1. Congratulations!
You've made it through the course! You've learned how to use some key tools from the tidyverse family of R packages to solve real business problems in the HR space. You can use graphs built with ggplot2 to explore the HR data and share your results with others, and you can use statistical tests to make sure the differences you find are meaningful.
2. HR analytics makes a difference
Most importantly, I hope you've come away with some ideas about how analytics can be used in many parts of HR, and an appreciation for the differences between HR data and other datasets. With HR data, you're analyzing employees -- real people who may spend at least a third of their day at work. Their job, how much they are paid, whether they find joy in what they do, and whether they feel included at work all impact their quality of life and their identity. What we do in HR analytics can make a real difference in people's lives.
3. Just scratching the surface
There's so much more to HR analytics than what I've covered in this course. Predictive modeling can help HR get in front of employee turnover before it happens, or choose better candidates to hire. Text analysis and natural language processing can enable HR teams to find out what their employees are happy or concerned about in real time. Organizational network analysis can tell organizations how work really gets done, and which employees may be unsung heroes. You can learn about these techniques in other DataCamp courses, and then apply the techniques to HR business problems.
4. Thank you!
I hope you've enjoyed the course. It's tough to find datasets and courses that focus directly on how to apply these and other techniques to HR data, but I encourage you to learn the tools and get involved with the HR analytics community. Learn what business problems others are solving, and see how you could do the same at your own workplace. Thank you!