Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following:
This course is part of the Advanced Statistics for Data Science Specialization
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Advanced Statistics for Data Science SpecializationJohns Hopkins UniversityAbout this Course
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Course 3 of 4 in the
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Advanced Level
Approx. 8 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Skills you will gain
StatisticsLinear RegressionR ProgrammingLinear Algebra
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 3 of 4 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 8 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Offered by
Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Syllabus - What you will learn from this course
2 hours to complete
Background
We cover some basic matrix algebra results that we will need throughout the class. This includes some basic vector derivatives. In addition, we cover some some basic uses of matrices to create summary statistics from data. This includes calculating and subtracting means from observations (centering) as well as calculating the variance.
2 hours to complete
7 videos (Total 28 min), 4 readings, 1 quiz
1 hour to complete
One and two parameter regression
In this module, we cover the basics of regression through the origin and linear regression. Regression through the origin is an interesting case, as one can build up all of multivariate regression with it.
1 hour to complete
6 videos (Total 29 min), 2 readings, 1 quiz
1 hour to complete
Linear regression
In this lecture, we focus on linear regression, the most standard technique for investigating unconfounded linear relationships.
1 hour to complete
8 videos (Total 23 min), 2 readings, 1 quiz
1 hour to complete
General least squares
We now move on to general least squares where an arbitrary full rank design matrix is fit to a vector outcome.
1 hour to complete
6 videos (Total 39 min), 1 reading, 1 quiz
Reviews
TOP REVIEWS FROM ADVANCED LINEAR MODELS FOR DATA SCIENCE 1: LEAST SQUARES
by JASep 8, 2020
This is an excellent course that enabled me to understand how multiple regression in linear models works behind the hood. The practical examples shown by the professor were very helpful. Thank you
by JRNov 6, 2017
Great, detailed walk-through of least squares. Linear Algebra is a must for this course. To follow the last part requires knowledge of matrix (eigen?)decomposition, which derailed me somewhat.
by DLJun 7, 2016
We need more advanced, theoretical courses on Coursera, like this one, in order to deeply understand the more general courses like Regression Models and Linear Models.
by JLMay 16, 2020
I really enjoyed the course. It was well explained and the quizzes at regular intervals were helpful. It would be great if there were some practice exercises though...
About the Advanced Statistics for Data Science Specialization
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.
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