This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless.
This course is part of the Performance Based Admission courses for the Data Science program. This course will focus on getting you acquainted with the basic ideas behind regression, it provides you with an overview of the basic techniques in regression such as simple and multiple linear regression, and the use of categorical variables. Software Requirements: R Upon successful completion of this course, you will be able to: - Describe the assumptions of the linear regression models. - Compute the least squares estimators using R. - Describe the properties of the least squares estimators. - Use R to fit a linear regression model to a given data set. - Interpret and draw conclusions on the linear regression model. - Use R to perform statistical inference based on the regression models.
AP
Excellent intro, gets the math-intuition-application ratio bang on.
TT
The Course has good in-depth explanation on the different regression and assumptions
SR
It is a good course, but I think the video lecture duration should be more.
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Course Material is not easy to follow due. Seams the prof makes a lot assumptions. As i am using this cause for a masters I have to find supplemental material get good grasp. I hope my feedback will help to improve the course for other students
some questions in assessments do not have preceding information, e.g. outputs to answer them
It is a good course, but I think the video lecture duration should be more.
Excellent intro, gets the math-intuition-application ratio bang on.
Excellent course! Professor Ong is great!
very educative and easy to understand
great content, a bit challenging
really a great learning
The Course has good in-depth explanation on the different regression and assumptions