DataCamp Digest February 2021: Trends for the New Normal
Welcome to the brand-new DataCamp Digest: a monthly roundup of key topics in the field of data. This month will cover trends for the new normal, the state of artificial intelligence in 2020, open-AI’s new DALL.E algorithm, data quality best practices, and more.
Trends for the next normal | McKinsey
2020 and the pandemic changed everything. 2021 will be a year of transition, from one “normal” to another. From accelerated digital transformation to the dawn of the future of work, what are the trends that will shape the new normal?
Global survey on the state of AI in 2020 | McKinsey
As artificial intelligence adoption increases throughout the organization, which business areas are deriving the most value today? What separates leaders from the rest of the field, and what are the challenges still remaining to scaling machine learning in the enterprise?
Over the past decade, we’ve seen an explosion of tools in the modern data stack, enabling data scientists and analysts to get from data to insights faster. This article will take a close look at the history of the data tooling stack, its shortcomings, and how the next generation of data tools will be designed with data democratization in mind.
DALL.E: Creating images from text | OpenAI
OpenAI introduced a new deep learning algorithm based on GPT-3, trained to generate images from text descriptions. Check out how it performs in the article, and what an AI-generated illustration of a “baby daikon radish in a tutu walking a dog” looks like.
Upskilling for Shared Prosperity | World Economic Forum
As we transition into the future of work, upskilling has never been more important. This World Economic Forum report outlines the need for major upskilling initiatives, the economic case for upskilling across a wide range of industries and locations, and what organizations can do today.
The People + AI Guidebook | PAIR
This Google-developed guidebook offers a range of resources on best practices for scoping and evaluating AI projects for product managers, project managers, and designers. — PAIR
Data Quality at Airbnb | Airbnb
In this series of articles, the Airbnb data science team outlines its data quality initiative and how it was able to define a shared gold standard for data quality across the organization. The series of articles provides a great overview of how to increase trust in data across the organization.
In this article, Riskified’s VP of Data Science Elad Cohen outlines how the data science team at Riskified identifies and prioritizes new directions in research and product development.
Best of Machine Learning with Python | Lukas Masuch
This GitHub repository is a weekly updated list containing 840+ open-source projects and datasets in Python across 32 different categories ranging from machine learning, data visualization, recommender systems, data pipelines, streaming, and more.
Machine Learning Operations | INNOQ
Want to know more about MLOps? Learn about the motivation for MLOps, best practices when managing end-to-end machine learning workflows, and the state of tooling in MLOps today.
Anomaly detection in SQL | Haki Benita
This article deep dives into building a simple anomaly detection system that works with just high school-level statistics and SQL.
Read our white paper on how data science is headed for further democratization, and how 2021 and beyond will be defined by data fluency. If you prefer video over reading, you can watch our webinar.
We’re doubling down on our commitment to donate free DataCamp subscriptions for those most impacted by the pandemic. Find out how you can help in this blog post.