Sarah Kaiser

Do you ever have a weird feeling that something around you is off? I certainly do, and one recurring thought I have is that the vintage boat I'm helping restore has sunk.

This talk explores how AI agents integrated directly into Jupyter notebooks can help with every part of your data science work. We'll cover the latest notebook-focused agentic features in VS Code, demonstrating how they automate tedious tasks like environment management or graph styling, enhance your "scratch notebook" to sharable code, and more generally streamline data science workflows...

There is an increasingly wide range of AI tools that developers have access to, it can sometimes be overwhelming to understand how to best leverage them. In this session, you'll see a real-life example showing how these three tools can work together to enhance data workflows. All in VS Code, we'll...

Spreadsheets are one of the most common ways to share and work with data which helpfully also works great in Python! In this tutorial, we will cover some of the basics and best pratice of consuming and producing spreadsheets in Python as well as a deep dive into how to run Python directly in your spreadsheets.

Working with data in grids or spreadsheets is great for collaboration as there are many different tools to view and edit the files. Data science workflows often include packages like openpyxl to create, load, edit, and export spreadsheets that then are shared with others who can use other tools like Excel, Google Sheets, or IDEs to view them.

With the increasing sizes of data sets and computational tasks, cloud-based resources like databases, GPU computation, data processing pipelines, or hosted Jupyter tools have become a critical resource for scientific development. Projects that do not start at massive scales, though, face the challenge of moving from local developer machines or clusters to fully scaling cloud-based resources.

“It says the package is not installed?” is a common refrain when working on software projects, especially in Python. Creating and configuring reproducible environments is a major part of modern software development and has led to the popularity of tools like Docker to specify where and how code runs.

Visual Studio Code (VS Code) is a free code editor that runs on Windows, Linux, macOS and in your browser. This tutorial aims at Python programmers of all levels who are already using VS Code or are interested in doing so, and will take them from zero (installing VS Code) to a production setup for Python development.