nibble dispatch

By Florent ( weekly - Issue #18: "One, Two, Automate", TensorFlow 2, Pycon 2019 and much more!!



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nibble dispatch

June 5 · Issue #18 · View online

Curated essays about the future of Data Science. Production Data Science and learning resources for continuous learning. Covers Data Science, Data Engineering, MLOps & DataOps. Curated by people at

“One, Two, Automate”
Our top pick this week is’s DataOps Principles.
Not much to be said, at nibble we firmly believe that data success comes from bringing together data science, data engineer and dataops. 🚀
At the end of the day, DataOps is all about people. A self-motivated team with appropriately diverse skills, clearly tasked with deriving business value from data, will generate the rest.

"One, Two, Automate"
"One, Two, Automate"
The role of a Data Science Manager
Driving impact + Building a world class team
Whether, you’re a data manager, aspiring to be one or working in a data science team you will want to read this article by the data science team at Sequoia Capital.
The misunderstood role of a data engineer
“Oh, you’re a data… something?”
If you’re a data engineer, you will relate. If you’re not, read this article and you won’t hurt data engineers feelings any more. 😉
Put down the deep learning
When not to use neural networks and what to do instead
Not gonna lie, I love the provoking title of this talk. Especially when performed from a data scientist from Kaggle. At Kaggle, they shouldn’t care about anything but performances, right?? By Rachael Tatman, freshly from Pycon 2019:
Learning resources
Tensorflow 2.0 alpha is out
Tensorflow 2.0 alpha is out
Getting started with TensorFlow 2.0
No more sessions and placeholders. 😅
Tensorflow 2.0 is pretty exciting, if you wanna catch up, be sure to watch this keynote from Google I/O 2019.
The sleeping beauty problem: a data scientists perspective
The Sleeping Beauty problem is a thought experiment in decision theory that touches upon such foundations of probability.
If you’ve never heard about the sleeping beauty problem and are interested in data science beyond machine learning, I would strongly encourage you to take the time to read this.
Can you see the Markov Chain?
Can you see the Markov Chain?
Improve your python: yield and generators explained
An old post that I recently rediscovered while trying to fix an issue while building generators of my own. This article by Jeff Knupp is still one of the It’s one of the best about the topic.
An intuition behind Poisson Distribution
A Poisson Process is a model for a series of discrete event where the average time between events is known, but the exact timing of events is random. Understanding the Poisson Distribution will help you modelize this kind of processes. This very short tutorial will help you develop some intuition about it. If you want to dig deeper we got your covered.
Enjoy you week! 🤗
🍪Bonus Also from Pycon 2019, this crazy 3 hours long talk about lambda calculus by David Beazley. Tell us if you can handle it! 😉
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