<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/data-science-with-r-series-week-1/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.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 best friend.</p>
<p>In this <strong><em>Data Science With R Series</em></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>We want you to experience what our students are saying is the best online course they have ever taken!</p>
<h2>Data Science With R Series</h2>
<p>You’re in <strong><em>Week 1: Getting Started</em></strong>, and 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>Week 1: Getting Started</li>
<li>Week 2: Business Understanding</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/2018-09-20-data-science-with-R-1/system_week1.png?w=456" data-recalc-dims="1">
</p>
<p>Week 1: Getting Started</p>
<h2>About The Course</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">Data Science For Business With R (DS4B 201-R)</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. Here’s why it’s unique and why <strong>our students consistently rate it 9 of 10 for satisfaction</strong>:</p>
<ul>
<li>
<p>The course is based on <strong>real-world experience</strong> applying data science as a consultant and within organizations</p>
</li>
<li>
<p>We provide our <strong>systematic framework</strong> for solving business problems with data science. This 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 the <strong>pursuit of return on investment (ROI) with data science</strong>, the single most important metric that organizations care about.</p>
</li>
<li>
<p>We cover <strong>high performance R packages</strong> including H2O, LIME, tidyverse, recipes, and more.</p>
</li>
</ul>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/lyWxrhaBJ38" width="100%" height="450"></iframe></p>
<p></p>
<p>DS4B 201-R, Course Trailer</p>
<h2>Week 1: Getting Started</h2>
<p>Week 1 is designed to get you the necessary knowledge before you jump into coding so you become a ginsu knife able to slice-and-dice problems. As such, week 1 is almost entirely designed to provide you information on the:</p>
<ul>
<li><a rel="nofollow" href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html#part1" target="_blank">Business problem</a></li>
<li><a rel="nofollow" href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html#part2" target="_blank">Tools in our toolbox</a></li>
<li><a rel="nofollow" href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html#part3" target="_blank">Frameworks: Systems for success</a></li>
<li><a rel="nofollow" href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html#part4" target="_blank">Data science project setup</a></li>
</ul>
<p>The first thing you’ll do is log into 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">DS4B 201-R Course</a> through <a rel="nofollow" href="https://university.business-science.io/" target="_blank">Business Science University</a>. The application looks like this.</p>
<p><img alt="Inside The Course Application" src="https://i0.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/course_application.png?w=456" data-recalc-dims="1"></p>
<p>Inside The Course Application</p>
<p>After you check out the awesome introductory content including the <strong>Private Slack Channel</strong> and the <strong>BONUS: Market Basket Analysis and Product Recommender Algorithm Tutorial</strong>, it’s time to move into the Week 1 materials.</p>
<p><img alt="DS4B 201-R Bonuses!" src="https://i0.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/bonuses.png?w=456" data-recalc-dims="1"></p>
<p>Bonus Tutorial and Private Slack Community!</p>
<h3>Business Problem</h3>
<p>We’re focused on a <strong>$15 MILLION DOLLAR PROBLEM: EMPLOYEE TURNOVER</strong>. This is a problem that affects thousands of organizations, and it’s extremely costly. Worse, it almost always goes unnoticed because it’s indirect. Employee turnover (attrition) affects productivity, which is not an accounting expense. We teach you:</p>
<ul>
<li>
<p>What happens when employees leave an organization, and how it affects productivity</p>
</li>
<li>
<p>How to calculate the <strong>TRUE COST OF ATTRITION</strong></p>
</li>
<li>
<p>How to use an Excel Calculator to quickly communicate estimated costs to executives</p>
</li>
</ul>
<p>You’ll learn from an expert on the business problem via self-paced lectures.</p>
<p><img alt="Understanding The Business Problem" src="https://i0.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/business_problem.png?w=456" data-recalc-dims="1"></p>
<p>Understanding the Business Problem</p>
<p>You’ll get the Excel Calculator to expose others in your organization to the cost of employee attrition.</p>
<p><img alt="Excel Calculator" src="https://i0.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/excel_calculator.png?w=456" data-recalc-dims="1"></p>
<p>Excel Turnover Cost Calculator</p>
<p>Once you fully understand the problem, it’s time to investigate tools that will empower us in our pursuit of data science ROI!</p>
<h3>Tools In Our Toolbox</h3>
<p>Now it’s time to learn about two groups of amazing tools that we have at our disposal to solve the problem:</p>
<ul>
<li>
<p><strong>Integrated Data Science Frameworks</strong>: The Business Science Problem Framework and the CRISP-DM, which are integrated.</p>
</li>
<li>
<p><strong>H2O and LIME</strong>: Machine Learning packages that enable both high performance predictive analytics and high explainability of machine learning models. Perfect for business problems!</p>
</li>
</ul>
<p><img alt="H2O and LIME" src="https://i2.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/h2o_lime.png?w=456" data-recalc-dims="1"></p>
<p>H2O and LIME: Tools In Our Toolbox</p>
<p>Once you get a sense for what tools are coming, it’s now time to learn about the first set of tools – the integrated data science frameworks.</p>
<h3>Frameworks: Systems For Success</h3>
<p>Frameworks are essential in business. Benefits of having frameworks:</p>
<ul>
<li>Instill confidence from management</li>
<li>Communicate a clear plan</li>
<li>Keep everyone on task</li>
<li>Answer the question: “Why is it impossible to perform a data science project in 1 week?”</li>
</ul>
<p>You’ll learn about two integrated data science frameworks that are built upon our experience and industry-leading performance:</p>
<ol>
<li>
<p><a rel="nofollow" href="https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining" target="_blank">CRISP-DM: Cross Industry Standard Procedure for Data Mining</a></p>
</li>
<li>
<p><a rel="nofollow" href="https://www.business-science.io/bspf.html" target="_blank">BSPF: Business Science Problem Framework</a></p>
</li>
</ol>
<p>These two frameworks create the foundation for every step in the subsequent weeks. <strong>We follow the BSPF + CRISP-DM every step of the way as you solve this data science project!</strong></p>
<p><img alt="Business Science Problem Framework" src="https://i2.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/bspf_computer.png?w=456" data-recalc-dims="1"></p>
<p>Follow The BSPF Process Step-By-Step Throughout The Project</p>
<p>Now that you have a general idea of how the frameworks work, we’re almost ready to get started. The last step is setting up the data science project.</p>
<h3>Data Science Project Setup</h3>
<p>We’ll go through the process of setting up your data science project by:</p>
<ul>
<li>
<p>Creating an R Project in the RStudio IDE (Integrated Development Environment)</p>
</li>
<li>
<p>Programmatically creating the directory structure with the <code>fs</code> package</p>
</li>
<li>
<p>Install the required R Packages (see video sample below)</p>
</li>
<li>
<p>Download the data for modeling turnover</p>
</li>
</ul>
<p>Check out the <a rel="nofollow" href="https://youtu.be/UAT3VeILS6g" target="_blank">“Installing Required Packages”</a> sample lecture that explains the R packages you’ll be using throughout the course!</p>
<p></p>
<p><iframe allowfullscreen src="https://www.youtube.com/embed/UAT3VeILS6g" width="100%" height="450"></iframe></p>
<p></p>
<p>Sample Lecture, Installing Required Packages, Week 1</p>
<h2>Start Learning Today</h2>
<p>You’ve just experienced Week 1 in the course that is changing the way data science is done in business. Now it’s time to get started.</p>
<p><strong>What are you waiting for?</strong></p>
<p></p>
<hr>
<h2>Data Science For Business With R (DS4B 201-R)</h2>
<p>
Learn everything you need to know to complete a real-world, end-to-end data science project with the R programming language. Transform your abilities in 10 weeks.
</p>
<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>
<hr>
<p></p>
<h2>Coming Next</h2>
<p>In the next post in the <strong><em>Data Science With R Series</em></strong> we’ll head to Week 2 where we cover Business Understanding. This begins our transition into code for Weeks 2 to 10!</p>
<p>
<img src="https://i1.wp.com/www.business-science.io/assets/2018-09-20-data-science-with-R-1/system_week2.png?w=456" data-recalc-dims="1">
</p>
<p>Week 2: Business Understanding, Coming Next</p>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.business-science.io/university/2018/09/20/data-science-with-R-week-1.html"> business-science.io - Articles</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/U16YA-lXzC4" height="1"></a></div>