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?
R Essential Training Part 2: Modeling Data Platform: LinkedIn Learning Instructor: (Assuming a senior data scientist or statistician) Level: Intermediate Course Duration: 4 hours 12 minutes Released: Updated for R 4.3 / 4.4 Course Overview Welcome to R Essential Training Part 2: Modeling Data , 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 risk of churning within 30 days. Then, provide a short memo explaining which three features most strongly predict churn and a recommended intervention.