DataCamp Digest April 2021: The technology powering your data team
Gartner says that as data science and machine learning platforms continue to gain traction, the industry is rife with innovation and visionary roadmaps. This article breaks down challengers, leaders, and visionaries in this space.
2021 AI Index Report | Stanford University
This report attempts to capture the state of AI’s progress across geographies and industries. It provides a panoramic view of the state of AI research and development, technical progress, ethical challenges, education, and more.
In this report, McKinsey breaks down the importance of HR in helping organizations thrive in a post-pandemic world. It covers a wide range of topics from upskilling and reskilling, adopting new organizational models, adapting company culture, and more.
The Data Visualizations Behind COVID-19 Skepticism | MIT Visualization Group
The team at the MIT Visualization Group analyzed and visualized over half a million tweets and 41,000 visualizations expressing Covid-19 skepticism. Check out their dashboard.
What your data team is using: the analytics stack | Justin Gage
A non-exhaustive but concise breakdown of various tools used across the analytics stack, from data collection, to warehousing, transformation, and insights.
We Failed to Set Up a Data Catalog 3x. Here’s Why | Prukalpa Sankar
As data teams mature and the amount of data an organization leverages scales, creating tools for data discoverability is a must. Check out lessons learned by the team at Atlan when creating their own data catalog.
Data Science for Marketing Optimization — Case Studies from Airbnb, Lyft, and DoorDash | Drazen Zaric
Find out how data science can optimize campaign spend, improve conversion rates, and increase customer lifetime value seen through the lens of case studies at data mature companies.
Uber’s Journey Toward Better Data Culture | Uber Engineering
In this blog post, the Uber Engineering team outlines the challenges data teams face when scaling data science and how they approached solving them.
Applied Machine Learning Resources | Eugene Yan
This is a comprehensive set of papers, articles, and blogs on how successful data mature organizations have solved applied machine learning problems spanning data quality, data engineering, creating feature stores, modeling, and more.
Ploomber | Ploomber
Ploomber is a Python package for building data pipelines for data science and machine learning. In a given pipeline, tasks can be anything from Python functions, notebooks, Python/R/Shell scripts, and SQL scripts.
In this webinar, Deloitte’s Gert de Geyter and Bhavya Dwivedi outline storytelling techniques for data science to gain better organizational alignment around data projects and initiatives.
This blog post outlines the major challenges and opportunities when operationalizing data science in healthcare and how data training is essential for healthcare organizations to become data-driven.