ANNOUNCEMENT:
To better serve you, Air University is moving to a new system for registering and managing students and delivering course content. This means there will be some changes in your experience with AFIT/LS. We apologize for any inconvenience or frustration this may cause. We are doing all we can to make this transition less stressful.
The current version of Canvas will be sun setting to make way for a new version. If you are currently in a SELF-PACED COURSE, it is HIGHLY recommended that you complete the course no later than 30 August 2022. If you are not able to finish by then, you will need to RE-REGISTER and RESTART that course in the new version of Canvas. Please note that PREVIOUS PROGRESS WILL NOT TRANSFER. Students can register for courses using the new registration system beginning 1 September.
If you are registered in an INSTRUCTOR-LED COURSE that will finish AFTER 1 September 2022, although you will be required to re-register for that course after 1 September 2022, you will NOT be required to restart. PREVIOUS PROGRESS WILL TRANSFER however student loaded material will need to be transferred by the student. Your instructor(s) will determine at what point the course will resume on the new Canvas platform.
Again, we sincerely apologies for any confusion or frustration this transition may cause. Starting 1 Sept 2022, more information about registration and other related topics can be found on our website at www.afit.edu/ls Please continue to review our website for updates at that time. Thanks for your patience and understanding.
updated August 10, 2022 7:57:46 AM EDT
This course provides Airmen an introduction and detailed look at the specific realm of data research known as statistical learning. Students will understand supervised and unsupervised learning, when to use linear regression or classification regression. Additionally resampling methods and model validations are discussed and implemented. The regularization of model selection through subset, dimensional reduction and ridge regression are examined. Finally, Tableau visualizations are implemented upon the statistical learning results to tell a story to the decision makers and stock holders. This is an advanced course in data research for students familiar with statistical theory and modeling methods. Additionally students should have experience with R-studio.
Grade Restrictions: None
Special Requirements: None
Delivery Method: e-Learning Course
Course Length:
Event Type: Training (Functional, Technical)Primary Audience:
CL Points: 30
Prerequisites:
