Go to:
Logótipo
Esta página em português Ajuda Autenticar-se
Formação Online da Biblioteca novembro a janeiro
You are here: Start > MECD01

Fundamentals of Data Science and Engineering

Code: MECD01     Acronym: FCED

Keywords
Classification Keyword
CNAEF Informatics Sciences

Instance: 2021/2022 - 1S (of 04-10-2021 to 26-02-2022) Ícone do Moodle

Active? Yes
Responsible unit: Department of Industrial Engineering and Management
Course/CS Responsible: Master in Data Science and Engineering

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MECD 33 Syllabus 1 - 12 84 324

Teaching - Hours

Recitations: 6,00
Type Teacher Classes Hour
Recitations Totals 1 6,00
António Miguel da Fonseca Fernandes Gomes 2,00
André Monteiro de Oliveira Restivo 2,00
António Pedro Rodrigues Aguiar 2,00

Teaching language

English

Objectives

Students should have fundamental concepts in four key areas for MDSE: Statistics, Signal Processing, Databases and Programming.

Provide students with an integrated view of Statistics and of its usefulness, making them capacitated users of Descriptive Statistics and Statistical Inference.

Signal Processing module aims to provide students with concepts, techniques and tools of analysis and design in this field.

About Databases students should be able to describe and analyze the requirements of an IS, represent them using a UML class diagram and transform it into a relational model. Students should also be able to use the SQL language to create, manipulate and query databases.

Concerning Programming, students should acquire fundamental knowledge on procedural and object-oriented programming techniques and be able to develop programs, using the Python language.

Learning outcomes and competences

Acquire an integrated view of Statistics and of its usefulness and be capacitated users of Descriptive Statistics and Statistical Inference. Skills obtained through work sessions where the theoretical concepts are demonstrated through the resolution of exercises and the realization of a mini-project.

Acquire concepts, techniques and tools of analysis and design in the Signal Processing field. Skills obtained through work sessions where the theoretical concepts are demonstrated through the resolution of exercises and the realization of a mini-project..

About Databases should be able to describe and analyze the requirements of an IS, represent them using an entity-relationship model and transform it into a relational model and be able to use the SQL language to create, manipulate and query databases. Skills obtained through work sessions where the theoretical concepts are demonstrated through the resolution of exercises and the realization of a mini-project.

Acquire fundamental knowledge on Programming, procedural and object-oriented techniques, and be able to develop programs, using the Python language. Skills obtained through work sessions where the theoretical concepts are demonstrated through the resolution of exercises and the realization of a mini-project.

Working method

Presencial

Program

Statistics: Descriptive Statistics; Probabilities; Random Variables and Probability Distributions; Main Discrete and Continuous Distributions; Sampling and Sampling Distributions; Estimation and Confidence Intervals; Hypothesis Testing.

Signal Processing: Discrete signals; Fourier transform; Sampling and reconstruction of signals; Z transform; Design of discrete IIR and FIR filters; Discrete equivalents of continuous systems; DFT and FFT; Applications.

Database: Design of a DB using UML  class diagrams and its conversion to the relational model; Creation, manipulation and interrogation of a database using an SQL language; The PostgreSQL DBMS.

Programming: Introduction to Python; Operators and Expressions; Selection and repetition structures. Structured data types; Organization, functions and modules of a program; Classes and Objects; Iterators and Generators; Testing, Debugging and Software Development Practices; Packages.

Mandatory literature

Raghu Ramakrishnan; Database management systems. ISBN: 0-07-116898-2
Alan V. Oppenheim; Discrete-time signal processing. ISBN: 0-13-083443-2
Tintle, N., Chance, B. L., Cobb, G. W., Rossman, A. J., Roy, S., Swanson, T. & VanderStoep, J.; Introduction to Statistical Investigations, Willey, 2016
John V. Guttag; Introduction to computation and programming using Python. ISBN: 978-0-262-52500-8

Complementary Bibliography

Igual, Laura, Seguí, Santi; Introduction to Data Science - A Python Approach to Concepts, Techniques and Applications, Springer, 2017

Teaching methods and learning activities

Sessions are designed to support students in exploring materials, solving exercises, and implementing projects.

Teaching methodologies and learning activities are based around mini-projects in the areas of Statistics, Databases and Signal Processing, all of them evolving Programming.

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation Weight (%)
Participação presencial 0,00
Exame 50,00
Trabalho prático ou de projeto 50,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Elaboração de projeto 120,00
Estudo autónomo 120,00
Frequência das aulas 84,00
Total: 324,00

Eligibility for exams

Admission criteria set according to General Evaluation Rules of FEUP.

Calculation formula of final grade

Grading consist of a final exam with particular focus on the areas of Statistics, Databases and Signal Processing, and a distributed component, based on the elaboration of three mini-projects in the areas of Statistics, Databases and Signal Processing (all evolving Programming).

Final classification calculation formula

0.5 * Average of the three Mini-Projects + 0.5 * Written exam

Observations

Working students and students dispensed from classes must combine with the teachers, present the evolution of their work with periodicity, as well as they should make the necessary presentations, simultaneously with the ordinary students.

Recommend this page Top
Copyright 1996-2021 © Faculdade de Engenharia da Universidade do Porto  I Terms and Conditions  I Accessibility  I Index A-Z  I Guest Book
Page generated on: 2021-12-07 at 00:16:52