COurse

Introduction to Data Science: Python Applications

This course aims to explain what data science is. Teach what are the main categories of machine learning. In the course, there will be a tutorial on how to approach a dataset, apply an appropriate machine learning technique and extract knowledge related to the initial data.

Input: video camera images

Pre-processing: mask application to focus on techniques in the area of interest

Output: lines/detected areas

course objectives

  • Introduction to data science in Python;

  • How to prepare datasets, for the application of machine learning techniques, using Numpy and Pandas libraries;

  • How to pre-process information by cleaning up unnecessary noise and records, or even incorrect data;

  • How to apply machine learning techniques using practical examples, namely in the following categories:

      • Unsupervised techniques when there are no examples for the categorization;

      • Supervised techniques when a relevant dataset and the respective learning objective is available;

  • Application of neural networks using a practical example.


course recipients

Professionals and academics from several domains of Civil Engineering.

The recipients must have:

  • Curiosity in the application of computing techniques to datasets from various sources of information considered in Civil Engineering, such as sensors, simulation models, statistical surveys, relevant entities, satellites, video cameras, among others;

  • Knowledge from the user perspective or, preferably, experience with some programming language. Not limited to Python;

  • Mathematical and algebraic knowledge to deal with matrices and vectors, which will be necessary to prepared the datasets for the application of data science.


instructor

Gonçalo de Jesus

Ph.D. in Informatics, Specialist in Informatics Engineering (Faculty of Sciences of the University of Lisbon, 2019) and a Bachelor in Informatics Engineering (University of Coimbra, 2006), with 15 years of experience in information systems, monitoring technologies and data science. He is a Research Officer at LNEC, where he joined the Information Technology in Water and Environment group (GTI-DHA, LNEC) in 2007. Since then he has collaborated in several research and innovation projects, both nationally and internationally-funded, related to water information systems and cloud-based forecast management frameworks, like G-cast, pac:man, C-WOS, SI-GeA, PREPARED FP7, INTERREG SPRES, FLAD/NSF RealQual, H2020 projects EOSC-hub, EGI-ACE and more recently INTERREG Inundatio European project. He is author and co-author of +60 publications, including 12 papers in indexed journals.

programme

The courses will be held online on June 23, 2022 from 12:30AM to 4:00PM.

other courses

The information about other courses can be found here.

registration

The conditions of participation and the registration form for the course can be found here.