DataCamp Digest is our newsletter aimed at providing the most up-to-date insights and news on all things data science. In this edition of the newsletter, we explore the future of jobs, discuss new tools and technologies, explore the future of AI regulation, and more.
The future of jobs in the era of AI | Boston Consulting Group
This report by BCG studies the impact of AI and automation on the job market in several key countries. It prescribes actions governments and organizations can take to alleviate automation woes, key amongst them being talent development.
New AI regulations are coming, are you ready? | Harvard Business Review
The use of AI systems is becoming mainstream and regulators are setting guardrails to ensure the responsible use of AI. In this article, Andrew Burt outlines recent AI regulation efforts, and how organizations can prepare and ensure their models are compliant.
Analytics is a mess | Benn Stancil
Benn Stancil from Mode Analytics discusses the creative, messy, and non-linear nature of data science and analytics. If you ever feel stuck when solving a data science problem, remember that “A mess is the process of progress”.
OpenAI launches $100M startup fund | OpenAI
OpenAI, the organization behind GPT-3, is launching a $100M startup fund for startups that leverage OpenAI tools specifically in “in fields where artificial intelligence can have a transformative effect—like health care, climate change, and education—and where AI tools can empower people by helping them be more productive.”
Good data scientist bad data scientist | Ian Whitestone
A short and snappy post that outlines what differentiates a good data scientist from a bad one.
Speech recognition with no supervision | Facebook AI
The team at Facebook AI leverages self-supervised learning to develop a speech recognition model that can learn to identify any language without any training data. This is huge for the recognition of languages where there is limited training data.
PowerBI coming to a notebook near you | Microsoft
Microsoft created a new Python package that lets you embed PowerBII directly in Jupyter Notebooks. This will make it much easier to embed compelling, editable data stories directly in Jupyter Notebooks.
Ernest Chan breaks down the components of the ML platforms that power the models of the most sophisticated data companies today.
Microsoft Recommenders | Microsoft
A GitHub repository created by Microsoft that covers their best practices in creating recommender systems, with Jupyter Notebooks included. Check out the related projects section for similar repositories on NLP, computer vision, and more.
Thinking in data | Paige Bailey
If you use visual studio this one’s for you. Thinking in data is a set of VS Code extensions that supercharge your analytics and data science workflows on visual studio.
Greykite: A flexible, intuitive, and fast forecasting library | Reza Hosseini
The data team at LinkedIn has open-sourced its Greykite forecasting library on Python. Check out the blog post to find out how it differs from other forecasting packages, and how the team applied it to forecasting problems at LinkedIn.
Flat data | GitHub
The team at GitHub released some tools that make it easy to work with and view data on GitHub.
In this webinar, regional Chief Data and Analytics Officer at Allianz Benelux Sudaman Thoppan Mohanchandralal deep dives into the ins and outs of building data cultures.
In this guide, we outline a framework for evaluating, and scaling data maturity throughout the organization, define the various data maturity stages an organization goes through, and draw a path of initiatives organizations can take to achieve data fluency.
In this episode of DataFramed, Adel speaks with Sergey Fogelson, Vice President of Data Science and Modeling at Viacom on how data science has evolved over the past decade, and the remaining large-scale challenges facing data teams today.
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