Johns Hopkins University - Advanced Statistics for Data Science Specialization
- Offered byCoursera
- Public/Government Institute
Advanced Statistics for Data Science Specialization at Coursera Overview
Duration | 5 months |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Advanced |
Official Website | Go to Website  |
Credential | Certificate |
- Overview
- Highlights
- Course Details
- Curriculum
Advanced Statistics for Data Science Specialization at Coursera Highlights
- Learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and more.
- Understand the matrix algebra of linear regression models.
- Learn about canonical examples of linear models to relate them to techniques that you may already be using.
Advanced Statistics for Data Science Specialization at Coursera Course details
- This specialization by Johns Hopkins University 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.
Advanced Statistics for Data Science Specialization at Coursera Curriculum
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 model
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