<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/new-course-marketing-analytics-in-r/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="https://www.datacamp.com/community/blog/marketing-analytics-r"> DataCamp Community - r programming</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530048419/marketing_analyticsR_y6havr.png?w=456" data-recalc-dims="1"></a></center></p>
<h3>Course Description</h3>
<p><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Here is a link to our new R course.</a></p>
<p>This is your chance to dive into the worlds of marketing and business analytics using R. Day by day, there are a multitude of decisions that companies have to face. With the help of statistical models, you’re going to be able to support the business decision-making process based on data, not your gut feeling. Let us show you what a great impact statistical modeling can have on the performance of businesses. You’re going to learn about and apply strategies to communicate your results and help them make a difference.</p>
<h3>Chapter 1: Modeling Customer Lifetime Value with Linear Regression</h3>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i1.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530044922/Screen_Shot_2018-06-26_at_4.25.08_PM_aqewf6.png?w=456" data-recalc-dims="1"></a></center></p>
<p>How can you decide which customers are most valuable for your business? Learn how to model the customer lifetime value using linear regression.</p>
<h3>Chapter 2: Logistic Regression for Churn Prevention</h3>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530044922/Screen_Shot_2018-06-26_at_4.26.05_PM_ajfwfl.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Predicting if a customer will leave your business, or churn, is important for targeting valuable customers and retaining those who are at risk. Learn how to model customer churn using logistic regression.</p>
<h3>Chapter 3: Modeling Time to Reorder with Survival Analysis</h3>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530044922/Screen_Shot_2018-06-26_at_4.27.13_PM_h04ise.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Learn how to model the time to an event using survival analysis. This could be the time until next order or until a person churns.</p>
<h3>Chapter 4: Reducing Dimensionality with Principal Component Analysis</h3>
<p><center><a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank"><img src="https://i0.wp.com/res.cloudinary.com/dyd911kmh/image/upload/f_auto,q_auto:best/v1530044922/Screen_Shot_2018-06-26_at_4.28.08_PM_rqxojo.png?w=456" data-recalc-dims="1"></a></center></p>
<p>Learn how to reduce the number of variables in your data using principal component analysis. Not only does this help to get a better understanding of your data. PCA also enables you to condense information to single indices and to solve multicollinearity problems in a regression analysis with many intercorrelated variables.</p>
<p>If you are interested in learning more about marketing and data science, check out this tutorial for Python, <a rel="nofollow" href="https://www.datacamp.com/community/tutorials/sem-data-science?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Data Science for Search Engine Marketing</a>.</p>
<p>The following R courses are prerequisites to take <a rel="nofollow" href="https://www.datacamp.com/courses/marketing-analytics-in-r-statistical-modeling?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Marketing Analytics in R: Statistical Modeling</a>.</p>
<ul>
<li><a rel="nofollow" href="https://www.datacamp.com/courses/introduction-to-the-tidyverse?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Intro to the Tidyverse</a></li>
<li><a rel="nofollow" href="https://www.datacamp.com/courses/correlation-and-regression?tap_a=5644-dce66f&tap_s=10907-287229" target="_blank">Correlation and Regression</a></li>
</ul>
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<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="https://www.datacamp.com/community/blog/marketing-analytics-r"> DataCamp Community - r programming</a></strong>.</div>
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