Domain Expertise Novice Session List
Accelerate Python with Julia
Stephan Sahm
You want to accelerate your Python code, but going C is too tedious? Julia is a fresh alternative which flows like Python and runs like C. Join this tutorial to learn how to use Julia to easily speed up Python.
Actionable Machine Learning in the Browser with PyScript
Valerio Maggio
Interactive ML apps in the browser with zero installation and no server needed? Come to my talk to know how..
Ask-A-Question: an FAQ-answering service for when there's little to no data
Suzin You
Doing data science in international development often means dealing with more resource-constraints. This talk will walk you through Ask-A-Question, a simple FAQ-answering service for when there's little to no data that we built for WhatsApp helplines for public health.
AutoGluon: AutoML for Tabular, Multimodal and Time Series Data
Caner Turkmen, Oleksandr Shchur
Learn about #AutoML and @AutoGluon, which can handle a range of tasks from regression to image classification and time series forecasting with state-of-the-art performance. #AutoML #datascience
Behind the Scenes of tox: The Journey of Rewriting a Python Tool with more than 10 Million Monthly Downloads
Jürgen Gmach
Behind the Scenes of tox: The Journey of Rewriting a Python Tool with Over 10 Million Monthly Downloads
BLE and Python: How to build a simple BLE project on Linux with Python
Bruno Vollmer
Learn what BLE is and how to use it with Python. @bvollmer5 shows in this talk how you can easily build a Linux-based BLE server for your next project.
Building Hexagonal Python Services
Shahriyar Rzayev
Building Hexagonal Python Services from scratch using Repository, Unit of Work and Use Cases patterns
Common issues with Time Series data and how to solve them
Vadim Nelidov
Handling time series data is an important yet not an easy task. After this talk you will learn to identify, understand, and resolve time series issues such as divergence, delayed data, time series imputation and impact of outliers.
Contributing to an open-source content library for NLP
Leonard Püttmann
Learn to build amazing open-source enrichments for natural language processing!
Cooking up a ML Platform: Growing pains and lessons learned
Cole Bailey
What is a ML platform and do you even need one? When should you consider investing in your own ML platform? What challenges can you expect building and maintaining one? Join my talk at PyData to hear how we are cooking up our own ML platform at @Delivery Hero!
Create interactive Jupyter websites with JupyterLite
Jeremy Tuloup
Do you want to create your own interactive Jupyter website with JupyterLite? Check out this step-by-step tutorial and learn how to configure and customize your website 💡
Data Kata: Ensemble programming with Pydantic #1
Lev Konstantinovskiy, Gregor Riegler, Nitsan Avni
Write code as an ensemble to solve a data validation problem using P. Working together is not just about code - we will see what it is like to listen to colleagues, make typos in front of everyone, become a supportive team member, defend our ideas and maybe even accept criticism.
Data Kata: Ensemble programming with Pydantic #2
Lev Konstantinovskiy, Gregor Riegler, Nitsan Avni
Write code as an ensemble to solve a data validation problem with Py. Working together is not just about code - we will see what it is like to listen to colleagues, make typos in front of everyone, become a supportive team member, defend our ideas and maybe even accept criticism.
Data-driven design for the Dask scheduler
Guido Imperiale
Historically, changes in the scheduling algorithm of Dask have often been based on theory, single use cases, or even gut feeling. Coiled has now moved to using hard, comprehensive performance metrics for all changes - and it's been a turning point!
Fear the mutants. Love the mutants.
Max Kahan
Developers often use code coverage as a target, which makes it a bad measure of test quality. Mutation testing changes the game: use your code to create mutants that break your tests, and you'll quickly start to write better tests! Come and learn to use it in your CI/CD process.
Geospatial Data Processing with Python: A Comprehensive Tutorial
Martin Christen
Learn how to use Python to process geospatial data in this comprehensive tutorial! You'll gain hands-on experience with many Geo modules, learning how to read and write spatial data, perform coordinate system transformations, create interactive maps, and more.
Getting started with JAX
Simon Pressler
Getting Started with JAX! Hands-on tips to overcome your first hurdles.
Have your cake and eat it too: Rapid model development and stable, high-performance deployments
Christian Bourjau, Jakub Bachurski
Python's data science tools are fantastic for data exploration and model development, but the price is often a slow and difficult deployment. Join us to find out what tools we have developed to have the cake and eat it too!
How Chatbots work – We need to talk!
Yuqiong Weng, Katrin Reininger
We need to talk - All about concepts, techniques as well as practical experience with the Rasa framework for building a chatbot
How Python enables future computer chips
Tim Hoffmann
Learn how we adopted Python to build the computer chips of the future
How to increase diversity in open source communities
Maren Westermann
Learn about strategies for increasing diversity in #opensource projects presented by @MarenWestermann
Introducing FastKafka
Tvrtko Sternak
"Don't miss our talk on FastKafka, a Python library for easy Kafka communication! #PyCon #Kafka #FastKafka"
Keynote - A journey through 4 industries with Python: Python's versatile problem-solving toolkit
Susan Shu Chang
Susan, Principal Data Scientist at Elastic, shares her experiences with Python in 4 industries, from telecom, gaming and beyond.
Keynote - Lorem ipsum dolor sit amet
Miroslav Šedivý
A randomly real and a really random journey to discover the balance between real and random data!
Keynote - Towards Learned Database Systems
Carsten Binnig
ML and DBMSs? Carsten talks about data-driven learning where the idea is to learn the data distribution over a complex relational schema.
Let's contribute to pandas (3 hours) #1
Noa Tamir, Patrick Hoefler
Join our beginner friendly, mentored contributing to @pandas_dev workshop at PyData Berlin! 🥳 #opensource #pandas
Let's contribute to pandas (3 hours) #2
Noa Tamir, Patrick Hoefler
Join our beginner friendly, mentored contributing to @pandas_dev workshop at PyData Berlin! 🥳 #opensource #pandas
Maps with Django
Paolo Melchiorre
"Maps with Django" Keeping in mind the Pythonic principle that simple is better than complex we'll see how to create a web map with the Python based web framework Django using its GeoDjango module, storing geographic data in your local database on which to run geospatial queries.
Postmodern Architecture: The Python Powered Modern Data Stack
John Sandall
Learn how to upgrade your pandas pipelines powering DAG workflows to a Python Powered Modern Data Stack, demystify the jargon from ETL to ELT, and see how tools like dbt can integrate with Python to change how data pipelines are built and maintained.
Practical Session: Learning on Heterogeneous Graphs with PyG
Ramona Bendias, Matthias Fey
Building and learning on heterogeneous graphs with PyG in a practical session
Pragmatic ways of using Rust in your data project
Christopher Prohm
Pragmatic ways of using Rust in your data project - strategies to speed up your data pipelines without rewriting the whole program.
Prompt Engineering 101: Beginner intro to LangChain, the shovel of our ChatGPT gold rush."
Lev Konstantinovskiy
A modern AI start-up is a front-end developer plus a prompt engineer" is a popular joke on Twitter. This talk is about LangChain, a Python open-source tool for prompt engineering.
Rethinking codes of conduct
Tereza Iofciu
Did you know that the Python Software Foundation Code of Conduct is turning 10 years old in 2023? It was voted in as they felt they were “unbalanced and not seeing the true spectrum of the greater community”. Why is that a big thing? Come to my talk and find out!
Shrinking gigabyte sized scikit-learn models for deployment
Pavel Zwerschke, Yasin Tatar
Shrinking gigabyte sized scikit-learn models for deployment: this talk shows how to deploy machine learning models with up to 6x disk space improvement
Streamlit meets WebAssembly - stlite
Yuichiro Tachibana
Streamlit, a pure-Python data app framework, has been ported to Wasm as "stlite". See its power and convenience with many live examples and explore its internals from a technical perspective. You will learn to quickly create interactive in-browser apps using only Python.
The Beauty of Zarr
Sanket Verma
Hi all, I’ll be talking about Zarr, an open-source data format for storing chunked, compressed N-dimensional arrays, along with a hands-on session. If you work with huge datasets in local/cloud storage and looking for an efficient format, please attend my talk. Thanks!
The bumps in the road: A retrospective on my data visualisation mistakes
Artem Kislovskiy
Join us for a talk: The bumps in the road: A retrospective on my data visualisation mistakes, on data visualisation and how it's essential for conveying insights from data. We'll discuss best practices with Matplotlib, the limitations of static visualisations, and how CI can stre
Thou Shall Judge But With Fairness: Methods to Ensure an Unbiased Model
Nandana Sreeraj
Biased models can impact each of us. While it may feel abstract, AI fairness can be achieved through many methods and metrics. More so, mitigation reports can initiate you to responsible AI. Check out my talk & demo at PyData Berlin.
When A/B testing isn’t an option: an introduction to quasi-experimental methods
Inga Janczuk
Have you ever wanted to know the causal effect of an action but A/B testing wasn’t an option? Here’s a brief helicopter tour over quasi-experimental methods that can be used instead!
You've got trust issues, we've got solutions: Differential Privacy
Vikram Waradpande, Sarthika Dhawan
What if I tell you I could answer everything about you without knowing you using Differential Privacy
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