The pace of our modern world, and the impressive volume of data we collect on a daily basis, can be dizzying. Take for example, the hour-by-hour updates and colorful dashboards made by news outlets as they track the spread of novel coronavirus (Covid-19). Organizations need quick and consistent solutions for exploring, analyzing, and acting on
While it might be tempting to liven up a report or presentation with a few 3D graphs, two-dimensional representation is generally better when numbers are the primary information you want to communicate. Nevertheless, on occasions when numeric values aren’t the primary focus, and you’re more interested in showing the shape of the data, adding a
Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way. This tutorial offers a quick example of
At Red Oak Strategic, we utilize a number of machine learning, AI and predictive analytics libraries, but one of our favorites is h2o. Not only is it open-source, powerful and scalable, but there is a great community of fellow h2o users that have helped over the years, not to mention the staff leadership at the
Introduction This post will demonstrate how to use machine learning to forecast time series data. The data set is from a recent Kaggle competition to predict retail sales. You will learn how to: Build a machine learning model to forecast time series data (data cleansing, feature engineering and modeling) Perform feature engineering to build
Applying Regular Expressions This is a tutorial on processing data with regular expressions using Python. It is also a reflection on the advantages and trade-offs that come into play when you use regular expressions. Once you have identified and defined a set of patterns, you can strategically search and extract data from raw text according
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package [crayon-60050ae30710c024471238-i/], which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package [crayon-60050ae307113613493622-i/] and a function called [crayon-60050ae307115042936355-i/], the [crayon-60050ae307118289070637-i/] drawing tool can
