PyCode Conference 2020

I am a doctoral researcher at UCLouvain. I work in logic, language and information, focusing on building probabilistic semantics for concept networks, providing relevance criteria for dependency patterns in these semantics, and characterizing the related learning procedures implementable in machine learning. In my dissertation, entitled "Hypothetical Reasoning. From Formal Learning Theory to Machine Learning", I use probabilistic logic and neural networks to define learning procedures for acquiring and testing conditional information. My approach is multidisciplinary, ranging from logic and theoretical computer science to probability theory, Bayesian networks and machine learning. My other areas of expertise include legal, political and economic aspects of privacy and surveillance, as well as using statistical methods to evaluate the development of civil society and the condition of modern democracies. I am particularly interested in methods used to diagnose the dynamics of civil society in Central and Eastern Europe and at the EU level.

Between language modelling and social science. Python for interdisciplinary research

In this talk, I would like to (1) show how I use Python for research in two seemingly distant fields of academic research: language modelling and political science; as well as (2) briefly discuss the advantages of Python in academic research, including transferability of skills between fields, applicability in interdisciplinary research programs, and availability to research teams with limited funding.

MSc in Computer Science for over a year now and DevOps Engineer for almost four years. Braveheart in the field of Jenkins, Kibana and every other tool that helps developers and tester to work fast and efficiently. Always first for new challenges and keeps looking for a hole in the whole.

Understand the rats - a history of a failure

It's best to learn from someone else's mistakes than your own. That's why I'll show you all the mistakes I made when I was trying to understand the rats speech. It will be a little bit about machine learning (but don't worry, you don't need to be machine learning expert to understand what I'm talking about), rodents, emotions and why it's necessary to sometimes take a break and ask someone completely different for advice.

Dr. Ajit Kumar is a Post-doctoral researcher in Soongsil University, Seoul, South Korea. He has completed his Ph.D. from the Department of Computer Science, Pondicherry University in 2018. His Ph.D. thesis titled "A Framework for Malware Detection with Static Features using Machine Learning Algorithms" focused on Malware detection using machine learning. He is working with Python since 2012 for his research work and other development work. He is also interested in web development, Information security, and Data Science. Python is his language of choice for all the programming related tasks. He has been motivating and training students to adopt Python as his programming language. He loves to write and share the article about Python and its applications.

Python for Building Malware Classifier: From Sample Collection to building Web Application

Malware is a critical threat to all kinds of Cyberinfrastructure. The proposed talk aimed to train the participants through various steps involved in building malware classifier based on machine learning algorithms. 1. Data collection 2.Pre-processing: (duplicate removal, file type identification, etc.) to prepare a suitable dataset for the experiment. 3. Labeling 4. Feature extraction 5. Model building 6. Deploy the classifier as a web application.

Ukrainian Software and Artificial Intelligence Engineer living in Poland. IT Architect at Capgemini. Former Python Developer and Data Engineer at Fortune 500 companies. Core areas are Software Development and Cloud Computing. Especially interested in Artificial Intelligence. PhD student. Taught Information system design and server-side technologies at the University of Silesia. Public speaker at PyCon PL, AWS User Group, CodeteCON, Silesian AI, Data Science Silesia.

DataOps 3.0, or how to build end-to-end data workflows

DevOps improves quality, velocity and scaling of software development. Borrowing methods from DevOps, DataOps offers the same improvements to production data science. This session includes both theory and practice of DataOps 3.0. We will compare all popular workflow managers such as Airflow, Luigi, Azkaban, Oozie as well as cloud-native solutions like Step Functions and Simple Workflow Service (SWF). Finally, this session teaches you how to build workflows in Airflow, Luigi, and Amazon Web Services (AWS).

Final year Masters student in Physics from Jadavpur University, Kolkata and also leading the R&D department of Sirpi Products and Services Private Limited. Coding in R/Python/Fortran. His core area of research is scientific computing.

Learn Lambda Calculus with Python

This is an introduction to the simple concepts of untyped Lambda calculus along with hands-on implementation of the Lambda function application in Python. We define a function and implement it with Python Lambda. We then discuss the concepts of "abstraction", "application" and "substitution". Finally, we study some combinators from combinatory logic, before ending with encoding datatypes in lambda calculus. This talk will be helpful to students with science and python backgrounds.

Data Scientists specialized in spatial and temporal data processing and analysis. Experience gained during projects for European Space Agency, Joint Research Centre and commercial entities. Technical writer, developer of Open Source packages for spatial statistics. Personally I'm a dancer and a breeder of bunnies.

GeoPandas - Python package for spatial data engineering

GeoPandas is the most important Python package when it comes to spatial data processing and engineering and it's importance increases in the world where everything is connected. Talk is quick but thorough and you get to know the most important functionalities of GeoPandas package.

Professor of University of Warsaw. He has been participating in the development of Rosetta software for the last several years (3.2 million lines of C ++ code). His programming lectures are: "Introduction to life sciences programming" at the Faculty of Chemistry, University of Warsaw and "Object-oriented programming" at the Faculty of Physics, University of Warsaw.

VisuaLife: client side data visualization in Python

VisuaLife is a library for the Python language developed for several years, devised to visualize scientific data.

You can use it in a script running locally to create a graph as an SVG file. Much more interesting application is to combine VisuaLife with Brython translator. In this case, the library allows you to create interactive web-pages.

Python replaces JavaScript and runs on the client side. In order to achieve a smooth operation in the browser, it was necessary to thoroughly optimize the code. Connecting the VisuaLife library to a Flask or Django serverallows you to build applications using only one programming language. During the presentation, I will discuss practical aspects related to Brython as well as show the basic functionality of VisuaLife library.

Software developer, electronics engineer, Python fan. Currently exploring area of serverless solutions and IoT. Guitar player in free time.

Python under the hood

There is no such thing as a free lunch - every abstraction brings some cost. While coding in Python we usually don't care about memory management, objects sizes, allocated resources and many other "low level" things. That's nice feature of language since we can focus on solving domain level problems. However, it is also worth being aware what's happening under Python's hood. Within this presentation I will focus on some details related to built-in containers, their implementation quirks and some interpreter level facts.

Eyal has been a developer and engineering team in the past 13 years, currently building a new team at Melio Payments.

Previously co-founder and CTO at Meekan, until its acquisition by Doodle, where he was a tech lead.

Usually you'll find him either hooking systems together to create pleasant developer experiences, or just running.

Hacking the Home Assignment

Home assignments are here to stay as they offer less biased views and are more focused on the quality of your work.

Unfortunately, if you’re a Junior Developer, the quality of work is usually what stands out. Your projects won’t look like what a professional reviewer is used to: They may be messy, half-working, or lacking documentation and instructions.

In this talk you'll learn an organized system for working on a small project while presenting best-practices. That means passing more interview stages, more options to choose from, and better bargaining power for your next job.