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:
Advanced Linear Models for Data Science 1: Least Squares
Johns Hopkins UniversityAbout this Course
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Advanced Level
Approx. 6 hours to complete
English
Subtitles: English
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.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 6 hours to complete
English
Subtitles: English
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
1 hour 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.
1 hour to complete
7 videos (Total 28 min), 4 readings, 1 quiz
7 videos
Matrix derivatives5m
Coding example2m
Centering by matrix multiplication6m
Coding example2m
Variance via matrix multiplication6m
Coding example2m
4 readings
Welcome to the class10m
Course textbook10m
Grading10m
In this module10m
1 practice exercise
Background Quiz12m
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
6 videos
Centering first8m
Coding example1m
Connection with linear regression7m
Coding example1m
Fitted values and residuals4m
2 readings
Before you begin10m
Before you begin10m
1 practice exercise
One Parameter Regression Quiz10m
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
8 videos
Coding example1m
Prediction2m
Coding example2m
Residuals2m
Coding example1m
Generalizations6m
Generalizations example2m
2 readings
Before you begin10m
Generalizations10m
1 practice exercise
Linear Regression Quiz12m
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
6 videos
Coding example3m
Second derivation of least squares4m
Projections9m
Third derivation of least squares12m
Coding example4m
1 reading
Before you begin10m
1 practice exercise
General Least Squares Quiz20m
Reviews
TOP REVIEWS FROM ADVANCED LINEAR MODELS FOR DATA SCIENCE 1: LEAST SQUARES
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 SPApr 29, 2017
Good mathematical rigour for the analysis of linear models. Builds some good intuition for the geometry of least squares which helps in model result interpretation.
by HMJun 11, 2016
As the name says it's an advanced course. Take the challenge though! In my opinion the content is a must if you want to perform competently in data science.
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