Tecnologias de Informação para a Gestão Industrial

– Theoretical classes/Lectures (1.5 hours/week).
Theoretical classes tend to be expository, but they promote active student participation by asking questions during the discussion of the topics covered and setting challenges for students to submit their solutions via the online academic platform. Students are encouraged to use computers during theoretical classes as well, in order to allow for greater experimentation and monitoring of their learning of all the concepts covered.
– Practical classes (2 hours/week).
In practical classes, the knowledge acquired in theoretical classes is applied through the completion of worksheets. Practical classes will be synchronised so that students can apply the concepts previously learnt in a theoretical class in a laboratory setting.

Students are advised to attend theoretical classes regularly, as this is essential for academic success and a proper understanding of the subject matter. Practical classes are not intended to replace what was taught in theoretical classes, i.e., the introduction of previously presented concepts.

To deepen knowledge of programming and domains relevant to engineering in general and industrial engineering and management in particular.
Provide students with a solid foundation of programming and information technology knowledge in industrial management, strengthening their algorithm and programming skills (e.g. in Python) so that they can make the most of the applications studied (such as Excel/PowerBI) and their automation, particularly in the creation of reports and interactive dashboards for data visualisation and analysis.
Skills to be developed: Advanced use of the main productivity tools in the day-to-day work of an industrial manager; Advanced computational thinking in problem formulation and solving using algorithms, data structures and programming; Critical ability to collect, prepare, transform, analyse and communicate insights to support decision-making in a business context.

Theory: Advanced topics in Python programming – Indexed variables, data structures, lists, tuples, sets, dictionaries, queues, trees. Data concepts, data-oriented culture and ET (Extraction and Transformation) – Location and types of data sources; Data extraction and import; Data preparation and transformation processes. Data visualisation and analysis – Business data analysis cycle; Relevant programming libraries. Introduction to Business Intelligence – Data modelling; Dynamic reports and interactive dashboards.

Practice: Integrating Python with Excel: User Defined Functions (UDF), specific libraries. Hypothesis analysis tools – Goal seeking in calculation formulas; Scenario management; Data tables. Data visualisation and analysis: Static and dynamic tables and graphs; Data filtering and segmentation. Introduction to building dynamic reports and interactive dashboards.

• - Practical tests (2*25%) - 50.0%
• - Exam - 50.0%

NAO