For Practitioners and Researchers

9-11 December 2015

ISEG - Lisbon, Portugal

Learn Python from scratch


We will start this workshop by a comprehensive overview of the basics and fundamentals of Python, the most taught programming language in CS courses around the world.


All the relevant libraries to produce outstanding scientific results. With the scientific Python toolbox we will cover scientific applications of Python such as solving partial differential equations , optimization or machine learning problems


Practical examples for practical people. You will learn how to integrate Python with the most standard enterprise applications to perform fast and complete analysis and visualizations of your data.


All the material available online . Anywhere. Anytime.


Part I - Basics

Day 1

On day 1 you will familiarize yourself with the basics of python. By the end of the first day you'll be able to set-up python, search, download and install packages, use a version control system, collaborate remotely, load, transform and visualize simple data.

  • Set-up python installation and IDE
  • Introduction to GIT and version control system
  • Python syntax
  • Python core packages for Data Science

Goal: Plot a correlation matrix for a financial index

Part II - Libraries

Day 2 - From crawling to walking

On day 2 we will introduce you to some intermediate libraries which are built on top of the ones from day 1, namely scientific libraries, visualization libraries and above all, data analysis libraries. With the help of IPython, by the end of day 2 you'll be able to load, clean, munge and transform efficiently most of the datasets which can fit in memory. You'll also be able to create beautiful statistical visualizations, as well as automatically creating documents and reports.

  • Scientific Python package
  • Intermediate Data Analysis
  • Improved visualizations
  • Documentation and reporting

Goal: End-of-day financial report

Part III - In Practice

Day 3 - Getting the most out of Python

On day 3 we will tackle some advanced libraries which are built on top of the material from the previous days. We will use machine learning techniques to estimate the optimal parameters, perform highly efficient computations for option pricing as well as learning which tools to use to extract data from an online resource.

  • Online Information retrieval
  • High-Performance Computing
  • Machine Learning libraries
  • Parallelization

Goal: Scrape online data and estimate the optimal parameters of a particular model

Download the detailed course programme: Detailed Programme (.pdf)


Please note that the number of participants is limited

Upon registration you'll receive an e-mail with the fee payment instructions

Before November 23,2015 Before December 4,2015 After December 4,2015
Discounted Fee * 90€ 110€ 130€
Normal Fee 140€ 180€ 230€
(*) The discounted fee applies for full-time students or academics.

What does the registration cover?

Certificate upon conclusion of the workshop

Welcome pack with hard-copies of the most relevant material

Coffee-breaks with assorted caffeinated drinks, to keep you going through the intense schedule

Awesome Workshop Dinner on Thursday 10th. Venue TBA

Register Now!


Course Instructor

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José Pedro Silva

Bergische Universitat Wuppertal

José Pedro Silva is currently engaged in his PhD at Bergische Universität Wuppertal in Financial Mathematics. He holds an Early Stage Researcher position at the European project ITN-Strike with focus on Model Order Reduction. Previously, he has been involved in different projects, with CERN and European Space Agency, as part of his academic education in Physics.


Pedro Pólvora


José Cruz


Filipe Santos


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Sara Lopes

Special Organizer


Questions? Get in touch !
Drop us an e-mail at info(at)


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