Exploring Mathematical Biostatistics Boot Camp 2
Uncover the World of Data Analysis and Statistical Inference
The Mathematical Biostatistics Boot Camp 2 is a free online course offered by Johns Hopkins University that teaches fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.
Pathway to Law, Economics, and Data Science
Explore Free Courses on topics related to Law, Economics, and Data Science with a particular emphasis on Causal Inference. This post is part of an exciting new blog series, where I will be compiling an array of exceptional free courses within these fields.
This blog series covers three distinct yet interconnected fields: law, economics, and data science. These domains offer unique insights into understanding our society, making informed decisions, and extracting meaningful insights from complex data.
Causal Inference: Unraveling Cause and Effect
Causal inference lies at the heart of understanding how actions, events, and conditions lead to specific outcomes. In our data-driven world, the ability to discern causality from correlation is crucial for making impactful decisions. The art of identifying cause-and-effect relationships is an invaluable skill in fields ranging from public policy to data-driven business strategies, so I hope these posts can be of some benefit whether you are looking at improving your skills for academia or industry paths.
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Course Structure:
The Mathematical Biostatistics Boot Camp 2 is comprised of four comprehensive modules, each addressing crucial aspects of statistical analysis in a systematic and engaging manner.
Module 1:
Introduction to hypothesis testing for basic one and two group settings as well as power.
Module 2:Methods for looking at two binomials, including the odds ratio, relative risk, and risk difference. This module mostly covers confidence intervals and develops the delta method, the tool used to create these confidence intervals.
Module 3:Testing in discrete data settings, including Fisher's exact test and the many forms of tests for contingency table data. You'll learn the famous observed minus expected squared over the expected formula, which is broadly applicable.
Module 4:Methods for discrete matched pairs data as well as some classical non-parametric methods.
After completing the course, you will gain skills in fundamental concepts in data analysis and statistical inference.
Enrol for free here
