Accreditations
The undergraduate degree in Data Science is based on the convergence of different scientific areas  Mathematics, Statistics and Informatics – and its programme structure is structured around projects which foster both practical and theoretical thinking, with a view towards granting the student an informed, critical, and autonomous understanding of data in the face of the various dimensions of the Knowledge Society and the Digital Revolution.
The Bachelor's is wellsituated for helping students to comprehend and explore the areas of this knowledgebase. These actions support the student's progressive acquisition of independence and the capacity to respond to problems of increasing complexity.
With the synthesis, which occurs in the last two semesters, the coherence of the training program is consolidated around responsible practice and the exceptional professional skills required in order to respond to the challenges of modern society.
Programme Structure for 2024/2025
Curricular Courses  Credits  

Data in Science, Bussiness and Society
6.0 ECTS

Mandatory Courses  6.0 
Linear Algebra Fundamentals
6.0 ECTS

Mandatory Courses  6.0 
Programming
6.0 ECTS

Mandatory Courses  6.0 
Calculus Topics I
6.0 ECTS

Mandatory Courses  6.0 
Sampling and Information Sources
6.0 ECTS

Mandatory Courses  6.0 
Exploratory Data Analysis
6.0 ECTS

Mandatory Courses  6.0 
Data Structures and Algorithms
6.0 ECTS

Mandatory Courses  6.0 
Optimization for Data Science
6.0 ECTS

Mandatory Courses  6.0 
Calculus Topics II
6.0 ECTS

Mandatory Courses  6.0 
Writing Scientific and Technical Texts
2.0 ECTS

Optional Courses > Transversal Skills > Mandatory Courses  2.0 
Critical Thinking
2.0 ECTS

Optional Courses > Transversal Skills > Mandatory Courses  2.0 
Big Data Storage
6.0 ECTS

Mandatory Courses  6.0 
Computational Statistics
6.0 ECTS

Mandatory Courses  6.0 
Fundamentals of Database Management
6.0 ECTS

Mandatory Courses  6.0 
Unsupervised Learning Methods
6.0 ECTS

Mandatory Courses  6.0 
Security, Ethics and Privacy
6.0 ECTS

Mandatory Courses  6.0 
Introduction to Dynamic Models
6.0 ECTS

Mandatory Courses  6.0 
Supervised Learning Methods
6.0 ECTS

Mandatory Courses  6.0 
Heuristic Optimization
6.0 ECTS

Mandatory Courses  6.0 
Big Data Processing
6.0 ECTS

Mandatory Courses  6.0 
Applied Project in Data Science I
6.0 ECTS

Mandatory Courses  6.0 
Network Analysis
6.0 ECTS

Mandatory Courses  6.0 
Symbolic Artificial Intelligence for Data Science
6.0 ECTS

Mandatory Courses  6.0 
Web Interfaces for Data Management
6.0 ECTS

Mandatory Courses  6.0 
Stocastic Modelling
6.0 ECTS

Mandatory Courses  6.0 
Applied Project in Data Science II
6.0 ECTS

Mandatory Courses  6.0 
Management Performance Analysis
6.0 ECTS

Mandatory Courses  6.0 
Applied Final Project in Data Science
12.0 ECTS

Mandatory Courses  12.0 
Data in Science, Bussiness and Society
After the course the student should be able to achieve the Learning Outcomes (LO):
LO1: Account for different definitions of data, different data types and different research approaches that generate it.
LO2: Identify the knowledge claims underlying different interpretations of data.
LO3: Explain the difference between quantitative and qualitative approaches to data generation.
LO4: Examine the implications of data collection for research, business and society.
LO5: Discuss different debates about the implications of data for people in organizations and society.
Syllabus (S):
S1. What are data and reason with data.
S2. Different types of problems and specificities in Data Science
S3. Diverse approaches, knowledge extraction and research methodologies.
S4. Translating technical concepts to realworld concerns through researchbased language
S5. The ethical dimension in (and for) datadriven approaches.
S6. Realcase presentations.
This course is only assessed by periodic assessment and does not include exams.
Assessment components:
a) Minitests (30%): 6 minitests (5% each, the vast majority to be taken at home)
b) Project (30%): group assignment
c) Final test (40%): Written test to be taken during the 1st season, 2nd season or special season (Art. 14, RGACC)
Passing requirement: Final test >= 8 points (out of 20 points)
The final grade for the Project will depend on the code, the reports, and the student's performance in presenting their work.
Attendance is not an essential requirement for approval.
Title: Cathy O'Neil, Rachel Schutt, Doing Data Science: Straight Talk from the Frontline, 2014, ISBN: 9781449358655,
Borgman, C. L., Big data, little data, no data: scholarship in the networked world, 2015, ISBN: 9780262529914,
Rob Kitchin, The data revolution: Big data, open data, data infrastructures and their consequences, 2014, https://doi.org/10.4135/9781473909472,
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Title: Davenport, T., Harris, J., and Morison, R., Analytics at work: smarter decisions, better results. Harvard Business Review Press, USA., 2010, ISBN: 9781422177693,
Turban, E., Sharda, R., Delen, D., Decision Support and Business Intelligence Systems (9th Eds), 2010, ISBN: 9780136107293,
Davenport, T., Big Data at Work: Dispelling the Myths, Uncovering the Opportunities, 2014, ISBN: 9781422168165,
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Linear Algebra Fundamentals
LO1. Perform computations using vector and matrix algebra.
LO2. Solve and classify systems of linear equations.
LO3. Perform matrix operations. Compute, interpret and apply determinants.
LO4. Interpret abstract vector spaces as subspaces of R^n.
LO5. Identify, construct and analyze linear transforms.
LO6. Compute eigenvalues and eigenvectors. Diagonalize a matrix. Compute integer and fractional powers of diagonalizable matrices.
1 Vectors in R^n
The vector space R^n, Linear dependency, Systems of linear equations, Vector subspaces, Bases, dimension and coordinates, Inner product, norm and angle, Orthogonality
2 Matrices
Classes of matrices, The vector space structure of M_{mxn}, Matrix multiplication, Matrix transpose, The inverse of a matrix
3 Determinants
The determinant of a matrix, Definition, properties and geometric interpretation, Determinants and elementary operations, The adjunct matrix and Cramer systems, The adjunct matrix and a formula for the inverse, Cramer systems
4 Linear functions
The space L(U,V), The abstract concept of a vector space, Linear functions, Kernel, Image, The dimension theorem, Isomorphism L(U,V) = M_{mxn}, The matricial representation of a linear function, Matrix algebra vs the algebra of linear functions, change of basis
5 Eigenvalues and eigenvectors
Eigenvalues and eigenvectors of an endomorphism, Diagonalization of an endomorphism
Approval (9.5 points or more) may be achieved through either of the two possible methods:
A. Periodic evaluation:
 3 minitests in class (20%), with only the two highest grades counting towards the final grade; minimum grade of 10.0 points for the arithmetic mean of those two minitests.
 2 online quizzes (10%), submitted on the elearning platform Moodle; minimum grade of 10.0 points for the arithmetic mean of the quizzes.
 Written exam (70%) on the 1st or 2nd season; minimum grade of 8.0 points.
Minimum attendance required: 16 classes (2/3 of the total).
The arithmetic means are rounded up or down to the nearest decimal place.
The final grade is rounded up or down to the nearest integer.
B. Evaluation by Exam:
 Full grade determined by a written exam (100%), taken on either of the examination periods. If the exam grade is higher than the grade obtained through method A, the evaluation method is automatically changed to B.
About the minitests:
 There will be 3 minitestes at the beginning of certain classes (see the Class Planning  PUC).
 The minitests must be taken in person, and they will have the maximum duration of 10 minutes.
 The minitests must be taken without consultation.
 To determine the minitests grade the worst result is discarded and the arithmetic mean of the highest grades is computed.
About the online quizzes:
 There will be 2 online quizzes during the quarter (see the Class Planning), using the elearning platform Moodle.
 Each quiz may be done during a period of 72 hours. However, after starting it, each student will have only 30 minutes to complete the submission.
 Each student has a single attempt to submit a quiz.
Title: Mendes, S., Notas de Álgebra Linear , v1.5  2022, disponível na página da UC.
Mendes, S., Exercícios de Álgebra Linear , disponível na página da UC.
Mendes, S., Introdução ao MATLAB com Aplicações à Álgebra Linear , disponível na página da UC.
Mendes, S., Pedro, M., Sebentas, 2023, Disponível na página Moodle da UC,
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Title: Strang, G., "Linear Algebra and its Applications" , 4th edition, Cengage Learning, 2006.
Blyth,T.S. and Robertson, E.F. "Basic Linear Algebra", Springer, 2002.
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Programming
After obtaining approval in the course, students should be able to:
OA1. Develop functions/procedures that implement simple algorithms.
OA2. Develop code that manipulates arrays and objects.
OA3. Develop simple object classes, considering the notion of encapsulation.
OA4. Write and understand Python code.
CP1. Functions and parameters
CP2. Variables and control structures
CP3. Invocation and recursion
CP4. Procedures and input/output
CP5. Objects and references
CP6. Object classes
CP7. Composite objects
CP8. Composite object classes
CP9. Arrays
CP10. Matrices
This course is done only by Periodic Evaluation, not considering the modality of assessment by exam.
Evaluation components:
a) TPCs (15%): 6 online minitests, to do at home;
b) TEST1 (20%): Intermediate written test;
c) PROJECT (25%): Individual project;
d) TEST2 (40%): Written test to be done in 1st season, 2nd season or special season (Art. 14 RGACC)
Approval requirement: TPCs + PROJECT >= 8 points (in 20 points).
The final grade for the PROJECT is determined for each student by an oral test and will depend on the code, the report, and the student's performance in the oral.
Attendance is not an essential requirement for approval
Other relevant information:
 Questions asked in the written tests may involve aspects related to the project.
 It is not possible to pass only by taking the final exam.
 in case of failure in the 1st season, the student can take TEST2 in the 2nd season, keeping the grade of the other components
 When the grade improvement occurs in a school year different from the one in which the work was done, the grade of the components PROJECT, TPCs and TEST1 is replaced by a practical exam, to be performed on a computer before or after the written exam. Students under these conditions who wish to improve their grades should contact the UC coordinator in advance, at least 2 days before the 1st season.
Due to the current situation caused by COVID19, the evaluation process may undergo some adaptations, which will be communicated in due time, if necessary.
Title: João P. Martins, Programação em Python: Introdução à programação com múltiplos paradigmas, 2013, IST Press, https://istpress.tecnico.ulisboa.pt/produto/programacaoempythonintroducaoaprogramacaoutilizandomultiplosparadigmas/
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Calculus Topics I
At the end of this course the student should be able to:
LG1: Calculate limits of sequences
LG2. Compute derivatives and interpret the corresponding result.
LG3. Determine linear and higher order approximations.
LG4. Explicitly compute the antiderivative of some elementary functions.
LG5. Use the fundamental theorem of calculus to differentiate and compute integrals.
LG6. Use integrals to compute areas, lengths, masses, probabilities, etc.
LG7. Apply some simple numerical methods to compute approximate values of integrals.
LG8. Integrate some notable ODEs.
1. Sequences
1.1. Some concepts
1.2. Convergence
1.3. Some limits and useful results
2. Differential calculus in R
2.1. A brief review
2.2. Continuity and limits
2.3. Differentiability and Taylor?s formula
2.4. Numerical methods
2.4.1. Fixed point method
2.4.2. Bisection method
2.4.3. NewtonRaphson method
2.4.4. Numerical differentiation
3. Integral calculus in R
3.1. Antiderivatives
3.2. Integrals
3.3. Fundamental theorem of calculus
3.4. Numerical integration
3.4.1. Midpoint method
3.4.2. Trapezoidal rule
4. Ordinary differential equations.
4.1. Separable variables
4.2. First order linear equations
4.3. Numerical methods
4.3.1. Euler?s method
4.3.2. RungeKutta method (RK4)
A student must obtain an overall grade of at least 10 (out of 20) in one of the assessment modes:
 Periodic assessment: Exam (75%) + teamwork on numerical computation (25%).
 Exam assessment: in any of the exam seasons (100%).
Students with a grade over 16 should be submitted to an oral examination.
Title: [3] Caputo, H.P., Iniciação ao Estudo das Equações Diferenciais, Livros Técnicos e Científicos Editora, S.A.
[2] Strang, G., "Calculus", WellesleyCambridge Press.
[1] Ferreira, J.C., "Introdução à Análise Matemática", Fundação Calouste Gulbenkian.
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Title: [6] Santos, M.I.R, ?Matemática computacional? (IST).
[5] Suleman, A., ?Notas elementares sobre o cálculo numérico? (disponível no elearnig).
[4] Suleman, A., ?Apontamentos de aula? (disponível no elearnig).
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Sampling and Information Sources
LG1: Identify types and sources of information appropriate to the research objectives.
LG2: Critically assess the quality of the information obtained.
LG3: Know and practice the main sampling methods.
1. Types and sources of information
2. Internet as an information source
3. Sampling
4. Designed Data vs Big Data
Evaluation methodology: periodic or exam
Periodic:
 Coursework (40%);
 Exam (60%); min. grade 7.5
Attendance >=80% of the lectures
Exam: the same as Periodic except the attendance requirement.
The coursework if is not delivered is marked with 0 and considered for the final grade with a weight of 40%.
An oral test may be requested to any student following the completion of any of the evaluation elements.
Students that fall under the RIEEE must contact the coordinator of the Learning unit, towards the insertion in the learning processes and evaluation in the Learning unit
Title: Jarrett, C. (2021). Surveys That Work: A Practical Guide for Designing and Running Better Surveys. Rosenfeld Media.
Salganik, M. (2018). Bit by Bit Social Research in the Digital Age. New Jersey: Princeton University Press.
Stebbins, L. (2005). Student Guide to Research in the Digital Age: How to Locate and Evaluate Information Sources. Libraries Unlimited.
Vicente, P., (2024) Apontamentos de apoio à UC de Amostragem e Fontes de Informação.
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Title: Vicente, P. (2012). Estudos de mercado e de opinião, Edições Sílabo.
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Exploratory Data Analysis
Learning goals (LG) to be developed in articulation with the general objectives:
LG1. Prepare data for analysis.
LG2. Use and interpret a set of statistical tools in the field of descriptive.
LG3. Use Excel, R and Jamovi in data preparation, analysis and representation applications.
LG4. Adapt the visual representation models to different objectives, according to good visualization practices.
LG5. Interpreting and writing the results of a descriptive data analysis.
Syllabus contents (SC) articulated with the learning objectives.
SC1. Organization, preparation and transformation of data
SC2. Exploratory data analysis
Missing values
Coding and imputation
Exploratory charts
Random variables
Empirical distribution function
Normal Distribution
SC3. Descriptive data analysis
Descriptive measures
Single and bivariate analysis
Association measures
SC4. Visual representation
Introduction to the principles of visual representation
Visual representation structures
Evaluation: periodic or exam
Periodic:
 Individual exercise with R (10%) min. grade 7.5
 Group work (35%); minimum grade 7.5
 Written test (55%); min. grade 7.5
Exam:
 Practical exam (40%); minimum grade 7.5
 Written exam (60%); minimum grade 7.5
Title: Rocha, M. & Ferreira, P.G. (2017) Análise e Exploração de Dados com R. Lisboa, FCA
Reis, E. (1998). Estatística Descritiva, Lisboa, Sílabo,7ª ed.
Carvalho, A. (2017). Métodos quantitativos com Excel, Lisboa, Lidel edições técnicas.
Cairo, A. (2013). The Functional Art: An introduction to information graphics and visualization (Voices That Matter). New Riders.
Brown, D.S. (2022). Statistics and Data Visualization Using R. The Art and Practice of Data Analysis. Sage Publication, Inc.
Barroso, M., Sampaio, E. & Ramos, M. (2003). Exercícios de Estatística Descritiva para as Ciências Sociais, Lisboa, Sílabo.
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Title: Reis, E. (1998). Estatística Descritiva, Lisboa, Sílabo.
Murteira, B. J. F. (1990). Análise Exploratória de Dados. Estatística Descritiva, McGraw Hill.
Hoaglin, D.C., Mosteller, F & Tukey, J. W. (1992). Análise Exploratória de Dados. Técnicas Robustas, Ed. Salamandra, Lisboa.
Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. London, SAGE Publications Ltd.
Dias Curto, J.J., & Gameiro, F. (2016). Excel para Economia e Gestão. Lisboa, Ed. Sílabo.
Carvalho, A. (2017). Gráficos com Excel  95 Exercícios, Lisboa, FCA.
Alexandrino da Silva, A. (2006). Gráficos e mapas?representação de informação estatística. Lisboa, Lidel edições técnicas.
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Data Structures and Algorithms
At the end of this course, students should be able:
LO1: identify and know how to implement the more adequate data structure for a certain problem resolution:
LO2: know how to evaluate the order of performance and efficiency of a given algorithm and/or data structure for the common operations of inserting, removing, and accessing;
LO3: understand the importance of designing efficient algorithms;
LO4: understand and know how to implement and use dynamic and static data structures;
LO5: know how to implement pointer reference programming;
LO6: understand the pros and cons of recursive algorithms.
CP1: Fundamental concepts (algorithm and program).
CP2: Abstract Data Types and ObjectOriented Programming.
CP3: Linear data structures: stacks, queues, and linked lists.
CP4: Recursion.
CP5: Introduction to algorithm analysis.
CP6: Hierarchical data structures: trees.
CP7: Graphs.
Approval in this course (UC) is only possible through periodic evaluation or (for the students allowed) through the special sitting period. There is not, for this course, the evaluation modality of exam.
Evaluation elements and their respective ponderation:
 test 1, written individual > 30%, minimum mark of 8 values, forecast to happen in the intercalar evaluation period;
 test 2, written individual > 30%, minimum mark of 8 values, forecast to happen in the first period of exam sitting;
 task 1, individual, with oral examination > 15%, minimum mark of 8 values;
 task 2, individual, with oral examination (eventally in groups of 2 students) > 25%, minimum mark of 8 values.
Thus Final_mark = 30% x Test1_mark + 30% x Test2_mark + 15% x Task1_mark + 25% x Task2_mark.
In the special sitting period (Época Especial) the evaluation elements and their respective ponderation are:
 test, written individual > 60%, minimum mark of 8 values, and
 two tasks, individual, with oral examination, minimum mark of 8 values each > 15% + 25%.
Thus Final_mark_special_sitting = 60% x Test_mark + 15% x Task1_mark + 25% x Task2_mark.
To obtain approval in the course (UC) it is required that the Final_mark or the Final_mark_special_sitting is of 10 values out of 20 values.
Title:  J. Wengrow, A CommonSense Guide to Data Structures and Algorithms, Second Edition. The Pragmatic Bookshelf, 2020.
 M. Goodrich, R. Tamassia, and M. Goldwasser, Data Structures & Algorithms in Python. Wiley, 2013.
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Title:  B. Miller and D. Ranum, Problem Solving with Algorithms and Data Structures using Python, Second Edition, Release 3.0. 2013.
 T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, Fourth Edition. MIT Press, 2022.
 Referências adicionais a indicar durante as aulas.
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Optimization for Data Science
At the end of this Curricular Unit, the student is expected to be able to:
LG1. Develop formulations in linear programming, integer linear programming, and nonlinear programming for efficiently solve complex problems in real contexts.
LG2. Use general software to determine solutions for problems formulated in linear programming, integer linear programming, and nonlinear programming.
LG3. Do the economic interpretation and produce recommendations based on the solutions obtained for problems formulated in linear programming, integer linear programming, and nonlinear programming.
Syllabus contents (SC):
SC1: Linear Programming
1.1 General form of a Linear Programming model
1.2 Formulating problems in Linear Programming
1.3 Graphical resolution
1.4 Resolution using general software (Excel Solver)
1.5 Interpreting results and sensitivity analysis
SC2: Integer Linear Programming
2.1 Formulating problems in Integer Linear Programming
2.2 Formulating problems with binary variables
2.3 Resolution using general software (Excel Solver)
2.4 Interpreting results
2.5 BranchandBound algorithm
SC3: NonLinear Programming
3.1 Formulating problems in Nonlinear Programming
3.2 Resolution using general software (Excel Solver)
3.3 Interpreting results
1. Periodic Evaluation:
a) Written test (60%);
b) Group coursework with discussion (40%);
c) Attendance of at least 2/3 of the classes.
2. Evaluation by Exam (1st and 2nd Season):
a) Written test (60%);
b) Individual project with discussion (40%);
Approval (in the Periodic or Exam evaluation):
i) Requires a minimum mark of 8.5 in each examination;
ii) An oral discussion may be required.
Scale: 020 points.
Title: * Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. 8th Ed. Cengage Learning.
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Title: * Evans, J. (2021). Business Analytics. 3rd Ed. Global Edition. Pearson.
* Hillier, F.S and Lieberman, G.J. (2015). Introduction to Operations Research, 10th Ed., McGrawHill.
* Ragsdale, C.T. (2001). Spreadsheet Modeling & Decision Analysis: A Practical Introduction to management science. 3rd Ed., SouthWestern College Publishing.
* Wolsey, L.A. (1998). Integer Programming. Wiley.
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Calculus Topics II
LG1. Compute partial derivatives and gradients.
LG2. Determine linear approximations of functions of several variables.
LG3. Determine and classify critical points of functions of several variables.
LG4. Apply the previous concepts in the context of regression problems.
LG5. Compute double integrals.
LG6. Apply integral calculus to the evaluation of volume, mass and probability.
LG7. Interpret geometrically all the previous concepts.
LG8. Implement in MATLAB some of the computacional methods studied in class.
1) Differential calculus
1.1. Limits and continuity
1.2. Partial derivatives.
1.3. Tangent plane and differentiability.
1.4. The chain rule
1.5. Computation and classification of critical points.
1.6. Gradient descent.
1.7. Linear regression.
2 ) Integral calculus.
2.1. Double integral.
2.2. Double integrals in polar coordinates.
2.3. Application of integral calculus to the evaluation of volume, mass and probability.
Students must obtain an overall grade of at least 10 (out of 20) in one of the assessment modes:
Periodic assessment: Written Test (80%) + MATLAB miniprojects (20%).
A final Exam (100%) in either the 1st or 2nd examination period.
Title: Stewart, J. "Cálculo  Volume 2", Tradução da 8ª edição norteamericana (4ª edição brasileira), Cenage Learning, 2017.
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Writing Scientific and Technical Texts
Learning goals (LG)1: To know the concept of scientific research;
LG2: To learn to summarize a scientific article and to identify the main topics;
LG3: To learn how to organize a research paper or a technical report;
LG4: To be familiarized with the rules of scientific writing.
1. Information, Initial draft, Reviewing, Final draft;
2. Structure of a technical and scientific text;
2.1 The pretextual elements;
2.2 The textual elements;
2.3 The posttextual elements;
3. The use of illustrative elements of technical and scientific argument or empirical demonstration;
3.1 The inclusion of charts, tables and other elements;
4. The standards of referencing bibliographic citation and annotations;
4.1 The various national and international standards. The standards adopted for carrying out works, dissertations and theses in ISCTEIUL;
4.2 The use of specific software to organize and manage bibliographies and production of technical and scientific manuscripts (Biblioscape, BiblioExpress and EndNote).
1. Expositional: casestudies' demonstration.
2. Participative: analysis and resolution of application exercises and case studies.
3. Active: realization of individual and group works;
4. Selfstudy: related with autonomous work by the student, as is contemplated in the Class Planning.
1) Ongoing evaluation:
a) Approval on courses provided by the blendedlearning program  Mandatory  The course evaluation assumes that the student achieves 50% or more in the answers to the quizzes he has to do in each module.
b Autonomous exercises (includes participation and feedback of exercises  30%)
c) Final evaluation work  70%
2)Exam evaluation:
Writing a final evaluation work  100%
Title: Soares, M. A. (2001). Como Fazer um Resumo. Queluz de Baixo, Barcarena: Editorial Presença
Pereira, M. G. (2012). Artigos Científicos. Como Redigir, Publicar e Avaliar. Brasil: Guanabara Koogan
Nascimento, Z. & Pinto, J.M. (2001). A Dinâmica da Escrita: Como escrever com êxito. Lisboa: Plátano Editora
Madeira, A. C. & Abreu, M. M. (2004). Comunicar em Ciência? Como redigir e apresentar trabalhos científicos. Lisboa: Escolar Editora
Lindemann, K. (2018). Composing Research, Communicating Results: Writing the Communication Research Paper. USA: John Wiley & Sons, Inc
Gastel, B. & Day, R. A. (2016). How to Write and Publish a Scientific Paper (8th Edition). Santa Barbara, California: Greenwood
Cargill, M. & O'Connor, P. (2013). Writing Scientific Research Articles (2nd Edition). UK: WileyBlackwell
Brandão, M. L. (2009). Manual para Publicação Científica: Elaborando manuscritos, teses e dissertações. Rio de Janeiro: Elsevier
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Title: Wolton, D. (2006). É preciso salvar a comunicação. Casal de Cambra: Caleidoscópio
Pereira, A. & Poupa, C. (2008). Como Escrever uma Tese, Monografia ou Livro Científico usando o Word. Lisboa: Edições Sílabo
Munter, M. (2006). Guide to managerial communication: effective business writing and speaking (7th Edition). New Jersey: Prentice Hall
Lipson, C. (2011). Cite Right: A Quick Guide to Citation Styles  MLA, APA, Chicago, the Sciences, Professions and More (2nd Edition). Chicago: University of Chicago Press
Júnior, J. M. (2008). Como Escrever Trabalhos de Conclusão de Curso? Instruções para planejar e montar, desenvolver, concluir, redigir e apresentar trabalhos monográficos e artigos. Petrópolis: Editora Vozes
Hofmann, A. (2016). Scientific Writing and Communication. Papers, Proposals, and Presentations (3rd Edition). Oxford: University Press
Goins, J. (2012). You Are a Writer (so start ACTIHering, L. & Hering, H. (2010). How to Write Technical Reports: Understandable Structure, Good Design, Convincing Presentation. London, New York: SpringerNG like one). United States of America: Tribe Press
Forsyth, P. (2016). How to Write Reports and Proposals. United Kingdom: Kogan Page, Ltd
Estrela, E., Soares, M. A. & Leitão, M. J. (2003). Saber escrever saber falar: um guia completo para usar correctamente a língua portuguesa. Lisboa: Publicações Dom Quixote
Bowden, J. (2011). Writing a Report  How to Prepare, Write and Present Really Effective Reports. United Kingdom: Little, Brown Book Group
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Critical Thinking
The student who successfully complete this Curriculum Unit will be able to:
LG*1 Analyse arguments regarding their structure and content;
LG2  Argue on a issue;
LG3  identify the deductive validity on propositions;
LG4  Question arguments, identifying its weaknesses.
* LG: Leaning goal
PC*1  The importance of critical thinking
PC2  Argumentative discourse
PC3  Types of arguments and arguments structure
PC4  Arguments quality and errors in argumentation.
* PC: Program contents
Lectures, inclass exercises, inclass discussions, readings, case discussions (in smaller groups).
Active participation in the proposed works during class is expected
Continuous evaluation
Homework  15% (1HW  5% + 1 HW  10%)
Attendance / participation  Classe exercices + group debate  25%
Work Final (60%)
 Exams
Written Work 100%
 To successfully complete the continuous assessment, students must not score less than 7 in any of the assessment components listed;
 In the case of UCs in which the Final Assessment includes an assignment: the awarding of the final assessment may involve a discussion of the final assignment submitted within the previously defined assessment deadlines.
Title: Bowell, T., & Kemp, G. (2002). Critical thinking: a concise guide. London: Routledge.
Weston, A. (2005). A arte de argumentar. Lisboa: Gradiva
Cottrell, S. (2005). Critical Thinking Skills: Developing effective analysis and argument. New York: Palgrave McMillan.
Authors:
Reference:
Year:
Title: Brookfield, S. (1987). Developing critical thinkers: challenging adults to explore alternative ways of thinking and acting. San Francisco: JosseyBass.
Morgado, P. (2003). Cem argumentos: A lógica, a retórica e o direito ao serviço da argumentação. Porto: Vida Económica.
Paul, R., & Elder, L. (2001). The miniature guide to critical thinking: concepts and tools. Foundation for Critical Thinking.
ThayerBacon, B.J. (2000). Transforming critical thinking: thinking constructively. New York: Teachers College Press.
Authors:
Reference:
Year:
Big Data Storage
1. Implement distributed and faulttolerant data storage solutions;
2. Manipulation and extraction of large amounts of information from unstructured databases;
3. To develop soft skills, namely
and Collaboration and Team Work and Critical Observation.
1. Introduction to Non Relational Databases;
2. Redundancy as a tool to manage fault tolerance;
3. Distribution of Data to manage large volumes of information;
4. Introduction to MongoDB;
5. Collection Design in MongoDB;
6. Json data structures;
7. Extraction of data in MongoDB;
Periodic assessment through a written test (minimum grade 7.5) and an assignment (optional) that is delivered contributes 30% of the grade. The test coincides with the 1st season exam. There is a 2nd season exam for students who fail or want to improve their grade. Work can only count towards the first season. The Special Season consists exclusively of a written exam.
BibliographyTitle: NoSQL Database: New Era of Databases for Big data Analytics  Classification, Characteristics and Comparison, A B M Moniruzzaman, Syed Akhter Hossain, 2013 (https://arxiv.org/abs/1307.0191)
MongoDb Homepage
Authors:
Reference:
Year:
Computational Statistics
Learning goals (LG) to be developed :
LG1 Do basic simulations of probabilistic scenarios
LG2 Compute conditional probabilities directly and using Bayes' theorem, and check for independence of events
LG3 Set up and work with discrete and continuous random variables, draw observations with such distributions
LG4 Understand the central limit theorem
LG5 Understand the difference between probability and likelihood functions, and find the maximum likelihood estimate for a model parameter
LG6Find confidence intervals for parameter estimates
LG7 Use null hypothesis significance testing to test the significance of results Compute and interpret the pvalue for these tests.
Syllabus contents (SC):
SC1 Probability theory: definitions, axioms, conditional probability, total probability theorem and Bayes' formula
SC2 Univariate random variables: mass and density functions, distribution function, and parameters
SC3 Working with usual random variables. Simulation of RV with a specified distribution.
SC4Bi and multivariate RVs. Joint probability and distribution functions. correlation and covariation. Independence between RVs. Sample joint distribution.
SC5 Sampling distributions: limit central theorem, theoretical sampling distributions.
SC6 Parameters estimation: point estimation, estimators' properties, maximum likelihood estimators, interval estimation
SC7 Hypothesis testing: types of errors and corresponding probabilities. Test for one and two means. Chisquare of independence. Meaning and computation of pvalues.
Students may choose either Periodical Evaluation or Final Exam.
Periodical evaluation
1. Homework assignments: 10 small exercises (one per week, approximately, to be delivered in 48h as a rule 48h). The 8 best grades will be considered toward the final grade and will account for 15% of such final grade.
Any assignment not delivered is graded with 0. Homework assignments will be graded on a 0100 scale. The final grade for this evaluation instrument is obtained multiplying the simple average of the best 8, by 20.
2. One midterm individual written test, 30% of the final grade, no minimum grade
3. One final individual written test, 30% of the final grade, minimum grade 9 out of 20
4. One final individual computer test, in R, 25% of the final grade, minimum 7 out of 20
OR
Final Exam: computerlab test (40%): written test (60%). Minimum grades: i) written test, 9 out of 20; ii) computerlab test, 7 out of 20. Minimum weighted grade of 10 out of 20.
In any case, the final weighted grade, rounded to the units, must be at least 10 ou of 20 in order to succeed
Title: Kerns, G.J., IPSUR: Introduction to Probability and Statistics Using R,, 2011, ISBN: 9780557249794, https://www.semanticscholar.org/paper/IntroductiontoProbabilityandStatisticsUsingRKerns/b2a2c69237387b4c18871d3137667461ff8ea33f
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada, volume 1. 6ª edição. Lisboa. Edições Sílabo., 2015, ISBN 9789726188193,
Reis, E., Andrade, M., Calapez, T. & Melo, P., Estatística Aplicada volume 2, 6ª edição, Lisboa. Edições Sílabo., 2016, ISBN 9789726189862,
Verzani, J., Using R for Introductory Statistics, 2nd Edition, Chapman & Hall/CRC, 2014, eBook ISBN 9781315373089, https://cran.rproject.org/doc/contrib/VerzaniSimpleR.pdf
Authors:
Reference:
Year:
Title: Rohatgi, V.K. and Ehsanes Saleh, A.K. Md, An Introduction to Probability and Statistics, 3rd edition, Wiley Series in Probability and Statistics, 2015, ISBN: 9781118799642,
Reis, E., Andrade, M., Calapez, T. & Melo, P., Exercícios de Estatística Aplicada volume 1. 2ª edição, Lisboa. Edições Sílabo., 2012, ISBN 9789726186885,
Reis, E., Andrade, M., Calapez, T. & Melo, P., Exercícios de Estatística Aplicada volume 2. 2ª edição, Lisboa. Edições Sílabo., 2014, ISBN 9789726187479,
Authors:
Reference:
Year:
Fundamentals of Database Management
O1: Develop abstraction mechanisms;
O2: Develop Information Modeling abilities;
O3: Develop the ability to extract data from a database in an efficient way.
P1  Database Design
P2 Relations and primary keys
P1.2.2 Foreign Keys and Integrity Rules
P1.2.3 Optimizationsand Indexes
P1.2.5 Transctions and Concurrency
P2 S.Q.L
P2. 1 Simpl Querys;
P2.2 Agregate Functions;
P2.3 SubQuerys;
P2.4 Triggers and Stored Procedures;
Periodic assessment through a written test (minimum grade 7.5) and two assignments (optional) that each contribute 15% of the grade. The test coincides with the 1st season exam. There is a 2nd season exam for students who fail or want to improve their grade. Work can only count towards the first season. The Special Season consists exclusively of a written exam.
BibliographyTitle: Ramos, P, Desenhar Bases de Dados com UML, Conceitos e Exercícios Resolvidos, Editora Sílabo, 2ª Edição, 2007
Perreira, J. Tecnologia de Base de Dados" FCA Editora de Informática, 1998
Damas, L. SQL  Structured Query Language " FCA Editora de Informática, 2005 (II)
http://plsqltutorial.com/.
Authors:
Reference:
Year:
Title: Date, C.J. "An introduction to Database Systems" AddisonWesley Publishing Company, sexta edição, 1995 (I.2, I.3, I.4, II);
Booch, G., Rumbaugh, J., Jacobson, I "The Unified Modeling Language User Guide" AddisonWesley Publishing Company, 1999 (I.1);
Nunes, O´Neill, Fundamentos de UML, FCA, 2002
Authors:
Reference:
Year:
Unsupervised Learning Methods
LG1: Understanding the main unsupervised data methods
LG2: Use R for unsupervised data analytics
LG2: Evaluate, validate and interpret the results
PC1: Introduction to unsupervised learning methods
PC2: Data reduction techniques (dimensionality)
 Principal components analysis (PCA)
 Data reduction techniques using R
PC3: Clustering techniques
 Hierarchical methods
 Partitioning methods
 Selforganizing maps
 Probabilistic methods
 Quality & Validity of clustering methods
 Clustering techniques using R
PC4: Case studies
Students may choose either Periodical Evaluation or Final Exam.
PERIODICAL EVALUATION:
 group work with minimum grade 8 (50%)
 individual test with minimum grade 8 (50%)
Approval requires a minimum attendance of 80% of classes and minimum grade of 10.
EXAM:
The Final Exam is a written exam. Students have to achieve a minimum grade of 10 to pass.
Title: James, G., Witten, D., Hastie, T., Tibshirani, R. (2013), An Introduction to Statistical Learning: with applications in R, New York: Springer.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2014), Multivariate Data Analysis, 7th Edition, Essex, UK: Pearson Education.
Authors:
Reference:
Year:
Title: Wedel, M., Kamakura, W. A. (2000), Market Segmentation. Conceptual and Methodological Foundations (2nd edition), International Series in Quantitative Marketing. Boston: Kluwer Academic Publishers.
Lattin, J., D. Carroll e P. Green (2003), Analyzing Multivariate Data, Pacific Grove, CA: Thomson Learning.
Kohonen, T. (2001). SelfOrganizing Maps. Third edition, Springer.
Hennig, C., Meila, M., Murtagh, F., Rocci, R. (eds.) (2016), Handbook of Cluster Analysis, Handbooks of Modern Statistical Methods. Boca Raton: Chapman & Hall/CRC.
Aggarwal, C. C., Reddy, C. K. (eds.) (2014), Data Clustering: Algorithms and Applications. Boca Raton: CRC Press.
Authors:
Reference:
Year:
Security, Ethics and Privacy
LG1. Recognize the main security issues in softwarebased systems, their causes, and consequences.
LG2. Identify and describe the security services necessary to implement a specific information protection policy based on risk analysis.
LG3. Learn the principles and regulatory frameworks in the domains of personal data protection and privacy, with special focus on the General Data Protection Regulation of 2016.
LG4. Ethically and critically reflect on the implications of technologies and data processing on individuals and society, addressing the resulting challenges in the fields of information security, data protection, and privacy.
CP1. Information Security: Fundamentals of security  data security; Vulnerabilities and threats in security; IRM  Information Risk Management; Cryptography and PKI.
CP2. Privacy and data protection: the General Data Protection Regulation (GDPR) and the Law 58/2019; Anonymisation and pseudonymization techniques.
CP3. Ethics: Ethics and technological development; Computer ethics; Challenges in data science; Responsibility in engineering; Normative ethics and case study.
The assessment is continuous and includes:
 1st test (40%) [CP1]
 2nd test (25%) [CP2]
 Group assignment (32.5%) [CP3]
 Student attendance in classes (2.5%) [CP2, CP3]. To obtain 100% in the attendance component, the student must attend a minimum of 70% of the classes.
Each of the tests and the individual assignment has a minimum grade of 7/20 points.
Final exam in the 1st and 2nd sessions for those who are not approved through continuous assessment:
 Written exam 100% [CP1, CP2, CP3]
Title: Andress, J. (2014). The Basics of Information Security: Understanding the Fundamentals of InfoSec in Theory and Practice. Syngress.
Kim, D., Solomon, M. (2016). Fundamentals of Information Systems Security. Jones & Bartlett Learning.
Cannon, J.C. Privacy in Technology: Standards and Practices for Engineers and Security and IT Professionals. Portsmouth: AN IAPP Publication, 2014.
Breaux, Travis. Introduction to IT Privacy: A Handbook for Technologists. Portsmouth: An IAPP Publication, 2014.
Whitman, M., & Mattord, H. (2013). Management of information security. Nelson Education.
Katz, J., & Lindell, Y. (2014). Introduction to modern cryptography. CRC press.
Ethics, Technology, and Engineering: An Introduction (2011). Ibo van de Poel, Lamber Royakkers, WileyBlackwell.
European Union Agency for Fundamental Rights, The Handbook on European data protection law, 2018:, 2019, http://fra.europa.eu/sites/default/files/fra_uploads/fracoeedps2018handbookdataprotection_en.pdf, http://fra.europa.eu/sites/default/files/fra_uploads/fracoeedps2018handbookdataprotection_en.pdf
Authors:
Reference:
Year:
Title: A. Barreto Menezes Cordeiro, Direito da Proteção de Dados à luz do RGPD e da Lei n.º 58/2019, Edições Almedina., 2020, Cordeiro (2020)
Sara Baase, A gift of fire : social, legal, and ethical issues for computing technology, 2013, 
Whitman, M., Mattord, H. (2017). Principles of Information Security. Course Technology.
Bowman, Courtney. The Architecture of Privacy: On Engineering Technologies that Can Deliver Trustworthy Safeguards. O?Reilly Media, 2015.
Anderson, R. J. (2010). Security engineering: a guide to building dependable distributed systems. John Wiley & Sons.
Zúquete, A. (2018). Segurança em redes informáticas. FCAEditora de Informática.
Regulamentos e orientações da Comissão Europeia relativos à Proteção de Dados, https://ec.europa.eu/info/law/lawtopic/dataprotection_en
Bynum, Terrell Ward, and Simon Rogerson, (2004), Computer Ethics and Professional Responsibility: Introductory Text and Readings. Oxford: Blackwell, 2004.
Grupo do Artigo 29, Parecer 05/2014 sobre técnicas de anonimização do grupo de trabalho de proteção de dados do artigo 29.º, de 10 de Abril de 2014, 2014, , https://ec.europa.eu/justice/article29/documentation/opinionrecommendation/files/2014/wp216_pt.pdf
Enisa, Orientações da Enisa sobre técnicas de pseudonimização e boas práticas, 2019, , https://www.enisa.europa.eu/publications/pseudonymisationtechniquesandbestpractices
UE, Proposta do regulamento do parlamento europeu e do conselho que estabelece regras harmonizadas em matéria de inteligência artificial (regulamento inteligência artificial) e altera determinados atos legislativos da União, 2023, , https://eurlex.europa.eu/legalcontent/PT/TXT/?uri=CELEX%3A52021PC0206
Outros textos a indicar e distribuídos pelo docente ao longo do semestre.
Authors:
Reference:
Year:
Introduction to Dynamic Models
LG1. Understood correlation between variables, simple and multiple linear regression models
LG2. Estimation methods (OLS and ML)
LG3. Residuals assumptions analysis, diagnostic and hypothesis tests
LG4. Lag operator, stationarity, unit root tests, outliers and dummy variables, ARIMA models.
LG5. Extensions of the classical linear regression: nonlinear and dynamic models.
LG6. Basic programming and computation with R and Python
LG7. Application of the studied concepts: train/test sets and prediction, information and value extraction from realworld data.
P1. Regression models
P1.1. Correlation
P1.2. Simple linear regression
P1.3. Multiple linear regression
P2. Estimation and inference, OLS and ML
P3. Residual assumptions
P3.1. Diagnostic and Hypothesis tests
P3.2. Practical cases
P4. ARMA/ARIMA/SARIMAX models
P4. Lag operator, stationarity, unit root test, outliers, dummy variables
P4.2. White noise, ARMA, ARIMA and SARIMAX models
P4.3. BoxJenkins methodology, forecasting
P5. Extensions of the classical regression model
P5.1. Nonlinear regression
P5.2. Practical cases
P6. Basic programming and computation with R and Python
P7. Applications for real data
P7.1. Train/test split/set, prediction and forecasting
P7.2. Practical cases
The periodic evaluation includes the realization of:
a) An individual test (60%).
b) A team work (40%).
The periodic evaluation requires that students attend at least 80% of classes.
In this type of evaluation, the students have to achieve a minimum grade of 8,5 in the individual test and of 10 in the team work. Otherwise the students should do a final exam (minimum approval score: 10).
Title:  Ficheiros (slides e scripts) da UC a disponibilizar no elearning/Fenix
 Rob J Hyndman and George Athanasopoulos, (2018), Forecasting: principles and practice, 2nd Edition, OTexts Melbourne ("fpp2" package CRAN)
 Tom Alby, (2024), Data Science in Practice, CRC Press.
 Bruce P., Bruce A., and Gedeck P., (2020), Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python, 2nd Edition, O' Reilly Media, Inc.
 Robert I. Kabacoff, (2022), R in Action: Data analysis and graphics with R, Third Edition, Manning Publications Co.
Authors:
Reference:
Year:
Title: Eric Goh Ming Hui, (2019), Learn R for Applied Statistics, Apress.
Daniel J. Denis, (2020), Univariate, Bivariate, and Multivariate Statistics Using R: Quantitative Tools for Data Analysis and Data Science, JohnWiley & Sons, Inc.
Authors:
Reference:
Year:
Supervised Learning Methods
LG1: Understanding supervised learning methods: scopes of application and procedures
LG2: Use of R software to perform data analysis
LG2: Evaluate and interpret the data analysis results
PC1: Overview of Supervised Learning
Typologies
Learning data
Objective functions
Models' assessment and selection
Notes on statistical inference
PC2: Regression Methods
KNearest Neighbor
Regression Trees (using CART algorithm)
PC3: Classification Methods
Naive Bayes
KNearest Neighbor
Logistic Regression
Classification Trees (using CART algorithm)
The teachinglearning methodology (TM) includes four components:?
TM1: Expositional, to present the theoretical reference frames
TM2: Experimental, using software to perform data analysis
TM3: Active, with the realization of team work
TM4: Selfstudy, related with autonomous work by the student, as is contemplated in the Class Planning

PERIODICAL EVALUATION:
 group quiz online (40%) with a minimum grade of 9
 individual test (60%) with a minimum grade of 9
Approval requires a minimum grade of 10.
EXAM:
1st part  individual test (60%)
2nd part individual practical data analysis test, online, with the R software used in classes (40%).
Students have to achieve a minimum grade of 9 in each part of the exam and a combined minimum grade of 10.
Title: Gareth, J., Daniela, W., Trevor, H., & Robert, T. (2013). An introduction to statistical learning: with applications in R. Springer.
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1758). New York: Springer.
Lantz, B. (2023). Machine Learning with R: Learn techniques for building and improving machine learning models, from data preparation to model tuning, evaluation, and working with big data. 4th Edition. Packt Publishing.
Larose, D., Larose, C. (2015). Data Mining and Predictive Analytics. John Wiley & Sons.
Authors:
Reference:
Year:
Title: Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R (2012). Great Britain: Sage Publications, Ltd, 958.
Authors:
Reference:
Year:
Heuristic Optimization
Learning goals (LG):
LG1 Discuss challenges faced in real, large scale optimization problems
LG2  Explain and discuss the available methodologies for addressing hard optimization problems
LG3  Formulate and design effective solution methods for addressing optimization problems
LG4 Employ the use of advanced tools to solve optimization problems
Syllabus contents (SC):
SC1. MULTI ? OBJECTIVE PROGRAMMING
1.1. Basic concepts
1.2. Methodologies
SC2. METAHEURISTICS
2.1. Concepts and terminology
2.2. Single point algorithms
2.3. Genetic Algorithms
1st season:
i) Individual assignments (IA): 50%
 IA1: 25%
 IA2: 25%
ii) Group term project (up to 5 students): 50%;
In the Individual Assignments (IA1 and IA2) and in the Group Term Project, oral discussion may be required.
2nd season:
i) Individual term project: 100% (oral discussion may be required).
In both seasons, an oral exam may be required even if final grade >= 9,5.
Title:  KeLin Du; M. N. S. Swamy (2018). Search and Optimization by Metaheuristics: Techniques and and Algorithms Inspired by Nature. Birkhäuser.
 Gutierrez, A. M; RamirezMendoza, R. A.; Flores, E. M.; PonceCruz, P; Espinoza, A.A. O.; Silva, D. C. B. (Eds.) (2020). A Practical Approach to Metaheuristics using LabVIEW and MATLAB (R). Taylor & Francis Ltd.
 Lobato, F. S.; Valder, S. Jr. (2017). MultiObjective Optimization Problems: Concepts and SelfAdaptive Parameters with Mathematical and Engineering Applications. Springer Cham.
 Ragsdale, C.T. (2017). Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Business Analytics. 8th Ed. Cemgage Learning.
 Burke, E. K.; Kendall, G. (Eds.) (2014). Search Methodologies: Introductory Tutorials in Optimization and Decision Support, 2nd edition, Springer.
 Siarry, P. (Ed.) (2016). Metaheuristics, Springer.
 Ehrgott, M. (2005). Multicriteria Optimization, 2nd edition, Springer.
Authors:
Reference:
Year:
Title:  Open Access documents such as instructor notes, book chapters, research articles, and tutorials that will be provided via Moodle.
Authors:
Reference:
Year:
Big Data Processing
At the end of this course, students should be able:
OA1: to understand and identify problems involving mining massive datasets
OA2: to understand and know how to apply distributed programming / computing models
OA3: to understand and know how to apply dimensionality reduction techniques
OA4: to apply supervised or unsupervised learning techniques to large scale problems
OA5: to understand and know how to apply different recommendation algorithms
OA6: to understand the different techniques to extract information from large graphs
CP1: Large scale programming
CP2: Large scale machine learning
CP3: Recommendation systems
CP4: Link analysis
Assessment can be performed in one of the following modes:
[1] Periodic assessment, comprising:
 one written test, weighting 60% on the final score, with a minimum score of 8 out of 20 to obtain approval in the UC;
 one project (in groups), weighting 40% on the final score.
[2] Final exam consisting of theory and practice parts, to be carried out at IscteIUL (see mandatory details on the Observation's field).
Title:  Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale, Ofer Mendelevitch, Casey Stella and Douglas Eadline, Addisonwesley, 2016.
 Advanced Analytics with Spark: Patterns for Learning from Data at Scale, Sandy Ryza et al., O'Reilly Media, 2017.
 Learning Spark: LightningFast Big Data Analysis, Holden Karau, A. Konwinski, P. Wendell and M. Zaharia, O'Reilly Media, 2015.
 Big Data: Algorithms, Analytics, and Applications, KuanChing Li et al., Chapman and Hall/CRC, 2015.
 Mining of Massive Datasets, A. Rajaraman, J. Ullman, 2011, Cambridge University Press.
Authors:
Reference:
Year:
Title:  The elements of statistical learning, Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer, 2001
 All of Statistics: A concise course in Statistical Inference, L.Wasserman, Springer, 2003.
Authors:
Reference:
Year:
Applied Project in Data Science I
LG1 Learn fundamental methods for data cleaning, preprocessing, engineering and integration
LG2  Identify the appropriate methodology for the problem to be solved.
LG3  Understand and interpret the results
LG4  Communicate the results correctly (report and oral presentation)
CP 1  Methodology for developing a project focusing on Data Science.
CP 2  Modules (Python) for data cleaning, wrangling and visualization
CP 3  Feature engineering and data understanding
CP 4  Methodologies for communicating and disseminating results.
CP 5  Project development.
Given the applied nature of this course, there will be no exam evaluation.
The evaluation will consist of:
1  An interim test  25%;
2  Oral presentations: 25% .;
3  Project with final report and presentation in workshop: 50%.
Approval requires a minimum weighted score of 10 points
Title: (1) Artigos científicos em conformidade com os temas específicos aos problemas em que os alunos vão desenvolver o seu projeto
(2) Ficheiros (slides, scripts e notebooks) da UC a disponibilizar no Moodle
(3) Wes McKinney (2022), Python for Data Analysis, 3rd Edition, O'Reilly Media, Inc. (https://wesmckinney.com/book/, https://github.com/wesm/pydatabook)
(4) Marek Gagolewski (2023), Minimalist Data Wrangling with Python, CC BYNCND 4.0. (https://datawranglingpy.gagolewski.com/)
Authors:
Reference:
Year:
Title: (1) Matt Harrison and Theodore Petrou (2020), Pandas 1.x Cookbook, Second Edition, Packt>.
(2) Suresh Kumar Mukhiya and Usman Ahmed (2020), HandsOn Exploratory Data Analysis with Python, Packt>. (https://github. com/PacktPublishing/handsonexploratorydataanalysiswithpython)
Authors:
Reference:
Year:
Network Analysis
On the completion of this course the student will be able to
LO1. Classify the networks using correlation and clustering coefficients, distances, centrality measures and heterogeneity measures. Evaluate the network robustness;
LO2. Obtain the cooccurrence network associated with a network representing relations. Analyze of weighted networks; LO3. Choose and characterize the random network models;
LO4. Detect communities and evaluate the methods applied to detect communities.
1. Basic Concepts
Elements of a network, subnetworks, density and degree. Bipartite networks.
2. Small Worlds
Degree correlation. Paths and distances. Connectivity. Six Degrees of Separation. Clustering coefficients.
3. Hubs and Weight Heterogeneity
Centrality Measures, Heterogeneity based on Degree, Robustness, Core Decomposition and Weight Heterogeneity.
4. Random Networks
Random Networks generation and characteristics, WattsStrogatz’s model, Configuration Model, Preferential Models.
5. Communities
Basic Definitions. Related Problems. Methods for community detection (Bridge Removal, Modularity Optimization, Label Propagation) and Evaluation Methods.
Two options:
1. Periodic Evaluation:
• team works (40%);
• final test (60%).
Grade (Final Test) >= 8.5;
Attend to at least 2/3 of the classes.
2. Exam:
• individual Project (40%);
• written exam (60%).
Grade (written exam) >= 8.5;
In both options:
• If grade >= 9.5, students may be asked to do an oral exam.
• Positive evaluation if weighted average >= 9.5.
Title: Menczer, F., Fortunato, S. and Davis, C., A First Course in Network Science,, 2020, 1st edition, Cambridge University Press: Cambridge.,
Barabási, A.L., Network Science, 2016, 1st edition, Cambridge University Press,
Authors:
Reference:
Year:
Title: Newman, M., Networks, 2018, 2nd edition. Oxford University Press: Oxford.,
Katherine Ognyanova, Introduction to R and network analysis, 2018, Rutgers University, https://kateto.net/wpcontent/uploads/2018/03/R%20for%20Networks%20Workshop%20%20Ognyanova%20%202018.pdf
Authors:
Reference:
Year:
Symbolic Artificial Intelligence for Data Science
The course introduces the major themes of (mostly) Symbolic Artificial Intelligence and Machine Learning, from an essentially applied perspective, bearing in mind the major context provided by the data science degree, the knowledge and skills acquired in the other courses, and the fundamental objectives and requirements of the data science degree.
The three major topics of the program are logic programming, mostly symbolic adaptive techniques for the representation of adaptive world models, and symbolic machine learning algorithms to learn world models.
After the students have completed the course, they must
? Be fully aware of the existence of mainly symbolic paradigms for the representation and autonomously learning of adaptive world models.
? Have mastered the capability to decide whether to use the paradigms learned in the course to application problems / domains whenever suited.
Overview of the Curricular Unit: the need, advantages and disadvantages of essentially symbolic technologies for representing and learning adaptive models of reality, and the role of each programme component in the desiderata of the chair.
Programming in logic to represent models of reality and to reason with them.
Representation and reasoning based on fuzzy sets and in fuzzy logic to represent essentially symbolic adaptive models and reason with them.
Representation and reasoning based on cases to represent essentially symbolic adaptive models and reason with them.
Introduction to Explainable AI and its characteristics and application domains.
Concepts of Responsible AI.
In periodic assessment, students will have to take:
 Individual written test on the entire CU programme (60%)  occurring during exams' period (1st or 2nd exam).
 (Group) research work on one of the CU topics, with a report and an oral presentation (40%). The oral presentation is done in class time during the semester. The grade of the research work is split 50% for each item and the members of the group may have different grades.
Both assessment components on periodic evaluations have a minimum mark of 8.
Alternatively, students can take only one exam (100%), which can be at both dates of exams.
At special exams' period the students take the exam (100%).
Title: Logic Programming and Inductive Logic Programming:
Ivan Bratko. 2011. Prolog Programming for Artificial Intelligence (4th Edition). Pearson Education Canada (International Computer Science Series).
Fuzzy Systems:
Guanrong Chen, and Trung Tat Pham. 2005. Introduction to Fuzzy Systems. CRC Press.
Case based reasoning:
Michael M. Richter, and Rosina Weber. 2013. CaseBased Reasoning. A Textbook. SpringerVerlag Berlin Heidelberg
Authors:
Reference:
Year:
Title: Lynne Billard, Edwin Diday. 2007. Symbolic Data Analysis: Conceptual Statistics and Data Mining, John Wiley & Sons, Ltd, Chichester, UK
Authors:
Reference:
Year:
Web Interfaces for Data Management
After finishing this unit a student should be able to:
LG1. Know and understand basic concepts and technologies for Web development.
LG2. Know and understand interface technologies between a Web application and a Database.
LG3. Model and develop a Web application allowing to manage persistant data from human interaction with software on the Web.
CP1 [Introduction]
 The history of the Web;
 Previous and actual programming languages for the web;
 W3C standards;
CP2 [Modelling and programming a Web application]
 Clientserver architecture;
 MVC architecture for the Web.
 Main graphical formatting languages for the Web;
 Libraries for graphical formatting;
 Main programming languages for the Web;
 Libraries for programming for the Web;
 Introduction to security on the client and on the server side.
CP3 [Database access]
 Database access from the Web;
 Data model on the Web application and corresponding interaction with the Database.
CP4 [Data Storage and Management]
 Storage of Web data in a Database;
 Data management.
Given the practical nature of the contents, the assessment will encompass a project. Its subject should be aligned with all or part of the syllabus.
Exercises in class (10%).
Project (90%, including teamwork (report and software) 40%, and oral exam 50%).
All components of the project  proposal, report, software, and oral exam, are mandatory. The minimal classification for each component is 10 on a scale of 0 to 20.
There will be a unique deadline for submitting the project, except for students accepted to the special period of assessment, that will be allowed to submit during that period.
Presence in class is not mandatory.
There is no final exam.
Students aiming to improve their classification can submit a new project in the following scholar year.
Title: Mitchell, R. (2016). Web Scraping with Python: Collecting Data from the Modern Web. Ed. O?Reilly Media, Inc. ISBN13: 9781491910290. ISBN10: 1491910291.
Vincent W. S. (2018). Build websites with Python and Django. Ed: Independently published. ISBN10: 1983172669. ISBN13: 9781983172663.
Dean J. (2018). Web Programming with HTML5, CSS, and JavaScript. Ed: Jones & Bartlett Learning. ISBN13: 9781284091793. ISBN10: 1284091791.
Ryan J. (2013). A History of the Internet and the Digital Future. Ed: Reaktion Books. ISBN13: 9781780231129
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Title: Lambert M. and Jobsen B. (2017). Complete Bootstrap: Responsive Web Development with Bootstrap 4. Ed: Impackt Publishing. ISBN10: 1788833406. ISBN13: 9781788833400.
Downey A. B. (2015). Think Python: How to Think Like a Computer Scientist. Ed: O'Reilly Media. ISBN10: 1491939362. ISBN13: 9781491939369.
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Reference:
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