Study programme competencies |
Code
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Study programme competences / results
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A13 |
To know the impact of technology on the different processes of the textile industry |
A19 |
To acquire the capacity to collect, select and analyse information flows; their integration in the information systems and processes of the firm; and their application to strategic and operational decision-making; always from an ethical perspective |
B1 |
That students demonstrate that they acquired and understood knowledge in a study area that originates from general secondary education and that can be found at a level that, though usually supported by advanced textbooks, also includes aspects implying knowledge from the avantgarde of its field of study |
B2 |
That students know how to apply their knowledge to their job or vocation in a professional form, and have the competencies that are usually demonstrated through elaboration and advocacy of arguments and problem resolution within their field of study |
B3 |
That students have the capacity to collect and interpret relevant data (normally within their field of study) in order to issue judgements that include a reflection upon relevant topics in the social, scientific or ethical realm |
B4 |
That students may convey information, ideas, problems and solution to the public, both specialized and not |
B5 |
That students develop those learning skills that are needed to undertake ulterior studies with a high degree of autonomy |
B8 |
Capacity to plan, organize and manage resources and operations |
B9 |
Capacity to analyse, diagnose and take decisions |
C3 |
Using ICT in working contexts and lifelong learning. |
C7 |
Developing the ability to work in interdisciplinary or transdisciplinary teams in order to offer proposals that can contribute to a sustainable environmental, economic, political and social development. |
C8 |
Valuing the importance of research, innovation and technological development for the socioeconomic and cultural progress of society. |
Learning aims |
Learning outcomes |
Study programme competences / results |
Acquisition of skills for the statistical analysis of data as support in decision making in the company, industry and research. |
A13 A19
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B1 B2 B3 B9
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C3
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Knowledge of the basic concepts of statistics, as well as those more specific related to the industry, management and business analytics, that allow the correct definition of real problems, the adequate collection of data and the application of the appropriate techniques. |
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B1 B4 B5 B8 B9
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Acquisition of skills for data analysis and decision making using statistical software, as well as for group work in multidisciplinary projects. |
A19
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B2 B3 B4 B9
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C3 C7 C8
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Contents |
Topic |
Sub-topic |
Descriptive statistics of a variable and introduction to the use statistical software. |
Basic concepts of descriptive statistics.
Characteristics measures of position, dispersion and shape.
Graphics.
Introduction to R statistical software. |
Descriptive statistics of more than one variable.
|
Measures of association and correlation.
Graphics for two or more variables.
Linear regression.
Unsupervised classification (cluster). |
Probability
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Experiments and events.
Probability definition and properties.
Conditioned robability.
Total probability and Bayes theorem. |
Random variables.
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Discrete random variables.
Continuous random variables. |
Main probability distributions.
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Binomial distribution.
Negative binomial distribution.
Hypergeometric distribution.
Poisson distribution.
Uniform distribution
Normal distribution.
Exponential distribution
Distributions associated with the normal. |
Statistical inference.
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Point estimates.
Confidence intervals.
Hypothesis testing.
Inference in linear regression models. |
Basic techniques of statistical quality control.
|
Basic concepts.
Six Sigma Methodology
Ishikawa's diagram.
Pareto chart.
Control charts
Process capacity analysis. |
Time series.
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Definition.
Components
Estimation and prediction.
|
Planning |
Methodologies / tests |
Competencies / Results |
Teaching hours (in-person & virtual) |
Student’s personal work hours |
Total hours |
Guest lecture / keynote speech |
B1 B3 B4 B5 B9 C8 |
67 |
0 |
67 |
Problem solving |
B1 B2 B3 B4 B5 B8 B9 |
16.5 |
16.5 |
33 |
ICT practicals |
A19 B2 B3 B4 B9 C3 |
21.5 |
21.5 |
43 |
Multiple-choice questions |
B1 B2 B3 B4 B9 |
2 |
0 |
2 |
Supervised projects |
A13 A19 B2 B3 B8 B9 C3 C7 C8 |
1 |
0 |
1 |
Events academic / information |
A13 B1 C8 |
4 |
0 |
4 |
|
Personalized attention |
|
0 |
|
0 |
|
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
Methodologies |
Methodologies |
Description |
Guest lecture / keynote speech |
Keynote speech will be given in which the teacher will explain, with the help of appropriate audiovisual media, the main contents of the subject. |
Problem solving |
Seminars consisting of problem solving will be held in small groups, in order to set the concepts shown in the lectures and provide information about the methodologies for the practical resolution of problems through statistics. |
ICT practicals |
In the practical classes the student will be introduced to the handling of the statistical software R. Computational tools for the resolution of problems will be shown and applied through the statistical analysis of data, either from simulated or real data. |
Multiple-choice questions |
At the end of the course, there will be a test of 15 to 20 questions, both practical and theoretical. |
Supervised projects |
Students will be proposed to develop a group work (2 to 4 people) consisting of the application of statistical and computational tools shown in class to a particular case study, described by real or simulated data. You can also perform a work consisting of the description of a case study in the industry and the management, in which the resolution of a real problem is carried out based on the application of statistical techniques. Another alternative will be the use of statistical tools and data analysis, its usefulness and its application in industry and business management, in particular, those related to the fashion sector. |
Events academic / information |
Presentations, lectures, small courses or seminars from professionals in the fashion sector and/or data analysis will be presented to complement the teaching and providing a global perspective on the importance and usefulness of data analysis in this industry. Participation in these events is mandatory. |
Personalized attention |
Methodologies
|
Guest lecture / keynote speech |
|
Description |
There will be keynote lectures in which the teacher will explain, with the help of appropriate audiovisual media, the main contents of the subject, promoting the debate in class. In the particular case of students with academic dispensation, you can perform face-to-face and virtual tutorials (email, video conference), which allow the student to satisfactorily follow the subject. |
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Assessment |
Methodologies
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Competencies / Results |
Description
|
Qualification
|
Multiple-choice questions |
B1 B2 B3 B4 B9 |
It will consist of 15 to 20 test questions with three possible answers. |
60 |
Supervised projects |
A13 A19 B2 B3 B8 B9 C3 C7 C8 |
These works will be carried out in groups of 2 to 5 people, applying statistics to real or simulated data, reviewing a topic on statistics or data science or even regarding a specific application of statistics in management and industry. |
20 |
Problem solving |
B1 B2 B3 B4 B5 B8 B9 |
Student attendance and performance in problem classes and / or delivery of resolved problems will be evaluated. |
10 |
ICT practicals |
A19 B2 B3 B4 B9 C3 |
The attendance and performance of the student in the practical classes will be evaluated, as well as the delivery of works related to the application of the statistical software R. |
10 |
|
Assessment comments |
Evaluation at the first opportunity The mark of the multiple-choice test will be weighted with the corresponding grade for the delivery of practical work related to the practices carried out with the R statistical software, with the attendance to practical classes (ICT practices and exercises) and systematic observation of the performance of the student, with the delivery of exercises and with the accomplishment of supervised projects. Second chance evaluation The evaluation will be done following the same procedure as in the first opportunity. Early exam session All these remarks are applied to the December exam session. "No presentado" grade For any of the two opportunities to pass the subject, the "NO PRESENTADO" grade will be given to the students who did non take the multiple-choice final test. Students with recognition of part-time dedication and/or academic exemption of attendance In the case of students with recognition of part-time dedication and/or academic exemption of attendance that decides not to attend classes, they will be evaluated in the two opportunities as the rest of the students who are in a similar situation. Fraud Fraud in tests or evaluation activities will directly imply the failure grade "0" in the subject in the corresponding call, thus invalidating any grade obtained in all the evaluation activities for the extraordinary call. Observation related to the attendance at academic activities/events Attendance at the academic activities programmed in the context of the subject will be compulsory and will be taken into account in the assesment. Attention to diversity The subject may be adapted to students who require the adoption of measures aimed at supporting diversity (physical, visual, auditory, cognitive, learning or mental health-related). If this is the case, they should contact the services available at the UDC/at the centre: within the official deadlines stipulated prior to each academic term, with the Diversity Attention Unit (https://www.udc.es/cufie/ADI/apoioalumnado/); otherwise, with the ADI tutor of the Faculty of Humanities.
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Sources of information |
Basic
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Umesh R Hodeghatta, Umesha Nayak (2016). Business Analytics Using R - A Practical Approach. Springer
Cao R., Franciso M, Naya S., Presedo M., Vázquez M., Vilar J.A. y Vilar J.M. (2005). Introducción a la Estadística y sus aplicaciones. Pirámide
María Dolores Ugarte, Ana F. Militino, and Alan T. Arnholt (2015). Probability and Statistics with R. CRC Press
Robert H. Shumway, David S. Stoffer (2011). Time Series Analysis and its Applications. Springer |
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Complementary
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Recommendations |
Subjects that it is recommended to have taken before |
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Subjects that are recommended to be taken simultaneously |
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Subjects that continue the syllabus |
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