|Responsible unit:||Department of Industrial Engineering and Management|
|Course/CS Responsible:||Master in Data Science and Engineering|
|Acronym||No. of Students||Study Plan||Curricular Years||Credits UCN||Credits ECTS||Contact hours||Total Time|
|André Monteiro de Oliveira Restivo|
|António Pedro Rodrigues Aguiar|
|António Miguel da Fonseca Fernandes Gomes|
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.
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.
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.
|Trabalho prático ou de projeto||50,00|
|Elaboração de projeto||120,00|
|Frequência das aulas||84,00|
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
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.