If you have a technical background in mathematics/statistics/computer science/engineering and or are pursuing a career change to jobs or industries that are data-driven, this course is for you. Those industries might be finance, retail, tech, healthcare, government, or many others. The opportunity is endless.
Variable Selection, Model Validation, Nonlinear Regression
Variable Selection, Model Validation, Nonlinear Regression
This course is part of Advanced Statistical Techniques for Data Science Specialization
Instructor: Kiah Ong
Access provided by New York State Department of Labor
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
10 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
Build your subject-matter expertise
This course is part of the Advanced Statistical Techniques for Data Science Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 4 modules in this course
In this module, you will learn the differences between logistic regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, and use R to compute the estimators of a linear regression model and give a probabilistic prediction of Y=1 given X=x鈥檚. There is a lot to read, watch, and consume in this module so, let鈥檚 get started!
What's included
7 videos4 readings3 assignments1 discussion prompt
In this module, you will learn the difference between Poisson regression and ordinary linear regression, how to obtain the regression parameters using the maximum likelihood method, use R to compute the estimators of a Poisson regression model and the generalized linear model, and the similarities between the linear, logistic, and Poisson regressions. There is a lot to read, watch, and consume in this module so, let鈥檚 get started!
What's included
6 videos3 readings3 assignments
In this module, you will learn how to modify the ordinary least squares method to make the regression model more robust to the effect of outliers and use R to compute the robust regression parameters using different M-estimators and perform model validations involving logistic regression. There is a lot to read, watch, and consume in this module so, let鈥檚 get started!
What's included
7 videos4 readings3 assignments
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.
What's included
1 assignment
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.
Build toward a degree
This course is part of the following degree program(s) offered by Illinois Tech. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.鹿
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