Many data scientists struggle with distributing their work, however, you can make that a problem of the past thanks to Shiny. Here are five reasons you should learn Shiny and why it is a game-changer for upskilling your career.
Shiny is an R web framework with a HUGE ECOSYSTEM of interactive widgets, themes, and customizable user interfaces called the Shinyverse. In this article, we use Shiny to make our R Markdown Report interactive.
Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.
Exploratory Data Analysis is what every data scientist does to understand actionable insights from the data. This process used to take forever. Not anymore...
Max Kuhn, from RStudio, discusses in-depth feature engineering for customer analytics. Watch Max and Matt tackle a tough feature engineering problem for customer analytics prediction.
I never thought I'd be able to make publication-ready statistical plots so easily. Seriously. Thanks to ggstatsplot.
In order to determine where you wish to set your career trajectory, you need to understand the grey area and differences between data scientists and data analysts.
Data frames (like Excel tables) are the main way for storing, organizing, and analyzing data in R. Here are 4 ways using the tidyverse.
For years Python and R have been pitted as mortal enemies in the world of data science, enticing its practitioners to choose a side and never look back - not anymore. It's time for these two titans to join forces through reticulate which allows us to use Python and R together!
One reason interest in machine learning jobs will continue to grow is how lucrative the pay is. Another is how interesting the work is. If you're looking to plant your foot in a growing industry, then machine learning could be for you. The average machine learning salary, according to Indeed's research, can be anywhere between $96,00 - $146,085.
In 10-minutes, learn how to visualize and impute in R using ggplot dplyr and 3 more packages to simple imputation. Here are the links to get set up.