Small Big Data @ PyData NYC 2019
Talk slides
You can view the slides for the talk.
Learn more
Here are some articles I’ve written that you might enjoy:
- Logging for scientific computing: debugging, performance, trust (originally a PyCon 2019 talk)
- Beyond cProfile: Choosing the right tool for performance optimization (originally a PyGotham 2019 talk)
- Production-ready Docker packaging for Python
Training
Data science requires writing software, which means your team is going to be slowed down by bugs, slow code, mysterious errors, and other programming issues.
If you’d like to help your team work faster and more effectively, consider my on-site training classes to help them build their software engineering skills.
Want to improve your software engineering skills?
You use software to analyze data—it's a means, not an end. But you still need to deal with all the problems software development entails: from bugs to slow code to mysterious errors.
You want to trust the software you write, and to ship it on schedule.
So you need improve you software engineering skills, from testing to packaging to performance. And to help you do that I'm writing new articles every week—subscribe to my newsletter to get them in your inbox:
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