Data Science With R Course Series - Week 7

Written by David Curry on October 29, 2018





June 1 - Shiny Web Applications for Business - OPENS!!!

On June 1st, we're opening the doors to Shiny Web Applications For Business (Level 1), the perfect course to begin developing Shiny web apps!

Learn More About Our Shiny Web Apps Course!


Shiny Web Applications for Business


After week 7, you will be able to communicate confidently which model features are the most important.

Interpretability is a very important topic in machine learning. The automated machine learning tool, H2O, makes a data scientist’s life easier, however it doesn’t remove the need to understand your model. As the data scientist, you need to be able to explain why the selected model is the best.

In this week’s curriculum, you learn how to explain “black-box” machine learning models with LIME. LIME stands for Local Interpretable Model-Agnostic Explanations and is used to understand which model features have the most predictive impact.


Here is a recap of our trajectory and the course overview:

Recap: Data Science With R Course Series

You’re in the Week 7: Machine Learning Interpretability with LIME. 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 7: Machine Learning Interpretability with LIME


Student Feedback


Week 7: Machine Learning Interpretability with LIME

Overview & Setup

The Overview & Setup will walk through the setup to support LIME within the project workflow, and prepare the machine learning model for interpretation.

After understanding the features that make up your machine learning model, you will be able to answer the critical business question, Why is employee churn happening?


Feature Explanation With LIME

Jump right into learning about the LIME package and how it works to interpret machine learning models. Here you will make predictions using your model and investigate employee turnover model results. You will then use LIME to produce an explanation of why certain employees were selected.


Challenge #4

In this 2 part challenge, you will recreate a single explanation plot and a full explanations plot to visualize important features.

After you complete the challenge, walk through the Solution videos to compare and review your working solution.





You Need To Learn R For Business

Data Science For Business With R Course

To be efficient as a data scientist, you need to learn R. Take the course that has cut data science projects in half (see this testimonial from a leading data science consultant) and has progressed data scientists more than anything they have tried before. Over 10-weeks you learn what it has taken data scientists 10-years to learn:

  • Our systematic data science for business framework
  • R and H2O for Machine Learning
  • How to produce Return-On-Investment from data science
  • And much more.

Start Learning Today!



Next Up

The next article in the Data Science With R Series covers Evaluation: Calculating The Expected ROI (Savings) Of A Policy Change.

Learn how to communicate the cost savings of using your model. Inform the business to make decisions around time and resources based on the value of your findings.

Use the Expected Value Framework after your model is complete to explain which features are most important. The Expected Value Framework is a method to calculate savings from implementing business changes based on the model’s results.

Week 8: Evaluation: Calculating The Expected ROI (Savings) Of A Policy Change



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


Get Started Today!


Announcements

NEW - Data Science Fundamentals Newsletter

We just launched a new initiative to help you take your data science skills to the next level. Every Tuesday we send you new resources, tips, and advice to accelerate your learning.

Data Science Fundamentals

Sign Up For Data Science Fundamentals Newsletter



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

Join Business Science University Today



Stay Connected, Get Updates, Learn Data Science

If you like our Business Science Software (anomalize, tidyquant, tibbletime, timetk, and sweep), our courses, and our company, you can connect with us:

Start learning today! Business Science University


Subscribe and we'll keep you updated.