Advanced Statistics for Data Science

Offered By: Johns Hopkins University via Coursera

Course Description

Overview

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. This specialization starts with Mathematical Statistics bootcamps, specifically concepts and methods used in biostatistics applications. These range from probability, distribution, and likelihood concepts to hypothesis testing and case-control sampling. This specialization also linear models for data science, starting from understanding least squares from a linear algebraic and mathematical perspective, to statistical linear models, including multivariate regression using the R programming language. These courses will give learners a firm foundation in the linear algebraic treatment of regression modeling, which will greatly augment applied data scientists' general understanding of regression models. This specialization requires a fair amount of mathematical sophistication. Basic calculus and linear algebra are required to engage in the content.

Syllabus

Course 1: Mathematical Biostatistics Boot Camp 1
- 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

17 reviews
13 hours
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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.
4 reviews
12 hours
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Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
1 review
8 hours 27 minutes
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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.
1 review
5 hours 40 minutes
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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.

Taught by

Brian Caffo, PhD