Identifying Data 2022/23
Subject (*) Statistical Analysis of Complex Data Code 614G02031
Study programme
Grao en Ciencia e Enxeñaría de Datos
Descriptors Cycle Period Year Type Credits
Graduate 1st four-month period
 Fourth Optional 6
Language
Spanish
Galician
Teaching method Face-to-face
Prerequisites
Department
Coordinador
López Cheda, Ana
 E-mail
ana.lopez.cheda@udc.es
Lecturers
López Cheda, Ana
 E-mail
ana.lopez.cheda@udc.es
Web http://https://dm.udc.es/modes/
General description Esta materia proporciona un primeiro contacto do alumnado coas principais técnicas estatísticas para analizar problemas con datos faltantes, datos funcionais, datos censurados ou datos sesgados. Estudaranse os principais mecanismos que provocan a falta de información e aplicaranse as técnicas presentadas a conxuntos de datos reais ou simulados. Analizaranse as limitacións de cada metodoloxía e realizarase diagnose e interpretación dos resultados en termos do problema proposto.

Study programme competencies
Code Study programme competences
A3 CE3 - Capacidade para a análise de datos e a comprensión, modelado e resolución de problemas en contextos de aleatoriedade.
A17 CE17 - Capacidade para a construción, validación e aplicación dun modelo estocástico dun sistema real a partir dos datos observados e a análise crítica dos resultados obtidos.
A20 CE20 - Coñecemento das ferramentas informáticas no campo da análise dos datos e modelización estatística, e capacidade para seleccionar as máis adecuadas para a resolución de problemas.
B2 CB2 - Que os estudantes saiban aplicar os seus coñecementos ao seu traballo ou vocación dunha forma profesional e posúan as competencias que adoitan demostrarse por medio da elaboración e defensa de argumentos e a resolución de problemas dentro da súa área de estudo
B3 CB3 - Que os estudantes teñan a capacidade de reunir e interpretar datos relevantes (normalmente dentro da súa área de estudo) para emitir xuízos que inclúan unha reflexión sobre temas relevantes de índole social, científica ou ética
B4 CB4 - Que os estudantes poidan transmitir información, ideas, problemas e solucións a un público tanto especializado como non especializado
B6 CG1 - Ser capaz de buscar e seleccionar a información útil necesaria para resolver problemas complexos, manexando con soltura as fontes bibliográficas do campo.
B7 CG2 - Elaborar adecuadamente e con certa orixinalidade composicións escritas ou argumentos motivados, redactar plans, proxectos de traballo, artigos científicos e formular hipóteses razoables.
B8 CG3 - Ser capaz de manter e estender formulacións teóricas fundadas para permitir a introdución e explotación de tecnoloxías novas e avanzadas no campo.
B9 CG4 - Capacidade para abordar con éxito todas as etapas dun proxecto de datos: exploración previa dos datos, preprocesado, análise, visualización e comunicación de resultados.
B10 CG5 - Ser capaz de traballar en equipo, especialmente de carácter multidisciplinar, e ser hábiles na xestión do tempo, persoas e toma de decisións.
C1 CT1 - Utilizar as ferramentas básicas das tecnoloxías da información e as comunicacións (TIC) necesarias para o exercicio da súa profesión e para a aprendizaxe ao longo da súa vida.
C4 CT4 - Valorar a importancia que ten a investigación, a innovación e o desenvolvemento tecnolóxico no avance socioeconómico e cultural da sociedade.

Learning aims
Learning outcomes Study programme competences
Know and understand the basics of missing data A3
A20
 B6
 C1
C4
To know the main techniques to analyse problems with missing data A3
A17
A20
 B3
B4
B9
 C1
To know the main techniques to analyse functional data A3
A17
A20
 B3
B4
B9
 C1
To know the main techniques to analyse censored data A3
A17
A20
 B3
B4
B9
 C1
To know the main techniques to analyse problems with biased data A3
A17
A20
 B3
B4
B9
 C1
To be able to apply different techniques for missing data, functional data, censored data and biased data to a real or a simulated dataset A20
 B2
B3
B4
B9
 C1
To be able to interpret the results and to know the limitations of the different methods A3
 B6
B7
B8
B10
 C1
C4

Contents
Topic Sub-topic
Introduction to missing data Challenges and problems with missing data
Missing data mechanisms: missing at random (MAR) and missing completely at random (MCAR)
The consequences of discarding missing data
Imputation methods Mean imputation
Single imputation methods
Maximum likelihood multiple imputation under MAR
Expectation{Maximization (EM) algorithm
Multiple imputation methods under MAR
Introduction to functional data Motivation and examples
Functional data registration and smoothing
Metrics and semimetrics for functional data
Representing functional data: basis expansions
Functional data analysis Estimation of mean and covariance operator
On the concept of depth for functional data: functional anomaly detection
Functional principal component analysis
Functional linear models
Censored data Missing data and censoring
The consequences of discarding censored data
Parametric estimation for censored data
Nonparametric estimation for censored data: the Kaplan-Meier estimator
Cox model: conditional survival
Biased data Selection bias: length, time and size
The consequences of disregarding bias
Mean and variance estimation for biased data
Likelihood principle for biased data
Situations with unspecified bias function

Planning
Methodologies / tests Competencies Ordinary class hours Student’s personal work hours Total hours
Oral presentation A3 B2 B3 B4 C4 21 31.5 52.5
ICT practicals A17 A20 A3 B2 B3 B4 B6 B7 B8 B9 B10 C1 7 24.5 31.5
Supervised projects A17 A20 A3 B2 B3 B4 B6 B7 B9 B10 C1 3.5 15.75 19.25
Problem solving A17 B2 B7 B8 B10 7 28 35
Mixed objective/subjective test A20 A3 B2 B3 B4 B8 C1 1.5 3 4.5
Mixed objective/subjective test A20 A3 B2 B3 B4 B8 C1 1.5 3.75 5.25
 
Personalized attention 2 0 2
 
(*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies Description
Oral presentation Presentation using the computer
ICT practicals Statistical data analysis using R
Supervised projects Statistical analyses of some databases applying the studied methodologies
Problem solving Deciding statistical tools and strategies for problem solving with missing data, functional data, censored data or biased data
Mixed objective/subjective test Test related to concepts and/or practical exercises with R (in the middle of the semester)
Mixed objective/subjective test Test related to concepts and/or practical exercises with R (official exam in January)

Personalized attention
Methodologies
Problem solving
ICT practicals
Supervised projects
Description
Attendance and participation in the theoretical lessons
Practical cases of study using R
Problem solving using R
Test related to theoretical and/or practical questions

Assessment
Methodologies Competencies Description Qualification
Mixed objective/subjective test A20 A3 B2 B3 B4 B8 C1 Test related to concepts and/or practical exercises with R (official exam in January) 40
Supervised projects A17 A20 A3 B2 B3 B4 B6 B7 B9 B10 C1 Statistical analyses of some databases applying the studied methodologies 30
Mixed objective/subjective test A20 A3 B2 B3 B4 B8 C1 Test related to concepts and/or practical exercises with R (in the middle of the semester) 30
 
Assessment comments

The assessment scoring will be the following:

  • Work in pairs related to Topics 3-4: 1.75 points (1 point solving the practical exercise in R and 0.75 points the oral presentation).
  • Work in pairs related to Topic 5: 1.25 points (0.75 points solving the practical exercise in R and 0.5 points the oral presentation).
  • Concept/practical test related to Topics 3, 4 and 5: 3 points. It will take place in the middle of the quadrimester. Students will avoid this test in the official exam if they obtain, at least, a score of 3.5 out of 10 in this first exam, unless they want to get a higher score (in this case, the score will be the one obtained in the official exam). Moreover, if students do not attend the first exam or if they obtain a score lower than 3.5 out of 10, then they will be evaluated (the day of the official exam) of this part as well as the Concept/practical test related to Topics 1, 2 and 6. 
  • Concept/practical test related to Topics 1, 2 and 6: 4 points. It will take place the date of the official exam. In order to pass the subject it is necessary to obtain a score of at least 3.5 out of 10 in this test.

To pass the subject it is necessary to obtain a score of at least 5 out of 10 overall.

On the second opportunity (July), students must attend the exams in which they obtained a lower score than 3.5 out of 10 in January tests. If they want to get a higher score, then the final score will be the one obtained in July.

Only students that didn't take any test will be qualified as NON ATTENDANT in the first opportunity (January-February). In July (2nd opportunity) only students that didn't take the final exam will be qualified as NON ATTENDANT.

If a student wants to take a test in a specific official language (Spanish or Galician), he/she must inform the professor at least 1 week in advance.

Sources of information
Basic Hosmer D. W., Lemeshow S., May S. (2008). Applied survival analysis: regression modeling of time-to-event data. Wiley-Interscience
Qin J. (2017). Biased sampling, over-identified parameter problems and beyond (Vol. 5). Springer
Ramsay J. O., Silverman B. W. (2005). Functional Data Analysis. 2nd Edition. Springer
Ferraty F., Vieu P. (2006). Nonparametric functional data analysis : theory and practice. Springer
Cox D. R. (2005). Some sampling problems in technology. . Selected Statistical Papers of Sir David Cox
Little R. J., Rubin D. B. (2019). Statistical analysis with missing data (Vol. 793). John Wiley & Sons
Lee E. T., Wang J. W. (2013). Statistical Methods for Survival Data Analysis. 4th Edition. Wiley

Complementary Therneau T. (2021). A Package for Survival Analysis in R. CRAN
Van Buuren, S. (2018). Flexible imputation of missing data. CRC Press
Therneau T. M., Grambsch P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer
Febrero-Bande M, Oviedo de la Fuente M. (2012). Statistical Computing in Functional Data Analysis: The R Package fda.usc. Journal of Statistical Software, 51(4), 1–28


Recommendations
Subjects that it is recommended to have taken before
Statistical Analysis of Dependent Data/614G02022
Regression Models/614G02012
Statistical Modeling of High Dimensional Data/614G02013
Statistical Inference/614G02007
Probability and Basic Statistics/614G02003

Subjects that are recommended to be taken simultaneously
Spatiotemporal Data Representation and Management/614G02035
Simulation and Resampling Techniques/614G02036

Subjects that continue the syllabus
Omics Data Management and Modeling /614G02042

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.