Previous knowledge: basic mathematical operations(powers, logarithms, fractions), polynomials, matrices, derivation, integration and graphic representation of functions. Basic computing skills.
This course provides the necessary skills for analyzing and interpretating data. In many areas of civil engineering the data analysis allows to make decisions in the professional performance. In particular, the contents of this course will be useful in subjects as Technology of Materials, Hydraulic Engineering and Hydrology or Maritime and Coastal Engineering; Safety, reliability, risk and life cycle performance of structures, market analysis, etc. Economics; Transportation, Urban Planning, etc.
Course competences | |
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Code | Description |
CE01 | Students can apply their knowledge in the practical solution of civil engineering problems, with capacity for the analysis and definition of the problem, the proposal of alternatives and their critical evaluation, choosing the optimal solution with technical arguments and with capacity of defense against third parties. |
CE02 | Students have the ability to broaden their knowledge and solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study. Self-study ability, to undertake further studies with a high degree of autonomy |
CE04 | Students have the ability to solve mathematical problems that may arise in engineering. Ability to apply knowledge of: linear algebra; geometry; differential geometry; differential and integral calculus; differential and partial derivative equations; numerical methods; numerical algorithms; statistics and optimization. |
CE06 | Students have a basic knowledge of the use and programming of computers, operating systems, databases and software with engineering application. |
CG01 | Students achieve general knowledge of Information and Communication Technologies (ICT). |
Course learning outcomes | |
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Description | |
Students know and interpret the fundamental measures of descriptive statistics, approximate data through regression adjustments, know the fundamentals of probability, estimate the parameters of statistical models, build confidence intervals, contrast hypotheses and make decisions. | |
Students are familiar with computer use: operative systems, databases, programming languages, and software applied to civil engineering. | |
Students are able to express correctly both orally and in writing and, in particular, they can use the language of mathematics as a way of expressing accurately the quantities and operations in civil engineering. Students get used to teamwork and behave respectfully. | |
Students use mathematical and computer tools to pose and solve civil engineering problems. | |
Students learn the most important approximations for numerical method resolution, use some statistical, data processing, mathematical calculation and visualization software packages at user level, develop algorithms and program using a high-level programming language, visualize functions, geometric shapes and data, design experiments, analyze data, and interpret results. | |
Additional outcomes | |
Description | |
Students realize that uncertaint is everywhere and engineers have to deal whit it. They develope skills analyzing the information contained in a data set by means of frequency tables, graphs and statistics. They know the most common models of discrete and continuous random variables and their relationship with engineering problems. They get the most common methods, including probability plots, for the estimation of extreme values ¿¿in engineering designs. They know return period concept for measuring engineering risk and make decisions based on probability, applying the usual estimation methods; contrast of hypothesis, regression, etc. |
Training Activity | Methodology | Related Competences (only degrees before RD 822/2021) | ECTS | Hours | As | Com | Description | |
Class Attendance (theory) [ON-SITE] | Lectures | CE01 CE02 CE04 CE06 CG01 | 1 | 25 | N | N | ||
Class Attendance (practical) [ON-SITE] | Problem solving and exercises | CE01 CE02 CE04 CE06 CG01 | 1.08 | 27 | N | N | ||
Final test [ON-SITE] | Assessment tests | CE01 CE02 CE04 CE06 CG01 | 0.16 | 4 | Y | Y | Recoverable | |
Study and Exam Preparation [OFF-SITE] | Self-study | CE01 CE02 CE04 CE06 CG01 | 3.24 | 81 | N | N | ||
Progress test [ON-SITE] | Combination of methods | CE01 CE02 CE04 CE06 CG01 | 0.16 | 4 | Y | N | ||
Total: | 5.64 | 141 | ||||||
Total credits of in-class work: 2.4 | Total class time hours: 60 | |||||||
Total credits of out of class work: 3.24 | Total hours of out of class work: 81 |
As: Assessable training activity
Com: Training activity of compulsory overcoming (It will be essential to overcome both continuous and non-continuous assessment).
Evaluation System | Continuous assessment | Non-continuous evaluation * | Description |
Progress Tests | 40.00% | 0.00% | Progress tests and on-line activities. |
Final test | 60.00% | 100.00% | Final test |
Total: | 100.00% | 100.00% |
Not related to the syllabus/contents | |
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Hours | hours |
Final test [PRESENCIAL][Assessment tests] | 4 |
Study and Exam Preparation [AUTÓNOMA][Self-study] | 75 |
Unit 1 (de 10): DESCRIPTIVE STATISTICS. Frequency tables. Graphics. Statistics. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 2 (de 10): PROBABILITY. Definition. Properties. Conditional probability. Total probability and Bayes Theorems. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 3 |
Unit 3 (de 10): RANDOM VARIABLES. One-dimensional variables: Definition. Discrete variables. Probability function. Continuous variables. Density function. Mixed variables. Probability-density function. Distribution function. Two-dimensional variables: Definition. Density, probability and distribution function for two-dimensional variables. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 4 (de 10): DISCRETE VARIABLES. One-dimensional variables: Bernouilli, binomial, negative binomial, pascal or geometric, hypergeometric, poisson. Two-dimensional variables: Multinomial. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 3 |
Unit 5 (de 10): CONTINUOUS VARIABLES. One-dimensional variables: Uniform, exponential, gamma, beta, normal, log-normal. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 6 (de 10): EXTREME DISTRIBUTIONS. Order Statistics. Distribution of an order statistic. Maximum distribution. Minimum distribution. Extreme distributions. Return period. Critical design values. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 7 (de 10): PROBABILITY PLOTS. Empirical function. Fundamentals of probability plots. Exceedance probability. Return period. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 3 |
Unit 8 (de 10): ESTIMATION. Punctual and by intervals. Estimation of proportions. Estimation of means. Estimation of variances. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 9 (de 10): HYPOTHESIS CONTRASTS. Fundamentals of the hypothesis contrast. Power of a contrast. P-value. Contrasts of proportions, means and variances. Goodness of fit tests. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 2 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Unit 10 (de 10): REGRESSION. Linear regression model. Hypothesis of the model. Matrix form of a regression problem. Analysis of variance. Hypothesis contrasts in the regression models. | |
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Activities | Hours |
Class Attendance (theory) [PRESENCIAL][Lectures] | 3 |
Class Attendance (practical) [PRESENCIAL][Problem solving and exercises] | 2 |
Global activity | |
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Activities | hours |