# Advanced Statistics for Data Science

Johns Hopkins University

Fundamental concepts in probability, statistics, and linear models form the backbone of data science work. This specialization equips learners with the essential knowledge required in biostatistics and data science. The course encompasses Mathematical Statistics bootcamps, covering probability, distribution, and likelihood concepts, as well as hypothesis testing and case-control sampling.

Additionally, learners will delve into linear models for data science, including least squares and statistical linear models using the R programming language. The courses provide a comprehensive understanding of the algebraic treatment of regression modeling, enhancing the overall grasp of regression models for applied data scientists.

The specialization necessitates a fair amount of mathematical sophistication, requiring a basic understanding of calculus and linear algebra to fully engage with the content.

• Probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and more
• Matrix algebra of linear regression models
• Canonical examples of linear models

Certificate Available ✔

##### Course Modules

The course comprises Mathematical Biostatistics Boot Camp 1 and 2, as well as Advanced Linear Models for Data Science 1 and 2. Learners will gain a solid understanding of probability, statistics, and linear models essential for data science work.

#### Mathematical Biostatistics Boot Camp 1

This class introduces fundamental probability and statistical concepts used in elementary data analysis. It is designed for students with junior or senior college-level mathematical training, including a working knowledge of calculus. While a small amount of linear algebra and programming knowledge is useful, it is not required.

#### Mathematical Biostatistics Boot Camp 2

Building on the foundational concepts covered in the previous module, this class delves deeper into data analysis and statistical inference, focusing on one and two independent samples.

#### Advanced Linear Models for Data Science 1: Least Squares

Advanced Linear Models for Data Science Class 1: Least Squares provides an introduction to least squares from a linear algebraic and mathematical perspective. It requires a basic understanding of linear algebra, multivariate calculus, statistics, regression models, proof-based mathematics, and the R programming language.

#### Advanced Linear Models for Data Science 2: Statistical Linear Models

Advanced Linear Models for Data Science Class 2: Statistical Linear Models further explores least squares from a linear algebraic and mathematical perspective. Similar prerequisites to the previous class are required, and after completing this course, students will have a firm foundation in a linear algebraic treatment of regression modeling.

#### Precalculus through Data and Modelling

Johns Hopkins University

This specialization equips learners with foundational mathematical skills to model, analyze, and interpret data, preparing them for further scientific studies.

#### Fibonacci Numbers and the Golden Ratio

The Hong Kong University of Science and Technology

This course explores the fascinating mathematics of Fibonacci numbers and the golden ratio, revealing their connection and appearance in nature.