# Mathematical Biostatistics Boot Camp 1

Johns Hopkins University

Join the Mathematical Biostatistics Boot Camp 1 to gain a strong foundation in probability, statistical concepts, and elementary data analysis. This comprehensive course, offered by Johns Hopkins University, is designed for students with junior or senior college-level mathematical training, including a working knowledge of calculus.

Throughout the course, you will delve into essential topics such as probability, expectations, random vectors, conditional probability, likelihood, distributions, confidence intervals, bootstrapping, and much more. The curriculum is tailored to cover a wide range of fundamental concepts in biostatistics, enabling you to develop crucial analytical skills and apply statistical methods in various research and data analysis contexts.

• Gain a robust understanding of probability and statistical concepts
• Explore essential topics such as conditional probability, likelihood, and confidence intervals
• Learn to apply statistical methods in research and data analysis
• Develop fundamental analytical skills for biostatistics

Certificate Available ✔

##### Course Modules

This course comprises four modules covering a wide range of topics such as probability, random vectors, conditional probability, likelihood, confidence intervals, bootstrapping, and more. Each module is designed to provide in-depth understanding and practical knowledge in biostatistics.

#### Introduction, Probability, Expectations, and Random Vectors

Welcome to the first module of the course, where you will explore the fundamental concepts of probability and statistical analysis. From understanding random vectors to exploring expectations and probability, this module sets the groundwork for your biostatistics journey.

• Gain insights into probability and random vectors
• Understand the concept of expectations and probability
• Explore the fundamental rules about expected values

#### Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics

Dive into the world of conditional probability, Bayes' Rule, and likelihood in this module. Understand how these concepts play a crucial role in biostatistics and gain practical knowledge on applying these principles to real-world data analysis scenarios.

• Learn about conditional probabilities and densities
• Explore the application of Bayes' Rule and likelihood
• Understand the significance of the Normal Distribution in statistical analysis

#### Confidence Intervals, Bootstrapping, and Plotting

This module focuses on confidence intervals, bootstrapping, and plotting techniques essential for biostatistics. Gain practical knowledge on constructing confidence intervals, utilizing bootstrapping methods, and mastering the art of effective data visualization through plotting.

• Understand the construction of confidence intervals
• Learn about bootstrapping and its applications in statistical analysis
• Master the art of effective data visualization through plotting

#### Binomial Proportions and Logs

Explore the concepts of binomial proportions and logarithms in this module, gaining insights into their significance in biostatistical analysis. Dive deep into the world of binomial proportions, understand their properties, and master the utilization of logarithms in statistical analysis.

• Understand the properties and significance of binomial proportions
• Explore the application of logarithms in statistical analysis
• Gather practical knowledge on utilizing binomial proportions and logs in biostatistics

#### Expressway to Data Science: Essential Math

This course provides a foundational understanding of essential math for Data Science, covering algebra, calculus, linear algebra, and numerical analysis.

#### Calculus through Data & Modeling: Applying Differentiation

Johns Hopkins University

Calculus through Data &amp; Modeling: Applying Differentiation provides practical applications of derivatives to estimate functions, locate extrema, and solve optimization...

#### Games without Chance: Combinatorial Game Theory

Georgia Institute of Technology

Games without Chance: Combinatorial Game Theory is an in-depth exploration of mathematical analysis and strategy in games without chance moves.