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 https://nibble.ai/
From Jupyter to Prod (see below for an overview of the different approaches to putting ML models in production)
While so many people are trying to enter the field of data science, it’s easy to get misguided and focus on the wrong stuff. Peter Scobas shares his experience on how he made the jump from economics to data science: https://peterscobas.com/2019/04/26/how-i-became-a-data-scientist/.
[…] what I’ve learned is that it is a bad idea for aspiring data science and analytics applicants to immediately jump into trying to learn fancy machine learning or deep learning models.
The role of the data engineer is no longer to just provide support for analytics purpose but to be the owner of data-flows and to be able to serve data both to production and for analytics purpose.
Human-Centered Artificial Intelligence
Google just released their People + AI Guidebook about the multidisciplinary and human-centered approach to designing with machine learning and AI
It’s not entirely clear what level of mathematics is necessary to get started in machine learning. If you need to brush up your math skills, this course by Microsoft on EdX provides a hands-on approach on topics like equations, functions, vectors, matrices, statistics and probability.
Foundations of Data Science
Microsoft Research just release a free book that covers the theory for data science they expect to be useful in the next 40 years. The book is available for free as a pdf here, you can also watch the presentation video.
