<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/r-for-business-analysts-nyc-data-science-academy-in-person-training-november-6-nyc/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://blog.nycdatascience.com/r/r-business-analysts-nyc-data-science-academy-person-training-november-6-nyc/"> R – NYC Data Science Academy Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img sizes="(max-width: 1024px) 100vw, 1024px" src="https://i0.wp.com/blog.nycdatascience.com/wp-content/uploads/2017/10/Brennan-Lodge-R-Business-Analysts-1024x545.png?w=450" alt="" class="wp-post-image" data-recalc-dims="1">
<p>R is a powerful language used widely for business analytics. More companies are seeking business analysts with knowledge of R.</p>
<p>In this <a rel="nofollow" href="http://nycdatascience.com/courses/r-business-analysts/?utm_campaign=R%20for%20Business%20Analysts&utm_source=Rblogger&utm_medium=Blog" target="_blank">R for Business Analysts</a> course, students will learn how data science is done in the wild, with a focus on data acquisition, cleaning, and aggregation, exploratory data analysis and visualization, feature engineering, and model creation and validation. Students use the R statistical programming language to work through real-world examples that illustrate these concepts. Concurrently, students learn some of the statistical and mathematical foundations that power the data-scientific approach to problem solving.</p>
<p>Classes will be given in a lab setting, with student exercises mixed with lectures. Students should bring a laptop to class. There will be a modest amount of homework after each class. Due to the focused nature of this course, there will be no individual class projects but the instructors will be available to help students who are applying R to their own work outside of class.</p>
<p>Designed and taught by <strong><a rel="nofollow" data-hs-link-id="0" href="https://www.linkedin.com/in/brennanlodge/?utm_campaign=R%20for%20Business%20Analysts&utm_source=hs_email&utm_medium=email&_hsenc=p2ANqtz--iVUs5jo75TsduyWqgTYR69uNaaYPFCJ7U7h7RiBE7yVcVvEz1GtuGWc4SpFNXRtqtZaG2" target="_blank">Brennan Lodge</a>, Team Lead at Bloomberg. </strong>Watch his interview here.</p>
<p><a rel="nofollow" href="http://nycdatascience.com/courses/r-business-analysts/?utm_campaign=R%20for%20Business%20Analysts&utm_source=Rblogger&utm_medium=Blog" target="_blank">Learn More and Sign Up</a></p>
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<h2>Details</h2>
</div>
<ul>
<li><span>Dates: November 6th – December 11th, 2017 </span></li>
<li><span>Time: </span><span>Mondays &amp; Wednesdays | 7:00 PM to 9:30 PM EST</span></li>
<li><span> Venue: </span><span><a rel="nofollow" href="https://www.google.com/maps/place/500+8th+Ave,+New+York,+NY+10018/@40.7534085,-73.9924743,17z/data=!4m2!3m1!1s0x89c259adb0d39f21:0xffe47802d5e71e43" title="500 8th Ave., Suite 905, New York, NY 10018" target="_blank">500 8th Ave., Suite 905, New York, NY 10018</a> (5 min from Penn Station)</span></li>
</ul>
<h2>Who Is This Course For?</h2>
<p>This course is for anyone with a basic understanding of data analysis techniques and business analysts interested in improving their ability to tackle problems involving multi-dimensional data in a systematic, principled way. A familiarity with the R programming language is helpful, but unnecessary if the pre-work for the course is completed (more on that below).</p>
<div>
<h2>Prerequisites</h2>
</div>
<div>Students should have some experience with programming and have some familiarity with basic statistical and linear algebraic concepts such as mean, median, mode, standard deviation, correlation, and the difference between a vector and a matrix. In R, it will be helpful to know basic data structures such as data frames and how to use R Studio.Students should complete the following pre-work (approximately 2 hours) before the first day of class:</div>
<div>
<ul>
<li>R Programming – https://www.rstudio.com/online-learning/#R</li>
<li>R Studio Essentials Programming 1: Writing Code https://www.rstudio.com/resources/webinars/rstudio-essentials-webinar-series-part-1/</li>
</ul>
</div>
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<h2>Outcomes</h2>
</div>
<p>Upon completing the course, students have:</p>
<ul>
<li>An understanding of data science business problems solvable using R and an ability to articulate those business use cases from a statistical perspective.</li>
<li>The ability to create data visualization output with Rmarkdown files and Shiny Applications.</li>
<li>Familiarity with the R data science ecosystem, strategizing and the various tools a business analyst can use to continue developing as a data scientist.</li>
</ul>
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<h2>Syllabus</h2>
</div>
<p><strong>Unit 1: Data Science and R Intro</strong></p>
<ul>
<li>Big Data</li>
<li>Data Science</li>
<li>Roles in Data Science</li>
<li>Use Cases</li>
<li>Data’isms</li>
<li>Class Format overview</li>
<li>R Background</li>
<li>R Intro</li>
<li>R Studio</li>
</ul>
<p><strong>Unit 2: Visualize</strong></p>
<ul>
<li>Rules of the road with data viz</li>
<li>Chart junk</li>
<li>Chart terminology</li>
<li>Clean chart</li>
<li>Scaling data</li>
<li>Data Viz framework</li>
<li>Code plotting</li>
</ul>
<p><strong>Unit 3: R Markdown</strong></p>
<ul>
<li>Presenting your work</li>
<li>R markdown file structure</li>
<li>Code chunks</li>
<li>Generating a R markdown file</li>
<li>Rmarkdown Exercise</li>
</ul>
<p><strong>Unit 4: Shiny</strong></p>
<ul>
<li>Shiny structure</li>
<li>Reactive output</li>
<li>Widgets</li>
<li>Rendering Output</li>
<li>Stock example</li>
<li>Hands-on challenge</li>
</ul>
<p><strong>Unit 5: Data Analysis</strong></p>
<ul>
<li>How to begin your data journey?</li>
<li>The human factor</li>
<li>Business Understanding</li>
<li>Dplyr</li>
<li>EDA – Exploratory Data Analysis</li>
<li>Data Anomalies</li>
<li>Data Statistics</li>
<li>Key Business Analysis Takeaways</li>
<li>Diamond data set exercise</li>
<li>Hands on challenge with Bank Marketing</li>
</ul>
<p><strong>Unit 6: Introduction to Regression</strong></p>
<ul>
<li>Regression Definition</li>
<li>Examples of regression</li>
<li>Formulize the formula</li>
<li>Plotting</li>
<li>Statistical definitions involved</li>
<li>mtcars regression example</li>
<li>Business use case with regression</li>
</ul>
<p><strong>Unit 7: Introduction to Machine Learning</strong></p>
<ul>
<li>ML Concept</li>
<li>Types of ML</li>
<li>CRISP Model</li>
<li>Modeling</li>
<li>Evaluation</li>
<li>Titanic Example</li>
<li>Decision Trees</li>
<li>Feature Engineering</li>
</ul>
<p><strong>Unit 8: Strategy</strong></p>
<ul>
<li>Data Driven Decision Making</li>
<li>Data Science Strategy</li>
<li>Strategy Fails</li>
<li>Macroeconomic strategy</li>
<li>Adapting</li>
<li>Data Science Project</li>
<li>Data Impact</li>
<li>Project guide</li>
<li>Opportunities for improvement</li>
<li>Big Box Store Strategic Exercise</li>
</ul>
<p>Seats are filling up fast! <a rel="nofollow" href="http://nycdatascience.com/courses/r-business-analysts/?utm_campaign=R%20for%20Business%20Analysts&utm_source=Rblogger&utm_medium=Blog" target="_blank">Sign up now</a>.</p>
<p>If you have any questions about our course or the application process, please do not hesitate to reach out to us via <a rel="nofollow" href="mailto:admissions@nycdatascience.com" target="_blank">email.</a></p>
<p>The post <a rel="nofollow" href="http://blog.nycdatascience.com/r/r-business-analysts-nyc-data-science-academy-person-training-november-6-nyc/" target="_blank">R for Business Analysts, NYC Data Science Academy in-person training | November 6 @NYC</a> appeared first on <a rel="nofollow" href="http://blog.nycdatascience.com/" target="_blank">NYC Data Science Academy Blog</a>.</p>
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