Amazon and Coursera have teamed up to create Getting Started with AWS Machine Learning. Topics covered are:
- Machine Learning
- Computer Vision
- Amazon Sagemaker
I am putting together some of my own resources on Data Strategy. Here are a few of the top resources I found helpful so far.
- What is a Data Strategy? – various definitions of a data strategy
- The 5 essential Components of a Data Strategy – a detailed whitepaper(PDF) from SAS
- How to Create a Successful Data Strategy – a detailed report from MIT
- How Do You Develop a Data Strategy (including 6 steps) – by Bernard Marr, He has created more data strategies than anyone, so his advice is rock-solid. Also, the entire site contains more helpful information.
- Building the AI-Powered Organization – while not specific to data strategy, it fits the topic
Keep watching the blog for more information around my thoughts on Data Strategy.
A number of new impactful open source projects have been released lately.
Open Source Data Science Projects
- Pythia – from Facebook for deep learning with vision and language, “such as answering questions related to visual data and automatically generating image captions “
- InterpretML – from Microsoft, ” package for training interpretable models and explaining blackbox systems “
- ML framework for Julia – from Alan Turing Institute, MLJ is a machine learning toolbox for Julia
- Plato – a conversational AI platform from Uber
Is the list missing a project released in 2019? If so, please leave a comment.
Just released this week, Nuts about Data, is a fun introductory book about the data science process. Meor Amer tells a witty story about squirrels, mining for nuts, teamwork, and survival. It brings together the entire data science lifecycle from asking questions to final storytelling.
It is a quick read and really fun. I highly recommend it and hope you enjoy it.