Physics, Mathematics & Statistics
Physics, Mathematics & Statistics resources
Free online Physics courses
A good collection of Physics courses from “Open Culture”.
Improving Your Statistical Questions
This course aims to help you to ask better statistical questions when performing empirical research. It will discuss how to design informative studies, both when your predictions are correct, as well as when your predictions are wrong. It will question norms, and reflect on how research practices can be improved to ask more interesting questions. In practical hands on assignments you will learn techniques and tools that can be immediately implemented in your own research, such as thinking about the smallest effect size you are interested in, justifying your sample size, evaluate findings in the literature while keeping publication bias into account, performing a meta-analysis, and making your analyses computationally reproducible.
IGMM Statistical Seminar Series 2020
A block of statistical seminars with particular relevance to biomedical research. The seminars were delivered online by scientists from the MRC Institute of Genetics & Molecular Medicine (including some of the XDF Programme Fellows) in 2020.
Website: https://media.ed.ac.uk/channel/IGMM%2BStatistical%2BSeminar%2BSeries%2B2020/165517051 (available to University of Edinburgh affiliates only)
Introduction to Probability and Statistics
This course provides an elementary introduction to probability and statistics with applications. Topics include: basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
Probability and Statistics
The Probability and Statistics course contains four main units (Exploratory Data Analysis, Producing Data, Probability, Inference) that have several sections within each unit. It is designed to be equivalent to one semester of a college statistics course.
Introduction to Modern Statistics
This free online book provides easy to follow introduction to multiple aspects of modern statistics and their applications including: computational methods for statistical inference, linking of computational and mathematical models, and introduction to multivariable modeling.
Modern Statistics for Modern Biology
The aim of this book by Susan Holmes, and Wolfgang Huber is to enable scientists working in biological research to quickly learn many of the important ideas and methods that they need to make the best of their experiments and of other available data.
Statistical Thinking for the 21st Century
This book by Russell A. Poldrack describes the approaches that are increasingly used in real statistical practice in the 21st century. Themethods described take advantage of today’s increased computing power to solve statistical problems in ways that go far beyond the more standard methods that are usually taught in the undergraduate statistics courses.
A visual introduction to probability and statistics
Statistical Thinking and Data Analysis
This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and nonparametric statistics.
Statistics for Applications
This course offers an in-depth theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.
Selective Inference and False Discovery Rate I
Interesting lecture on multiple hypothesis testing.
Statistics and R
An introduction to basic statistical concepts and R programming skills necessary for analyzing data in the life sciences.
Explore Statistics with R
Learn basic statistics in a practical, experimental way, through statistical programming with R, using examples from the health sciences.
Introduction to Statistical Methods for Gene Mapping
This data course is a primer to statistical genetics and covers an approach called linkage disequilibrium mapping, which analyses non-familial data and has been successfully used to identify genetic variants associated with common and complex genetic traits.
Introduction to Mathematical Thinking
The goal of the course is to help you develop a valuable mental ability – a powerful mathematical way of thinking that our ancestors have developed over three thousand years.
Applications of Linear Algebra Part 1
Learn to use linear algebra in computer graphics by making images disappear in an animation or creating a mosaic or fractal and in data mining to measure similarities between movies, songs, or friends.
Applications of Linear Algebra Part 2
Explore applications of linear algebra in the field of data mining by learning fundamentals of search engines, clustering movies into genres and of computer graphics by posterizing an image.