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Data science tools are powerful for investigating the current pandemic and other outbreaks, when accurate and actionable data are crucial. Epidemiologist and R Epidemics Consortium leader Amrish Baidjoe shared his insights into using data science to fight disease, from modeling to automation to new technologies.
Last week I posted a full introduction to factor analysis, plus a workflow demonstrating one use of this analytic approach in Designer. But now the process is even easier with a macro -- attached at the bottom of this post -- that you can easily grab and put into your own workflow!
What if you believe something is happening in your data that isn’t precisely reflected by a single variable you measured -- maybe because it wasn’t or couldn’t be observed? Learn about one way to identify explanations for that mystery.
We’d planned to celebrate Take Your Kids to Work Day with this fun new video, but right now, you may be working alongside your kids every day. Maybe it’s time to share some data expertise with them -- and do some laundry, too!
Now, at your fingertips, a one-page summary of key Python Tool functions! You’ll also find productivity-maximizing Jupyter Notebook keyboard shortcuts and some essential functions for manipulating your data with pandas.
How do you figure out what to include in an interactive data experience, and how do you design it to effectively communicate a data story? The many COVID-19 data dashboards all look and feel different. Our podcast details the creation of one of the most visited pandemic data sites, and we've got more stories of data communication and design here, plus resources.
You walk down one aisle of the grocery store to get your favorite cereal. On the dairy aisle, someone sick from COVID-19 coughs. Did your decision to grab your cereal before your milk possibly keep you healthy? How can these unpredictable, near-random choices be included in complex models?
Feature engineering is challenging because it depends on leveraging human intuition to interpret implicit signals in datasets that machine learning algorithms use. Consequently, feature engineering is often the determining factor in whether a data science project is successful or not.