While "Big Data" may be an overhyped buzzword, it's not uncommon for Python users to end up with more data than can fit on their laptops. Sampling is great, but sometimes you need to process everything. In the past, Python users didn't have much choice beyond Spark (and the fact that most data lakes were HDFS made it the standard option). But today, even the stodgiest enterprises have migrated a ton of data to cheap blob storage in the cloud. This has freed python users from the misery of the JVM (I mean, hey, it's way better to see a Python error than a JVM stack trace, right?). So as a result, tools like Dask make it much easier to scale the tools Python users love, e.g., NumPy, Pandas, Sklearn. In this talk, you'll learn how to scale your PyData workloads with minimal code changes using Dask so that you can focus on your work without having to learn a new API PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/c/PyDataTV/videos https://www.youtube.com/watch?v=yHnHOSdihKo

Check out Bastian Gruber's book 📖 Rust Web Development | http://mng.bz/KBKK ​ 📖 To save 40% off this book ⭐ DISCOUNT CODE: watchgruber40 ⭐ Learn web development with Rust! In this video, Bastian shows how to choose a runtime and a web framework, create your first REST endpoint, enable logging, and create a multi-platform binary for production deployment. 📚📚📚
Rust Web Development | http://mng.bz/KBKK
To save 40% off this book use discount code: watchgruber40 📚📚📚 About the book:
In Rust Web Development, you’ll learn to build server-side web applications using the Rust language and its key libraries. If you know the basics of Rust, you’ll quickly pick up some pro tips for setting up your projects and organizing your code. This book gets you hands-on fast, with numerous small and large examples. You’ll get up to speed with how Rust streamlines backend development, implements authentication flows, and even makes it easier for your APIs to interact. As you go, you’ll build a complete Q&A web service and iterate on your code chapter-by-chapter, just like a real development project. About the author:
Bastian Gruber is a Solutions Architect at Twilio Inc. He was part of the official Rust Async Working group, and founded the Rust and Tell Berlin MeetUp group. He has worked for one of the world’s largest Digital Currency exchanges, using Rust on its core backend. He has over twelve years of experience as a writer, and blogs regularly on Rust for LogRocket, his own blog, and other magazines and news outlets. https://www.youtube.com/watch?v=R8i6XKmR2aE

Sprints, Scrum, Kanban, Stories, Epics, Retrospectives, Extreme Programming, Velocity…Agile's opaque terminology and practices, plus the zeal of its advocates, can be off-putting to newcomers. Can it even be applied to data science, analytics and machine learning projects? In this talk we provide a gentle introduction to implementing an agile workflow for a data science team. We will demystify the terminology, tools and processes, and provide practical tips from our experience moving all of our client teams and projects to agile workflows in 2021. We've seen an increase in measurable output, better communication and a higher value-per-effort on work delivered. We've found it works especially well for managing research projects with a high level of uncertainty, such as developing machine learning models. Agile's focus on measurable results aligns well with other goal-setting paradigms such as OKRs, but when applied to data scientific projects it encourages best practices such setting clear expectations on how a team validates their work. This light-hearted talk is beginner-friendly with no prior knowledge required. Whilst it may be especially relevant for leaders of data science teams, moving to an agile workflow requires the whole team to understand and buy into the concept. We hope this talk proves a useful resource in this endeavour. PUBLICATION PERMISSIONS:
PyData provided Coding Tech with the permission to republish PyData talks. CREDITS:
PyData YouTube channel: https://www.youtube.com/channel/UCOjD18EJYcsBog4IozkF_7w https://www.youtube.com/watch?v=b6gniYc1sJk