— Insight —
The NBA is well-known for it's extensive use of data acquisition and analytics but many commentators are harsh critics of the practice. Just check out the first video in this article and you'll quickly understand. Do you think it's hopeless that data could make sports more engaging? Or are you "one of those idiots who believes in analytics"...?
— Sponsored Link —
How to choose the right ML approach for your business goals and how to determine the best data labeling technique for your use cases.
— Tools and Techniques —
Great post! And the insights here apply to projects of all kinds.
"Speed" isn't a word that's typically associated with machine learning teams but speed matters. In this post, Neal Lathia, the Machine Learning Lead at Monzo, offers practical ideas for building systems that get results quickly.
Mobile apps and devices offer the potential for huge health-related studies but few researchers have the expertise that's needed to work with these kinds of datasets. This collection of best practices is derived from several example studies and is a good introduction to the challenges of working with consumer-generated health data.
Do you really know what's in your data? Here's a thorough and structured approach for making sure you have a good handle on potential issues before you start writing code. Includes tips for dealing with common problems.
Nice introduction to data lineage: what it is, why it's important, and how the team at Dailymotion approached a solution.
Thrive in the fast-growing world of analytics with the Global Master of Management Analytics from Smith School of Business. Earn your degree while you work from anywhere in the world.
— Resources —
When Brandon Rohrer asked the Internet for favorite linear algebra resources, there were a lot of solid recommendations but two clear winners emerged. If you're looking to step up your understanding of linear algebra, start here.
— Data Viz —
This newly updated guide from Datawrapper is a great introduction to the use of color in data visualization. Includes strategies, tools, and lots of linked references.
People talk about the importance of telling stories with their data but what does that really mean? In this post, Joshua Smith shows that "story" has a broad range and the idea of telling stories may actually undermine the intention to communicate clearly with data.
— Conferences & Events —
- Activate - Deliver hyper-personalized digital experiences w/ search & AI - Sept 9-12, Washington, DC - Save 20% w/ discount code: DE20
- Data Science Salon - Applying AI & machine learning to finance, healthcare & hospitality - Sept 10-13, Miami, FL
- Domino Data Science Popup - Bringing data science leaders together to explore emerging practices & technologies - Sept 12, San Francisco, CA
- Strata Data Conference - Sept 23-26, New York, NY