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(This article was first published on <strong><a href="http://www.business-science.io/university/2018/11/12/data-science-with-R-course-week-9.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>There are only two more weeks in the course! This week will extend what you learned from the Expected Value by performing an optimization and sensitivity analysis.</p>
<p>The <strong>optimization and sensitivity analysis</strong> will teach you how to identify the maximum business savings for the overtime problem and see how additional factors change the amount of savings:</p>
<ul>
<li>
<p>Threshold Optimization – A method used to maximize expected saving via iteratively calculating savings at various thresholds</p>
</li>
<li>
<p>Sensitivity Analysis – 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>Get ready, this week is packed full of learning!</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/2018/11/12/data-science-with-R-course-week-9.html#week9" target="_blank">Week 9: Expected Value Optimization And Sensitivity Analysis</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><a rel="nofollow" href="https://www.business-science.io/university/2018/11/05/data-science-with-R-course-week-8.html" target="_blank">Week 8: Link Data Science To Business With Expected Value</a></li>
<li><strong>Week 9: Expected Value Optimization And Sensitivity Analysis</strong> (You’re here)</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/2018-11-12-data-science-with-R-9/system_week9.png?w=456" data-recalc-dims="1">
</p>
<p>Week 9: Expected Value Optimization And Sensitivity Analysis</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 9: Expected Value Optimization And Sensitivity Analysis</h2>
<h3>Threshold Optimization: Maximizing Expected ROI</h3>
<p>Last week you learned how to increase business savings by targeting employee overtime. In this module, you will use the R package <code>purrr</code> to determine the maximum savings for the overtime policy.</p>
<p>
<img src="https://i1.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/threshold_optimization.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Threshold Optimization: Visualizing The Expected Savings At Various Threshold</h3>
<p>Create a plot using the threshold optimization to visualize the optimization results (business savings). This is a useful way to compare the optimization analysis to the employee churn business case and see how the threshold optimization produces business savings.</p>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/M9WBfOGiVKE" width="100%" height="450"></iframe></p>
<p></p>
<h3>Sensitivity Analysis: Adjusting Parameters To Test Assumptions</h3>
<p>From the previous module, you determined that employees with a certain percentage of overtime (threshold) should be targeted to help reduce employee churn. However, this is based on a static employee salary.</p>
<p>Now you will learn how business savings can change based on different employee salaries (sensitivity).</p>
<p>
<img src="https://i1.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/sensitivity_analysis_1.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Sensitivity Analysis: Visualizing The Effect Of Scenarios &amp; Breakeven</h3>
<p>In this module, you will analyze the threshold analysis with the addition of another variable, employee salary. Create a profitability heatmap to visualize how business savings changes based on employee revenue amounts.</p>
<p>
<img src="https://i2.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/sensitivity_analysis_2.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Challenge #5: Threshold Optimization For Stock Options</h3>
<p>Your overtime analysis is complete, but now you see that people with no stock options are leaving at a faster rate than people with stock options.</p>
<p>In this two-part challenge, you implement the same threshold optimization and sensitivity analysis, but this time for employee stock option.</p>
<p>
<img src="https://i0.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/challenge5.png?w=456" data-recalc-dims="1">
</p>
<p></p>
<h3>Challenge #6: Sensitivity Analysis For Stock Options</h3>
<p>Continuing the challenge, perform a sensitivity analysis for the stock option threshold, and adjust by stock option price.</p>
<p>Once you complete your solution, compare it with the instructor’s solution in the challenge solution videos.</p>
<p>
<img src="https://i1.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/challenge6.png?w=456" data-recalc-dims="1">
</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>Build A Recommendation Algorithm To Improve Decision Making</strong>.</p>
<p>In the final week, you will implement a 3-Step Process for creating a recommendation algorithm. The learning modules are designed to integrate critical thinking and strategy development with data-driven decision making.</p>
<p>As an added bonus, you’ll see a sneak preview of the Shiny Web App built in DS4B 301-R. The Recommendation Algorithm developed here plays a major role in that course.<br>
sensitive the expected savings is to various parameter values that were created based on assumptions</p>
<p>
<img src="https://i2.wp.com/www.business-science.io/assets/2018-11-12-data-science-with-R-9/system_week10.png?w=456" data-recalc-dims="1">
</p>
<p>Week 10: Build A Recommendation Algorithm To Improve Decision Making</p>
<p></p>
<hr>
<h2>Our Revolutionary System For Learning R For Business</h2>
<p><a rel="nofollow" href="https://university.business-science.io/" target="_blank"><img alt="R Cheatsheet" src="https://i2.wp.com/www.business-science.io/img/course_logo_full_DS4B_101_R.png?w=456" data-recalc-dims="1"></a></p>
<p><strong>Are you interested in learning R For Business? Then look no further.</strong></p>
<ul>
<li>
<p><a rel="nofollow" href="https://university.business-science.io/" target="_blank">Business Science University</a> has the most advanced, technology intensive, and streamlined data science educational system for business on the planet.</p>
</li>
<li>
<p>We are developing a <strong>NEW INTRODUCTORY COURSE (DS4B 101-R)</strong> that delivers an amazing educational experience for learners that want to apply R to business analysis.</p>
</li>
<li>
<p>The <strong>beginner DS4B 101-R</strong> is the prequel to the <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>intermediate/advanced DS4B 201-R course</strong></a></p>
</li>
</ul>
<h3>Course Launch Date</h3>
<p><strong>Launch is expected at end of November 2018 / Beginning of December 2018. Sign up at <a rel="nofollow" href="https://university.business-science.io/" target="_blank">Business Science University</a> to get updates.</strong></p>
<h3>Course Details</h3>
<p><a rel="nofollow" href="https://university.business-science.io/" target="_blank"><img alt="R Cheatsheet" src="https://i2.wp.com/www.business-science.io/img/course_logo_full_DS4B_101_R.png?w=456" data-recalc-dims="1"></a></p>
<p>Our <strong>NEW BUSINESS ANALYSIS WITH R COURSE (DS4B 101-R)</strong> combines:</p>
<ul>
<li>
<p>Teaching the <a rel="nofollow" href="http://www.business-science.io/r-cheatsheet.html" target="_blank">Data Science with R Workflow</a> in a <strong>100% business context</strong>: data import, data manipulation (business aggregations, time-based calculations, text manipulation, categorical manipulation, and missing data), data visualization, business reporting with <code>RMarkdown</code>, and advanced analysis including scaling analysis and building functions.</p>
</li>
<li>
<p>Two state-of-the-art business projects.</p>
</li>
</ul>
<p><img alt="DS4B 101-R Course - Project 1" src="https://i0.wp.com/www.business-science.io/assets/2018-11-04-data-science-with-R-cheatsheet/DS4B_101_R_project1.png?w=456" data-recalc-dims="1"></p>
<p>Project 1 – Exploratory Analysis of Digital Media Merchant</p>
<p></p>
<ul>
<li>
<p><strong>Business Project 1</strong>: Complete a full exploratory analysis on a simulated digital media merchant:</p>
<ul>
<li>Connect to a <code>sqlite</code> database to retrieve transactional data,</li>
<li>Join data from 11 database tables including customers, products, and time-based transactional history</li>
<li>Cleaning data using <code>dplyr</code> and <code>tidyr</code></li>
<li>Business Analysis: Product Level Analysis, Customer Level Analysis, Time-Based Analysis, and Business Filtering to Isolate Key Populations</li>
</ul>
</li>
<li>
<p><strong>Business Project 2</strong>: Apply the advanced skills through a <strong><em>Customer Segmentation project</em></strong></p>
<ul>
<li>Use K-Means Clustering to anlayze customer purchasing habits and group into segments</li>
<li>Apply data visualization techniques investigate the key buying habits</li>
<li>Bonus Material and More!</li>
</ul>
</li>
</ul>
	<p>
<div>
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