Learn how to perform a tidy approach to classification problem with the new parsnip R package for machine learning.
Customer Churn Modeling using Machine Learning with parsnip
Written by Diego Usai on November 18, 2019
Is Your Job's Data Science Tech Stack Intimidating?
Written by Matt Dancho on November 15, 2019
A data science team has many tools that all need to be integrated. And, this can be INTIMIDATING. Here are some tips to deal with the complexity of a data science tech stack.
Part 2 - Data Science with AWS (A Top Skill for 2020)
Written by Matt Dancho on November 13, 2019
Organizations depend on the Data Science team to build distributed applications that solve business needs. AWS provides an infrastructure to host data science products for stakeholder to access.
Apply Data Science to Improve Addiction Treatment
Written by Matt Dancho on November 11, 2019
Learn how one Business Science student created a data product that aims to help his organization improve the quality of care while reducing cost.
How to Sell Your Manager on A Data Science Course Purchase
Written by Matt Dancho on October 24, 2019
Expert Shiny Developer with AWS - Course Launch Today!
Written by Matt Dancho on October 21, 2019
Learn how to build Scalable Data Science Applications using AWS Cloud Technology.
Course Launch - Expert Shiny Developer with AWS in 7 Days!
Written by Matt Dancho on October 14, 2019
We've crafted an amazing course to teach Data Scientists and Programmers how to develop applications and deploy into production! We've combined an innovative program with a clear-cut path to production using R, Shiny, and AWS! You'll undergo a complete transformation. Time to accelerate your career!
Web Scraping Product Data in R with rvest and purrr
Written by Joon Im on October 7, 2019
Learn how to web scrape HTML, wangle JSON, and visualize product data from the Bicycle Manufacturer, Specialized Bicycles.
Cleaning Anomalies to Reduce Forecast Error by 9% with anomalize
Written by Matt Dancho on September 30, 2019
We can often improve forecast performance by cleaning anomalous data prior to forecasting. This is the perfect use case for integrating the clean_anomalies() function from anomalize into your forecast workflow.
PDF Scraping in R with tabulizer
Written by Jennifer Cooper on September 23, 2019
Learn how to scrape and wrangle PDF tables of a Report on Endangered Species with the tabulizer R package and visualize trends with ggplot2.
Big Data: Wrangling 4.6M Rows with dtplyr (the NEW data.table backend for dplyr)
Written by Matt Dancho on August 15, 2019
Wrangling Big Data is one of the best features of the R programming language - which boasts a Big Data Ecosystem that contains fast in-memory tools (e.g. data.table) and distributed computational tools (sparklyr). With the NEW dtplyr package, data scientists with dplyr experience gain the benefits of data.table backend. We saw a 3X speed boost for dplyr!
Introducing correlationfunnel v0.1.0 - Speed Up Exploratory Data Analysis by 100X
Written by Matt Dancho on August 7, 2019
I'm pleased to announce the introduction of correlationfunnel version 0.1.0, which officially hit CRAN yesterday. The correlationfunnel package is something I've been using for a while to efficiently explore data, understand relationships, and get to business insights as fast as possible.