To acquire knowledge and understanding to 1. Analyze univariate and bivariate data 2. Solve probability problems 3. Analyze problems involving random phenomena 4. Use random variables 5. Be able to solve problems using a statistical software. 1. Capacity for analysis and synthesis. 2. Knowledge of the use of statistical software. 3. Resolution of problems. 4. Teamwork. 5. Critical reasoning. 6. Oral and written communication.

K3: To know fundamental contents in their area of study starting from the basis of general secondary education and reaching a level proper of advanced textbooks, including also some aspects of the forefront of their field of study. K4: Knowledge of basic scientific and technical subjects that qualify for the learning of new methods and technologies, as well as providing a great versatility to adapt to new situations, in the field of data storage, management and processing. K5: Ability to understand and relate fundamental concepts of probability and statistics and be able to represent and manipulate data to extract meaningful information from them S1: To plan and organize team work making the right decisions based on available information and gathering data in digital environments. S5: Ability to correctly identify predictive problems corresponding to certain objectives and data, based on knowledge of algorithms, modeling, prediction and filtering, and to use the basic results of regression analysis as the basis for prediction methods S16: Ability to synthesize the conclusions obtained from the analyses carried out and present them clearly and convincingly both in writing and orally to both specialized and non-specialized audiences C5: Be able to analyze and synthesize basic problems related to engineering and data science, elaborate, defend and efficiently communicate solutions individually and professionally, applying the knowledge, skills, tools and strategies acquired or developed in their area of study.

The objective of the course is that the student learns basic statistical concepts and tools that will allows him to: a) analyze, summarize, and draw conclusions from real world data and b) understand the concepts of uncertainty and probability and apply distribution models to solve relevant problems. 1. Introduction 1.1. Concepts and use of Statistics 1.2. Statistical terms: populations, subpopulations, individuals and samples 1.3. Types of variables 2. Analysis of univariate data 2.1. Representations and graphics of qualitative variables 2.2. Representations and graphics of quantitative variables 2.3. Numerical summaries 3. Analysis of bivariate data 3.1. Representations and graphics of qualitative and discrete data 3.2. Representations and numerical summaries of quantitative data: covariance and correlation 4. Introduction to Probability 4.1. Introduction 4.2. Random phenomena 4.3. Definition of probability and properties 4.4. Assessment of probabilities in practice 4.5. Conditional probability 4.6. Bayes Theorem 5. Random variables 5.1. Definition of random variable 5.2. Discrete random variables 5.3. Continuous random variables 5.4. Characteristic features of a random variable 5.5. Random vectors 5.6. Independence of random variables 6. Distribution models 6.1. Binomial distribution 6.2. Geometric distribution 6.3. Poisson distribution 6.4. Uniform distribution (continuous) 6.5. Exponential distribution 6.6. Normal distribution (with CLT) 7. Linear regression 7.1. Introduction 7.2. Simple linear regression 7.3. Multiple linear regression

- Lectures: introducing the theoretical concepts and developments with examples, 2.2 ECTS - Problem solving sessions: 2.2 ECTS - Computer (practical) sessions: 0.6 ECTS -- 4 SESSIONS - Evaluation sessions (continuous evaluation and final exam): 1 ECTS