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(This article was first published on <strong><a href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.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>Data Science and Machine Learning in business begins with R. Why? R is the premier language that enables <strong><em>rapid exploration, modeling, and communication</em></strong> in a way that no other programming language can match: <strong>SPEED!</strong> 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.</p>
<p>In this <strong>Data Science With R Course Series</strong>, we’ll cover what life is like in our ground-breaking, enterprise-grade course called <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">Data Science For Business With R (DS4B 201-R)</a>. 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 <strong>10 weeks</strong>.</p>
<p>In this article, we’ll cover <a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#week2" target="_blank"><strong>Week 2: Business Understanding</strong></a>, which is where we begin coding in R using exploratory techniques with the goal of sizing the business problem.</p>
<p>But, first, a quick recap of our trajectory and the course overview.</p>
<h2>Data Science With R Course Series</h2>
<p>You’re in the <a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#week2" target="_blank"><strong>Week 2: Business Understanding</strong></a>. Here’s our game-plan over the next 10 articles in this series. We’ll cover how to apply data science for business with R 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><strong>Week 2: Business Understanding</strong> (You’re Here)</li>
<li>Week 3: Data Understanding</li>
<li>Week 4: Data Preparation</li>
<li>Week 5: Predictive Modeling With H2O</li>
<li>Week 6: H2O Model Performance</li>
<li>Week 7: Machine Learning Interpretability With LIME</li>
<li>Week 8: Link Data Science To Business With Expected Value</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://i1.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/system_week2.png?w=456" data-recalc-dims="1">
</p>
<p>Week 2: Business Understanding</p>
<h2>Course and Problem Overview</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"><strong>Data Science For Business With R (DS4B 201-R)</strong></a> is a one-of-a-kind course designed to teach you the essential aspects for applying data science to a business problem with R.</p>
<p></p>
<p>We analyze a single problem: <strong><em>Employee Turnover</em></strong>, which is a <strong>$15M per year problem</strong> to an organization that loses 200 high performing employees per year. It’s designed to teach you techniques that can be applied to any binary classification (Yes/No) problem such as:</p>
<ul>
<li>
<p><strong>Predicting Employee Turnover</strong>: Will the employee leave?</p>
</li>
<li>
<p><strong>Predicting Customer Churn</strong>: Will the customer leave?</p>
</li>
<li>
<p><strong>Predicting Risk of Credit Default</strong>: Will the loan applicant or company default?</p>
</li>
</ul>
<p></p>
<p>Here’s why our students consistently give it a <strong>9 of 10 for satisfaction rating</strong>:</p>
<ul>
<li>
<p>It’s based on <strong>real-world experience</strong></p>
</li>
<li>
<p>You apply our <strong>systematic framework</strong> that cuts project times in half. <a rel="nofollow" href="https://youtu.be/yw5CtGTzIw0" target="_blank">Refer to this testimonial from our student.</a></p>
</li>
<li>
<p>We focus on <strong>return on investment (ROI)</strong></p>
</li>
<li>
<p>We cover <strong>high performance R packages</strong>: <code>H2O</code>, <code>LIME</code>, <code>tidyverse</code>, <code>recipes</code>, and more.</p>
</li>
<li>
<p><strong>You get results!</strong></p>
</li>
</ul>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/lyWxrhaBJ38" width="100%" height="450"></iframe></p>
<p>DS4B 201-R, Course Overview</p>
<p>Next, let’s experience what life is like in <strong>Week 2: Business Understanding</strong>.</p>
<h2>Week 2: Business Understanding</h2>
<p>Week 2 is where we begin our deep-dive into data science for business. In <strong><em>Business Understanding</em></strong>, we learn how to:</p>
<ul>
<li>
<p><a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#part1" target="_blank">Understand the problem using <code>R Code</code> and BSPF</a></p>
</li>
<li>
<p><a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#part2" target="_blank">Streamline repetitive employee attrition code using <code>Tidy Eval</code></a></p>
</li>
<li>
<p><a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#part3" target="_blank">Visualize employee turnover with <code>ggplot2</code></a></p>
</li>
<li>
<p><a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#part4" target="_blank">Make our first custom plotting function, <code>plot_attrition()</code></a></p>
</li>
<li>
<p><a rel="nofollow" href="http://www.business-science.io/university/2018/09/24/data-science-with-R-course-week-2.html#part5" target="_blank">Introduce Challenge #1</a></p>
</li>
</ul>
<p>The first thing you’ll do is log into <a rel="nofollow" href="https://university.business-science.io/" target="_blank">Business Science University</a>, and move to the <strong>Week 2 Module</strong>, which looks like this.</p>
<p><img alt="DS4B 201-R Week 2 Module" src="https://i2.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/app_week_2.png?w=456" data-recalc-dims="1"></p>
<p>Week 2: Business Understanding Module, DS4B 201-R Course</p>
<p>We’ll begin by analyzing the problem in R in the section titled, Problem Understanding with the BSPF.</p>
<h3>Understand the problem using <code>R Code</code> and BSPF</h3>
<p>Sizing the business opportunity or cost is OVERLOOKED by most data scientists. If the cost / benefit to the organization is not large, it’s not worth your time. We need to be efficient, which is our second focus. ROI is first, efficiency is second.</p>
<blockquote>
<p><strong>If the cost / benefit to the organization is not large, it’s not worth your time.</strong></p>
</blockquote>
<p>To size the problem, we lean on a tool we learned about in <a rel="nofollow" href="https://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html" target="_blank">Week 1</a>: <strong><em>The Business Science Problem Framework (BSPF)</em></strong>. Specifically, you’ll learn to:</p>
<ul>
<li>View the business as a machine</li>
<li>Understand the drivers</li>
<li>Measure the drivers</li>
</ul>
<p><img alt="Business Science Problem Framework (BSPF)" src="https://i2.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/app_bspf.png?w=456" data-recalc-dims="1"></p>
<p>Walking Through The Business Science Problem Framework (BSPF)</p>
<p>As we walk through the <strong><em>BSPF</em></strong>, we focus our efforts on identifying (1) if the organization has a problem and (2) how large that problem is. We investigate:</p>
<ul>
<li>
<p>How many high performance employees are turning over</p>
</li>
<li>
<p>What the true cost of their turnover is, converting the Excel calculation to a scalable R calculation</p>
</li>
<li>
<p>Key Performance Indicators (KPIs) for turnover</p>
</li>
<li>
<p>Potential drivers including common cohorts: Job Department and Job Role</p>
</li>
</ul>
<p>Here’s a sample lecture showing what the code experience is like: <a rel="nofollow" href="https://youtu.be/TSvWM4c5tdQ" target="_blank">“View the Business as a Machine”</a>.</p>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/TSvWM4c5tdQ" width="100%" height="450"></iframe></p>
<p>View the Business As A Machine Lecture</p>
<p></p>
<p>As we go through the process of understanding and sizing the business problem, we realize that we are performing the same calculations repetitively. Any time <strong>repetitious code happens</strong>, we should create a function. Next, we’ll learn about a powerful new set of tools for building <strong><em>tidy-functions</em></strong> that reduces and simplifies repetitive code: <code>Tidy Eval</code>.</p>
<h3>Streamline repetitive employee attrition code using <code>Tidy Eval</code></h3>
<p>To this point you’ve sized the problem and even determined that the problem is larger within certain cohorts within the organization. Through this exploratory process, you’ve <strong><em>repeated the same code multiple times</em></strong>. Now it’s time to <strong><em>streamline this code workflow</em></strong> with a powerful set of tools called <code>Tidy Eval</code>.</p>
<p><img alt="Tidy Eval" src="https://i1.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/tidy_eval.png?w=456" data-recalc-dims="1"></p>
<p>Learning <code>Tidy Eval</code> To Simplify Code Steps Repeated Frequently</p>
<p>You will use or create several functions that implement <code>Tidy Eval</code> and <code>rlang</code> including:</p>
<ul>
<li><code>count</code>: Summarizes the counts of grouped columns. Implemented in <code>dplyr</code></li>
<li><code>count_to_pct</code>: Converts counts to percentages (proportions). You create.</li>
<li><code>assess_attrition()</code>: Filters, arranges, and compares attrition rates to KPIs. You create.</li>
</ul>
<p>Armed with this streamlined code workflow, it’s now time to visualize the problem using the <code>ggplot2</code> library.</p>
<h3>Visualize employee turnover with <code>ggplot2</code></h3>
<p>The best way to grab an executive decision maker’s attention is to show him or her a <strong>business-themed plot that conveys the problem</strong>. In this section, we cover exactly how to do so using the <code>ggplot2</code> package.</p>
<p><img alt="ggplot2" src="https://i1.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/ggplot.png?w=456" data-recalc-dims="1"></p>
<p>Using <code>ggplot2</code> to create an impactful visualization of the problem</p>
<p>Next, you learn how to create a plotting function that can flexibly handle various grouped data within your code workflow.</p>
<h3>Make our first custom plotting function, <code>plot_attrition()</code></h3>
<p>Once again, we’re repetitively reusing code to plot different variations of the same information. In this section, we teach you how to create a custom plotting function called <code>plot_attrition()</code> that flexibly handles grouped features including the employee’s Department and Job Role.</p>
<p><img alt="ggplot2" src="https://i2.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/plot_attrition.png?w=456" data-recalc-dims="1"></p>
<p>Create a flexible plotting function, <code>plot_attrition()</code></p>
<p>By now, you have a serious set of <code>dplyr</code> and <code>ggplot2</code> investigative skills. Next, we put them to use with your first challenge!</p>
<h3>Challenge #1</h3>
<p>Your first challenge is something that happens in the real world – your Subject Matter Experts (SMEs) – in this case the Accounting and Human Resources department provided you new data at a more granular level, which will make your analysis more accurate. Your job is to integrate the new information into you analysis. Are you up to the challenge?</p>
<p><img alt="DS4B 201-R: Challenge #1" src="https://i1.wp.com/www.business-science.io/assets/2018-09-24-data-science-with-R-2/challenge1.png?w=456" data-recalc-dims="1"></p>
<p>Now It’s You’re Turn To Apply Your Knowledge!</p>
<p>At the end of the module, the challenge solution is provided for the learners along with the full code used in the course.</p>
<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>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><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><a rel="nofollow" href="https://university.business-science.io/" target="_blank">Get Started Today!</a></p>
	<p>
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