Advanced Statistics for Data Science
Johns Hopkins University via Coursera Specialization
Overview
Class Central Tips
Syllabus
- Offered by Johns Hopkins University. This class presents the fundamental probability and statistical concepts used in elementary data ... Enroll for free.
Course 2: Mathematical Biostatistics Boot Camp 2
- Offered by Johns Hopkins University. Learn fundamental concepts in data analysis and statistical inference, focusing on one and two ... Enroll for free.
Course 3: Advanced Linear Models for Data Science 1: Least Squares
- Offered by Johns Hopkins University. Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an ... Enroll for free.
Course 4: Advanced Linear Models for Data Science 2: Statistical Linear Models
- Offered by Johns Hopkins University. Welcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. This class ... Enroll for free.
Courses
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17 Reviews
4 weeks long, 13 hours worth of material
View detailsThis class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required. -
4 Reviews
4 weeks long, 11 hours worth of material
View detailsLearn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples. -
1 Reviews
6 weeks long, 8 hours worth of material
View detailsWelcome 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:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.
After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models. -
1 Reviews
4 weeks long, 5 hours worth of material
View detailsWelcome to the Advanced Linear Models for Data Science Class 2: Statistical Linear Models. 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:
- A basic understanding of linear algebra and multivariate calculus.
- A basic understanding of statistics and regression models.
- At least a little familiarity with proof based mathematics.
- Basic knowledge of the R programming language.
After taking this course, students will have a firm foundation in a linear algebraic treatment of regression modeling. This will greatly augment applied data scientists' general understanding of regression models.
Taught by
Brian Caffo, PhD
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0.0 rating, based on 0 reviews
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