Identifying Data 2020/21
Subject (*) Fundamentals of Mathematics and Data Analysis Tools Code 710G03014
Study programme
Grao en Xestión Industrial da Moda
Descriptors Cycle Period Year Type Credits
Graduate 1st four-month period
 Second Basic training 6
Language
English
Teaching method Face-to-face
Prerequisites
Department Matemáticas
Coordinador
Tarrio Saavedra, Javier
 E-mail
javier.tarrio@udc.es
Lecturers
González Rueda, Ángel Manuel
Tarrio Saavedra, Javier
 E-mail
angel.manuel.rueda@udc.es
javier.tarrio@udc.es
Web http://estudos.udc.es/gl/subject/710G04V01/710G04040/2020
General description Esta materia introduce os conceptos básicos da análise estatística de datos, desde a estatística descritiva á inferencia estatística, pasando pola introdución á probabilidade, o concepto de variable aleatoria, as series de tempo e as ferramentas fundamentais do control estatístico da calidade, enfocando a súa docencia na resolución de problemas prácticos no marco da xestión industrial da moda.
Contingency plan 1. Modificacións nos contidos
Non se modifican os contidos.
2. Metodoloxías
Metodoloxías docentes que se manteñen
• Proba de resposta múltiple.
• Traballos tutelados.
• Solución de problemas.
• Prácticas a través de TIC.
Metodoloxías docentes que se modifican
Non se modifica ningunha metodoloxía docente.
3. Mecanismos de atención personalizada ao alumnado
Ferramentas: Microsoft Teams, correo electrónico e Moodle.
Temporalización: Microsoft Teams utilizarase en horario de clases, ademais de en horario de titorías. O correo electrónico servirá de medio para resolver dúbidas e para o intercambio de ficheiros e información en xeral. Moodle servirá para intercambio de información e material da materia, ademais de ser o medio da realización de probas de resposta múltiple, ademais de traballos de avaliación continua.
4. Modificacións na avaliación
A proba de resposta múltiple puntuarase ata un máximo de 40 puntos sobre 100 e constará de 15 a 20 preguntas tipo test con tres respostas posibles.
Os traballos tutelados contarán ata un total de 20 puntos sobre 100, sendo traballos a realizar en grupos de 2 a 5 persoas, de aplicación da estatística ou a análise de datos en xeral, ou mesmo relacionado cunha aplicación específica da estatística na xestión ou a industria.
A solución de problemas puntuarase ata un máximo de 20 puntos sobre 100. Avaliarase o desempeño do alumno mediante a entrega de exercicios resoltos.
As prácticas a través de TIC representarán un 20% da nota final. Nelas avaliarase o desempeño do alumno nas clases prácticas e/ou a entrega de traballos relacionados coa aplicación do software estatístico R.
Observacións de avaliación:
A proba de resposta múltiple ou exame pasa a representar o 40% (antes o 60%), os traballos tutelados o 20% (antes o 20%), as prácticas a través de TIC o 20% (antes o 10%) e a entrega de exercicios resoltos o 20% (antes o 10%).
5. Modificacións da bibliografía ou webgrafía

Study programme competencies
Code Study programme competences
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
Acquisition of skills for the statistical analysis of data as support in decision making in the company, industry and research. A13
A19
 B1
B2
B3
B9
 C3
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. B1
B4
B5
B8
B9
Acquisition of skills for data analysis and decision making using statistical software, as well as for group work in multidisciplinary projects. A19
 B2
B3
B4
B9
 C3
C7
C8

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
 Experiments and events.
Probability definition and properties.
Conditioned robability.
Total probability and Bayes theorem.
Random variables.

 Discrete random variables.
Continuous random variables.
Main probability distributions.
Binomial distribution.
Negative binomial distribution.
Hypergeometric distribution.
Poisson distribution.
Uniform distribution
Normal distribution.
Exponential distribution
Distributions associated with the normal.
Statistical inference.
 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.

 Definition.
Components
Estimation and prediction.

Planning
Methodologies / tests Competencies Ordinary class hours 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.

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.

Assessment
Methodologies Competencies 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.

In the case of students with recognition of part-time dedication and academic exemption of attendance that decides not to attend classes, thet will be evaluated in the two opportunities as the rest of the students who are in a similar situation.

Second chance evaluation

The evaluation will be done following the same procedure as in the first opportunity.

In the case of students with recognition of part-time dedication and academic exemption of attendance that decides not to attend classes, thet will be evaluated in the two opportunities as the rest of the students who are in a similar situation.

Sources of information
Basic 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

Complementary


Recommendations
Subjects that it is recommended to have taken before

Subjects that are recommended to be taken simultaneously

Subjects that continue the syllabus

Other comments


(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.