Introduction to Statistical Learning will explore concepts in statistical modeling, such as when to use certain models, how to tune those models, and if other options will provide certain trade-offs. We will cover Regression, Classification, Trees, Resampling, Unsupervised techniques, and much more!
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.
DM
Great course with clear and concise explanation. I highly recommend taking the course.
Showing: 5 of 5
The focus should be on both - theory and practical application, which I did not see in this course. Practical Application makes the learning easy along with the theoretical explanations
Great course with clear and concise explanation. I highly recommend taking the course.
This is a fine course and I especially liked the lecturer and the lectures. However, the assignments are raw and do not provided clear requirements on how to complete them which takes a lot of time to figure out. The code provided in workshops is useful and applicable for future modelling.
This course is so NOT well designed and prepared. Contents are not clearly explained, assignments are too easy and some of the requirements are unclear.
No recomendable. Resolver las actividades de programación puede llegar a ser muy frustrante.