Data-Driven Decision Making

Code:

CC4074

Acronym:

CC4074

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2020/2021 - 2S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~jpp/dddm
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master 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
			2
M:ENM	3	Official Study Plan since 2013-2014	1	-	6	42	162
			2

Teaching language

English

Objectives

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

Presencial

Program

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.

Software

Python

keywords

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

n/a

Observations

Jury: João Pedro Pedroso, Inês Dutra.