |
Friday | |||
10:30-12:30 | Grupo /CLIL_01 | Galician | IA.11 |
12:30-13:30 | Grupo /CLE_01 | Galician | IA.11 |
05.18.2026 09:15-14:00 | Grupo /CLE_01 | IA.01 |
05.18.2026 09:15-14:00 | Grupo /CLIL_01 | IA.01 |
05.18.2026 09:15-14:00 | Grupo /CLIL_02 | IA.01 |
05.18.2026 09:15-14:00 | Grupo /CLIL_03 | IA.01 |
05.18.2026 09:15-14:00 | Grupo /CLIL_01 | IA.02 |
05.18.2026 09:15-14:00 | Grupo /CLIL_02 | IA.02 |
05.18.2026 09:15-14:00 | Grupo /CLIL_03 | IA.02 |
05.18.2026 09:15-14:00 | Grupo /CLE_01 | IA.02 |
07.07.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |
07.07.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
07.07.2026 16:00-20:30 | Grupo /CLIL_02 | IA.01 |
07.07.2026 16:00-20:30 | Grupo /CLIL_03 | IA.01 |