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Data-Driven Decision Making

Code: CC4074     Acronym: CC4074     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 2S Ícone do Moodle Ícone  do Teams

Active? Yes
Web Page: http://www.dcc.fc.up.pt/~jpp/dddm
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Data Science

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
E:ECAD 13 PE_Estatística Computacional e Análise de Dados 1 - 6 42 162
M:DS 12 Official Study Plan since 2018_M:DS 1 - 6 42 162
M:ENM 3 Official Study Plan since 2013-2014 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
João Pedro Pedroso Ramos dos Santos

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 1 3,00
João Pedro Pedroso Ramos dos Santos 3,00

Teaching language



Students should:
1. Get acquainted with the main supervised and unsupervised machine learning methods for analytics and decision support.
2. Learn how to formalize optimization models for prescriptive analytics using mathematical programming.
3. Get acquainted with languages and libraries for solving these problems.
4. Be able to critically analyze solutions obtained.

Learning outcomes and competences

What you'll learn:
1. An applied understanding of many different analytics methods, including linear regression, logistic regression, CART, clustering, and data visualization; focus on interpretable methods.
2. An applied understanding of mathematical optimization and how to solve these models with general-purpose solvers.
3. How to implement all of these methods.

Working method



1. Introduction: examples of usage on artificial intelligence and optimization.
2. Data analysis: case studies in Python.
3. Regression problems: background and case studies.
4. Classification problems: background and case studies.
5. Decision trees: definition and use. Random Forests.
6. Text analysis: use in knowledge acquisition.
7. Clustering and unsupervised learning methods.
8. Linear optimization: formulation and computational resolution.
9. Integer optimization: formulation and computational resolution.
10. Nonlinear optimization: formulation and computational resolution.
11. Final Considerations. Impact of the subjects taught on organizations.

Mandatory literature

Dimitris Bertsimas; The analytics edge. ISBN: 978-0-9899108-9-7
Géron, A; Hands-on Machine Learning with Scikit-Learn and TensorFlow, O'Reilly Media, 2019. ISBN: 1492032646
Pedregosa, F. et al.; scikit-learn: Machine Learning in Python, 2019 (https://scikit-learn.org)

Complementary Bibliography

Bertsimas, D. and Freund, R.; Data, Models, and Decisions, Dynamic Ideas LLC, 2004

Teaching methods and learning activities

- Lectures: presentation of the program topics and discussion of examples.
- Labs: problem solving and case studies.
- Assignments: development and presentation of practical projects by students.




Physical sciences > Mathematics > Applied mathematics > Operations research
Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation Weight (%)
Trabalho prático ou de projeto 80,00
Teste 20,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Elaboração de projeto 40,00
Elaboração de relatório/dissertação/tese 40,00
Frequência das aulas 42,00
Estudo autónomo 40,00
Total: 162,00

Eligibility for exams

Attendance to classes is mandatory.
Submission of proposed works.

Calculation formula of final grade

In-class questions (Quizzes): 20%
Practical assignments: 80%

Internship work/project

There will be practical projects to be done in a group.

Special assessment (TE, DA, ...)

The same evaluation criteria is used for all students.

Classification improvement



Jury: João Pedro Pedroso, Inês Dutra.
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