This site uses different types of cookies, including analytics and functional cookies (its own and from other sites). To change your cookie settings or find out more, click here. If you continue browsing our website, you accept these cookies.
You could make a data visualization that you think is the most beautiful thing ever -- but it could be mostly useless for many viewers. Learn about some key ways to make your data visualization better for everyone.
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.
How about using Facebook's Prophet package for time series forecasting in Alteryx Designer? With Prophet, you are not stuck with the results of a completely automatic procedure if the forecast is not satisfactory — an analyst with no training in time series methods can improve or tweak forecasts using a variety of easily-interpretable parameters.