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(This article was first published on <strong><a href="http://www.business-science.io/university/2018/11/05/data-science-with-R-course-week-8.html"> business-science.io - Articles</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>This is a fun part of the course where you learn how to add value to the business by providing <strong>ROI-Driven Data Science!</strong></p>
<p>Week 8 will teach you how to calculate a simple policy change – <strong>Implementing a No Overtime Policy.</strong> You will calculate the expected savings compared to the current policy of allowing overtime.</p>
<p>Next, develop a more sophisticated policy change – <strong>Implementing a Targeted Overtime Policy</strong> – wherein you target high-flight risk employees.</p>
<p>We’ll teach you everything you need to know about the <strong>Expected Value Framework</strong> so you can begin to implement this for <strong>ANY BINARY CLASSIFICATION MODEL</strong>. This includes Customer Churn, Targeted Advertisements, and more!</p>
<p></p>
<p>Here is a recap of our trajectory and the course overview:</p>
<h2>Recap: Data Science With R Course Series</h2>
<p>You’re in the <a rel="nofollow" href="http://www.business-science.io/university/2018/11/05/data-science-with-R-course-week-8.html#week8" target="_blank">Week 8: Link Data Science To Business With Expected Value</a>. Here’s our game-plan over the 10 articles in this series. We’ll cover how to apply <strong>data science for business with R</strong> following our systematic process.</p>
<ul>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html" target="_blank">Week 1: Getting Started</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html" target="_blank">Week 2: Business Understanding</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/10/01/data-science-with-R-course-week-3.html" target="_blank">Week 3: Data Understanding</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/10/10/data-science-with-R-course-week-4.html" target="_blank">Week 4: Data Preparation</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/10/15/data-science-with-R-course-week-5.html" target="_blank">Week 5: Predictive Modeling With H2O</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/10/22/data-science-with-R-course-week-6.html" target="_blank">Week 6: H2O Model Performance</a></li>
<li><a rel="nofollow" href="https://www.business-science.io/university/2018/10/29/data-science-with-R-course-week-7.html" target="_blank">Week 7: Machine Learning Interpretability With LIME</a></li>
<li><strong>Week 8: Link Data Science To Business With Expected Value</strong> (You’re here)</li>
<li>Week 9: Expected Value Optimization And Sensitivity Analysis</li>
<li>Week 10: Build A Recommendation Algorithm To Improve Decision Making</li>
</ul>
<p>
<img src="https://i2.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/system_week8.png?w=456" data-recalc-dims="1">
</p>
<p>Week 8: Link Data Science To Business With Expected Value</p>
<p></p>
<p>
<img src="https://i0.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/course_testimonial.png?w=456" data-recalc-dims="1">
</p>
<p>Student Feedback</p>
<hr>
<h2>Week 8: Link Data Science To Business With Expected Value</h2>
<h3>Overview &amp; Setup</h3>
<p>This overview will teach you how to quantify the business return on investment (ROI) using the Expected Value Framework.</p>
<p>
<img src="https://i0.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/expected_value_framework.png?w=456" data-recalc-dims="1">
</p>
<p>The Expected Value Framework is way to apply an expected value to a classification model – it connects a machine learning classification model to ROI for the business.</p>
<p>Learn how to combine:</p>
<ol>
<li>The threshold,</li>
<li>Knowledge of costs and benefits, and</li>
<li>The confusion matrix converted to expected error rates to account for the presence of false positives and false negatives.</li>
</ol>
<p>We can use this combination to calculate the business savings for implementing policy as a results of your data science work.</p>
<p></p>
<h3>Calculating Expected ROI: No Overtime Policy</h3>
<p>Over the past few weeks you have created a machine learning model to predict which employees are likely to leave. Through your analysis, you determined the number one cause of employee turnover is working overtime hours.</p>
<p>In this module, you will create a baseline calculation to determine how much the business will save if they completely remove overtime for all employees.</p>
<p>
<img src="https://i1.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/no_overtime.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Targeting By Threshold Primer</h3>
<p>Targeting by threshold will allow you to target employees above a certain level of turnover risk to <strong>pinpoint those who are most likely to leave</strong>.</p>
<p>Use the calculation to determine the expected value to <strong>find the optimal threshold that will maximize business savings</strong> for implementing the policy.</p>
<p>
<img src="https://i2.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/threshold.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Calculating Expected ROI: Targeted Overtime Policy</h3>
<p>Using the threshold from the previous module, learn how to apply the expected value to employees above a certain probability to leave.</p>
<p>You will also compare the <i>targeted overtime policy</i> to the previous <i>no overtime policy</i> to calculate the cost difference between each policy.</p>
<p>This will enable you to clearly communicate the savings for implementing overtime policy.</p>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/eOvw8UEiObs" width="100%" height="450"></iframe></p>
<p></p>
<p></p>
<p></p>
<hr>
<h2>You Need To Learn R For Business</h2>
<p><a rel="nofollow" href="https://university.business-science.io/p/hr201-using-machine-learning-h2o-lime-to-predict-employee-turnover/?product_id=635023&coupon_code=DS4B_15" target="_blank"><br>
<img alt="Data Science For Business With R Course" src="https://i2.wp.com/www.business-science.io/img/course_logo_full_DS4B_201_R.png?w=456" data-recalc-dims="1"><br>
</a></p>
<p><strong>To be efficient as a data scientist, you need to learn R.</strong> Take the course that has <strong>cut data science projects in half</strong> (see <a rel="nofollow" href="https://youtu.be/yw5CtGTzIw0" target="_blank">this testimonial</a> from a leading data science consultant) and has <strong>progressed data scientists more than anything they have tried before</strong>. Over 10-weeks you learn what it has taken data scientists 10-years to learn:</p>
<ul>
<li>Our systematic data science for business framework</li>
<li>R and H2O for Machine Learning</li>
<li>How to produce Return-On-Investment from data science</li>
<li>And much more.</li>
</ul>
<p>
<a rel="nofollow" href="https://university.business-science.io/p/hr201-using-machine-learning-h2o-lime-to-predict-employee-turnover/?product_id=635023&coupon_code=DS4B_15" target="_blank"><strong>Start Learning Today!</strong></a>
</p>
<p></p>
<hr>
<p></p>
<h2>Next Up</h2>
<p>The next article in the <strong><em>Data Science With R Series</em></strong> covers <strong>Expected Value Optimization And Sensitivity Analysis</strong>.</p>
<p>This is a really fun series of chapters that teach you the skills to align your data science work with business ROI.</p>
<p>This week’s targeted analysis leads into Week 9, where you will perform two advanced analyses that are critical in the course:</p>
<ul>
<li>
<p><strong>Threshold Optimization</strong> – A method used to maximize expected saving via iteratively calculating savings at various thresholds</p>
</li>
<li>
<p><strong>Sensitivity Analysis</strong> – A method used to investigate how sensitive the expected savings is to various parameter values that were created based on assumptions</p>
</li>
</ul>
<p>
<img src="https://i0.wp.com/www.business-science.io/assets/2018-11-05-data-science-with-R-8/system_week9.png?w=456" data-recalc-dims="1">
</p>
<p>Week 9: Expected Value Optimization And Sensitivity Analysis</p>
<p></p>
<hr>
<h2>New Course Coming Soon: Build A Shiny Web App!</h2>
<p>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 <strong><em>Interactive Web Dashboard</em></strong>?</p>
<p>In our new course, <strong><em>Build A Shiny Web App (DS4B 301-R)</em></strong>, 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!</p>
<p></p>
<p>Shiny Apps Course Coming in October 2018!!! Sign up for <a rel="nofollow" href="https://university.business-science.io/" target="_blank">Business Science University Now!</a></p>
<p></p>
<p><a rel="nofollow" href="https://university.business-science.io/" target="_blank"><br>
<img alt="DS4B 301-R Shiny Application: Employee Prediction" src="https://i0.wp.com/www.business-science.io/img/hr_301_app.png?w=456" data-recalc-dims="1"><br>
</a></p>
<p>Building an R + Shiny Web App, DS4B 301-R</p>
<p></p>
<p><a rel="nofollow" href="https://university.business-science.io/" target="_blank">Get Started Today!</a></p>
	<p>
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