Pietro Peterlongo

Programming languages used in Scientific Computing have evolved from Fortran to Python, but many languages are still actively used and are important in the ecosystem (C, C++, R). New languages made specifically for solving problems of the scientific ecosystem have been created (Julia).

While Data scientists focus on mastering algorithms and code, domain expertise often takes a backseat. Taking examples from logistics and supply chain, I will argue that domain knowledge is critical for success of Data Science projects and I will provide ideas on how to upskill your domain expertise

Forecasting is a common activity that has clear business value in various domains but it is not a very common skill that Data Scientists have or feel confident about. In this crash course I will cover the fundamentals of Time Series forecasting from the basic methods to more advanced techniques....

Nel panel "Advent of Python", esploreremo l'emozionante "Advent of Code", un'avventura annuale di programmazione che unisce sfide di programmazione e spirito festivo. Discuteremo cos'è l'Advent of Code, perché è così appassionante e condivideremo esperienze personali e lezioni apprese,...

What if I could tell you that there is a language that looks like Python but achieves the performance of C? And that also has the power of metaprogramming of Lisp? Nim is a statically typed compiled language that does all the above and more. And it is easy to learn for Pythonistas.