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DataCamp Digest May 2021

Read our favorite articles from the last month.
May 2021  · 4 min read

 

 

 

Building a learning culture that drives the business forward | McKinsey

In this interview, McKinsey’s Chief Learning Officer Matthew Smith, shares his thoughts around delivering learning programs for 30,000+ employees, his best practices on creating a sustainable learning culture, and how people can become better learners.

AI Adoption in the Enterprise 2021 | O’Reilly

This O'Reilly report covers the state of AI in the enterprise. While the use and deployment of AI in the enterprise has never been more prevalent, organizations still suffer from lack of AI talent, data quality issues, a lack of data culture, and the absence of tooling to scale and monitor AI systems in production .

This has just become a big week for AI regulation | MIT Technology Review

The regulatory space for AI is heating up. Over the past week, the European Union has released its plans for wide-ranging regulations on AI systems, and the Federal Trade Commission released plans to take action on AI systems that exhibit bias and discrimination. Check out this article for a detailed view on both these plans.

Five types of thinking for a high performing data scientist | Anand Rao

In this blog post, Anand Rao, Global Artificial Intelligence Lead at PwC articulates mental models for high-performing data scientists. These mental models are highly useful for data scientists to reason about the world and the problems they’re trying to solve.

Achieving Insights and Savings with Cost Data | Airbnb

Any data-driven organization has to scale its cloud infrastructure and make sure it’s doing so efficiently. In this blog post, find out how the team at Airbnb leveraged AWS cost data to reduce spend and achieve AWS cost savings.

The Right Way to Measure ROI on Data Quality | Monte Carlo Data

Data quality issues can have serious consequences on the bottom line of any aspiring data-driven organization. In this article, learn how to calculate the ROI for increasing data quality and to garner executive support for any data governance or quality effort.

New AI tool calculates materials’ stress and strain based on photos | MIT News

Engineers often spend countless hours solving complex equations to determine the stress and strain on the materials they work with. A new deep-learning-based approach, promises to streamline this process by leveraging image data alone.

Time series forecasting at Microsoft | Yasmin Bokobza & Siddharth Kumar

This series of articles on the Data Science at Microsoft blog deep dives into the best practices for time series forecasting from choosing the right forecasting method to simplifying the forecasting workflow, and more.

OpenAI-powered Linux shell uses AI to Do What You Mean | River’s Educational Channel

Ever get tired of forgetting bash syntax? This project leverages OpenAI’s GPT-3 to generate bash commands based on a written description. Check out how it performs on commands like “find all files in current directory bigger than 1gb”.

Webinar: Democratizing Data in Government Agencies

In this webinar, Senior Data Scientist at the New South Wales Government Alex Scriven outlines what data democratization means in a government context, and how to best achieve it.

Blog: Storytelling for More Impactful Data Science

In this blog post, we outline storytelling techniques for data science to gain better organizational alignment around data projects and initiatives.

Podcast: Creating Smart Cities with Data Science

We’ve relaunched our DataFramed podcast! In this episode, Amen Ra Mashariki, former Chief Analytics Officer of New York City outlines the state of data science in government agencies, how he helped make the city of New York smarter, the state of data literacy in government, and more.

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