24 July 2020
1st Python Porto Summer Schoolonline course
Python is the most popular language for data science and it is becoming a common job requirement in the financial services industry. This Python Summer School (PSS) aims at providing participants with basic Python skills with a short and intensive course that requires participants to adopt a hands-on approach, focusing on a significant number of exercises and case studies. The main focus will be the use of Python in financial research and covers topics ranging from structuring a program, importing data, data handling, data visualisation, web scrapping and an introduction to unstructured data in a Natural Language Processing approach.
This Summer School targets postgraduate students (MSc and Phd) with an interest in finance, but it can be extended to final year undergraduate students.
Fluency in English, no knowledge of the Portuguese language is required.
The workshop aims to equip PhD and MSc students with the fundamental skills to use Python in a research context. The course is intended for learners who have not yet worked with Python and covers both a general introduction as well as more advanced approaches on data retrieval and processing.
After first reviewing the basics of Python, particularly how to set up the development environment and how to implement simple tasks, we focus on tools commonly utilised by researchers (e.g., NumPy, Pandas and Matplotlib). The workshop is comprised of a combination of guided introductions and more independent in-depth exploration. Thus, students have the opportunity to apply the learned concepts to a relevant case study chosen for its scientific breadth and its coverage of different Python features.
We place particular focus on the use of online resources such as the documentation of the utilised Python packages to equip students with the ability to independently handle even more complex issues that are beyond the scope of this workshop.
Set up Python and manage virtual environments;
Develop replicable data processing workflows and modular “chunks” of code that can be reused in other contexts/projects;
Use online resources to solve issues that are not part of the workshop independently;
Match data sets from different data sources and save the output (packages: NumPy and Pandas);
Web scrapping; and
Conduct text mining tasks (an NLP approach).
In this course assessment is based on an individual piece of coursework and it is optional. Coursework is only mandatory if participants want to obtain the ECTS.
Participants can obtain 3 ECTS if they opt to be assessed and achieve the minimum passing grade.
EUR 300: UCP & LUMS: 270
Partner University: 285
EUR 300: Normal No Accommodation
UCP & LUMS: 270
Partner University: 285