Data Science With R Course Series - Week 3

Written by Matt Dancho on October 1, 2018

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Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables rapid exploration, modeling, and communication in a way that no other programming language can match: SPEED! This is why you need to learn R. Time is money, and, in a world where you are measured on productivity and skill, R is your machine-learning powered productivity booster.

In this Data Science With R Course Series, we’ll cover what life is like in our ground-breaking, enterprise-grade course called Data Science For Business With R (DS4B 201-R). The objective is to experience the qualities that make R great for business by following a real-world data science project. We review the course that will take you to advanced in 10 weeks.

In this article, we’ll cover Week 3: Data Understanding, which is where we expand our exploratory techniques with the goal of exposing key characteristics of the features in our data set.

But, first, a quick recap of our trajectory and the course overview.

Data Science With R Course Series

You’re in the Week 3: Data Understanding. Here’s our game-plan over the 10 articles in this series. We’ll cover how to apply data science for business with R following our systematic process.

Week 3: Data Understanding

Week 3: Data Understanding

In data understanding, you’ll learn two key packages that can help identify characteristics of your data:

  1. `skimr`: For efficiently exploring data by data type (e.g. numeric, character, etc)
  2. `GGally`: For visualizing pair plots for many features within the data

Let’s take a peek at the course.

EDA with skimr

We kick week 3 off with skimr, a package for quickly skimming data by data type. In the course you’ll review both numeric data and character data. This is important to identify quickly what issues may be present such as missing values, numeric data that should be categorical, and so on.

Here’s a snapshot of our first use of the skim() function.

EDA with GGally

Next, we build our knowledge of the data by making use of the GGally package for visually identifying relationships in the data. We focus on identifying relationships between the target (employee attrition) and various features in the data set. We make use of the ggpairs() function that enables us to visualize the complex relationships.

Next, you’ll build a custom plotting function with Tidy Eval (learned in Week 2) to extend the functionality of ggpairs() and enable honing in on the relationship between each feature and its interaction with attrition.

We end Week 3 with the second Challenge.

Challenge #2

In Challenge 2, you’ll use your custom plotting function plot_ggpairs() to investigate many complex relationships. You’ll combine features into logical groups based on business knowledge and then visualize the grouped features together to explore their complex relationships to attrition.

Data Science For Business With R (DS4B 201-R)

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Next Up

The next article in the Data Science With R Series covers Data Preprocessing. We’ll learn about an awesome package called recipes that enables preprocessing workflows. We’ll focus on two aspects of data preparation:

  1. Preparing data for people
  2. Preparing data for machines

Week 4: Data Preprocessing

New Course Coming Soon: Build A Shiny Web App!

You’re experiencing the magic of creating a high performance employee turnover risk prediction algorithm in DS4B 201-R. Why not put it to good use in an Interactive Web Dashboard?

In our new course, Build A Shiny Web App (DS4B 301-R), you’ll learn how to integrate the H2O model, LIME results, and recommendation algorithm building in the 201 course into an ML-Powered R + Shiny Web App!

Shiny Apps Course Coming in October 2018!!! Sign up for Business Science University Now!

DS4B 301-R Shiny Application: Employee Prediction

Building an R + Shiny Web App, DS4B 301-R

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Data Science for Business Curriculum

Business Science University is an educational platform that teaches how to apply data science to business. Our offering includes of a fully integrated, project-based 3-Course R-Track.

BSU R-Track Course Curriculum

Each course takes the student through their progression in a data science journey. Begin your journey with DS4B 101-R which teaches foundations using the tidyverse. Next, master machine learning for business with DS4B 201-R, where you learn H2O and many advanced R packages. Finish with DS4B 301-R where you learn to develop high-performing web applications using Shiny, a powerful framework for productionizing R code.

R-Track Curriculum Summary

Business Analysis with R (Beginner) - Data Science Foundations 7-Week course 12 tidyverse Packages 2 business projects
Data Science For Business with R (Intermediate/Advanced) - Machine Learning + Business Consulting 10-Week course H2O, LIME, recipes, and 10 more packages 1 end-to-end business project
Web Apps for Business with Shiny (Advanced) - Web Frameworks (Bootstrap, HTML/CSS) and Shiny 6-Week course Shiny, shinytest, shinyloadtest, profvis, and more! Take machine learning model into production

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