Linkedin R Essential Training Part 2: Modeling Data Site

Build a predictive model to identify users at risk of churning within 30 days. Then, provide a short memo explaining which three features most strongly predict churn and a recommended intervention.

Data modeling is not merely about applying functions; it is the bridge between descriptive statistics and predictive inference. In this course, you will move beyond summary() and ggplot() to answer the most critical business questions: What drives customer churn? Can we forecast next quarter’s revenue? Which variables actually matter?

is a popular intermediate-level course on LinkedIn Learning taught by data scientist Barton Poulson. The course focuses on using the R programming language to move beyond basic data visualization and into advanced statistical modeling and predictive analytics. Course Overview and Structure linkedin r essential training part 2: modeling data

: Identifying relationships between variables to see which factors move together.

Welcome to , the second installment in LinkedIn’s comprehensive R programming series. If Part 1 introduced you to the grammar of R—vectors, data frames, and the Tidyverse—Part 2 is where you learn to make R think . Build a predictive model to identify users at

. The most interesting part for me was learning how to handle categorical predictors—it really opens up how you can look at demographic or segmented data. Huge thanks to [Instructor Name, if applicable] for the clear walkthroughs. If anyone in my network is looking to chat about #RStats or data modeling best practices, I’d love to connect! #ProfessionalDevelopment #Coding #RProgramming #Statistics Pro-tip for your post: Tag the instructor or the platform (e.g., LinkedIn Learning) to increase visibility. Attach your certificate or a screenshot of a cool visualization you made during the course to grab more attention in the feed. Should I help you

A significant portion of the R Essential Training Part 2: Modeling Data focuses on classical statistical tests to determine if observed differences are significant. In this course, you will move beyond summary()

By mastering these techniques, you move from being a data reporter to a data architect. Whether you are following the R Essential Training Part 2 Modeling Data path on social media or completing the full curriculum, these skills are critical for modern analytics roles.

This training is designed for professionals who have mastered the basics of R (covered in Part 1) and want to generate deeper insights through statistical testing and machine learning. The curriculum is divided into several logical modules that guide learners through the entire modeling lifecycle, from initial data exploration to complex ensemble modeling. Key Learning Modules

: Using techniques like Principal Component Analysis (PCA) to simplify large datasets into their most critical components. 2. Statistical Testing and Mean Comparisons

Before building complex models, learners use R to compute frequencies, descriptive statistics, and correlations . It also covers advanced techniques like Principal Component Analysis (PCA) and confirmatory factor analysis to understand data structures.