Coursera - Advanced Statistics for Data Science Specialization by Johns Hopkins University
Video: .mp4 (1280x720) | Audio: AAC, 44100 kHz, 2ch | Size: 2.22 Gb
Genre: eLearning Video | Duration: 22h 22m | Language: English
Video: .mp4 (1280x720) | Audio: AAC, 44100 kHz, 2ch | Size: 2.22 Gb
Genre: eLearning Video | Duration: 22h 22m | Language: English
Familiarize yourself with fundamental concepts in probability and statistics, data analysis and linear models for Data Science.
Mathematical Biostatistics Boot Camp 1
This 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.
Mathematical Biostatistics Boot Camp 2
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Advanced Linear Models for Data Science 1: Least Squares
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:
- 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.
Advanced Linear Models for Data Science 2: Statistical Linear Models
Welcome 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.