We have searched the web for the best BioStatistics classes. We analyzed over 189 BioStatistics courses found on popular education sites like Udemy and Coursera and rated them based on course instructors, number of reviews, ratings, and more. Below is a list of our top 11 favorite BioStatistics classes. At the very bottom of the post you can check our revision history.
1. Understanding Clinical Research: Behind the Statistics
Do you want to take your medical profession ahead into conducting research? If yes, then you start by taking up this course. The concepts of clinical research and statistical analysis will help you understand any medical research paper or article better. The best part about this course is that it is completely free of cost for you.
You’ll be instructed by Dr Juan H Klopper from the University of Cape Town. He is a senior lecturer in surgery and practices the same at Groote Schuur Hospital. Handling emergency surgeries is one of his specializations due to which he is the head of the Acute Care Surgery unit in the hospital.
- Understand the field of medical research and the data collected in it.
- Learn how to start with statistical analysis in medical research.
- Introduce yourself to the concept of hypothesis testing and confidence intervals.
- Categorize your data and understand the test method you need to implement to make an accurate analysis.
2. Biostatistics in Public Health Specialization
Biostatistics is a vast field that involves a lot of working knowledge. This is a specialization program that consists of a series of 4 courses, where you start from the basics of statistics implemented in medical research, learn various testing methods and then end with simple and multiple regression analysis in public health.
You’ll be guided by Dr John McGready, an associate scientist and a faculty member of the Johns Hopkins University. His subjects include Statistical Reasoning in Public Health I and II and Statistical Methods in Public Health III. He has won the Golden Apple Award for excellence in teaching 4 times and the Teaching Award for Excellence in Distance Education twice.
- Introduce yourself to various statistical methods used for conducting research in public health.
- Study about hypothesis testing, central limit theorem, p values and confidence intervals.
- Learn simple regression methods which use linear equations to find relations between a predictor and an outcome.
- At last, move on to learn multiple regression analysis for interpreting data.
3. Mathematical Biostatistics Boot Camp 1
This course has been designed for absolute beginners having some working knowledge in Calculus. You’ll be learning the preliminary steps to making statistical analysis on medical research, which include probabilities, vectors, hypothesis testing, confidence intervals, bootstrapping, binomial proportions and logs.
This course is offered by another professor from Johns Hopkins University, Dr Brian Caffo. He completed his graduation in 2001 from the University of Florida. His achievements include winning the Presidential Early Career Award for Scientist & Engineers and the Golden Apple & AMTRA teaching awards from Bloomberg School of Public Health.
- Introduce yourself to probability, expectations and random vectors.
- Move on to understand conditional probability, likelihood, distributions and asymptomatics.
- Learn about confidence intervals, bootstrapping and plotting.
- Study binomial proportions and logs to clarify all your basic skills.
This is another free introductory level course on biostatistics designed specially for undergraduate students. You’ll be trained to conduct experimental and observational studies to collect, analyze and interpret statistical data in the fields of healthcare and biology. Some of these include studying the effectiveness of certain drugs and risk factors of any illness.
Your course guide is Michael Kangas, the professor of Doane University. He has a PhD in inorganic chemistry and has conducted research on solid state chemistry, publishing 16 papers on the same. His expertise lies in R, principal component analysis and Image-J that he uses for analysing colorimetric arrays with statistical methods.
- Start with identification of different types of data and their method of collection.
- Use R for summarizing data graphically as well as numerically.
- Learn how to perform the right statistical test for a given data.
- Interpret your final results from the test you conducted.
5. Introduction to Applied Biostatistics: Statistics for Medical Research
This intermediate course on applied biostatistics introduces you to epidemiological study designs, data analysis and various other concepts of medical statistics. You’ll be given access to several clinica research papers to get a more practical and hands-on experience in this field. You’ll be using R-commander and PS sample size software for doing so.
Your instructor, Ayumi Shintani is the professor of Osaka University. She has been teaching there for 10 years now and has won several awards for the same. While her area of expertise lies in statistical studies, observational studies and health services research, her work includes more than 190 peer-reviewed publications.
- Start with an understanding of descriptive statistics and hypothesis testing.
- Introduce yourself to various epidemiological concepts.
- Study the different statistical tests and understand how to select each.
- Use PS sample size for making power analysis of clinical studies.
6. Biostatistics for Big Data Applications
This is another introductory level course that uses R programming for learning various biostatistics methods and concepts. You’ll be learning the methods used to conduct research in omics and population health. As you work with biomedical big data and practice datasets, you’ll be improving your skills in R-commander and interpreting statistical data.
You have 2 professors from The University of Texas Medical Branch – James Graham and Heidi Spratt. While James is an associate professor and director of the Rehabilitation Sciences PhD program, Heidi happens to be an associate professor in Preventive Medicine and Community Health and senior statistician in the Office of Biostatistics.
- Introduce yourself to R programming and use it to work with biostatistics.
- Learn about parametric and non-parametric methods in inferential statistics.
- Create graphical summaries of data collected in research.
- Understand the interpretation of various statistical data.
7. Introduction to Biostatistics
This is a detailed program on learning biostatistics that has been designed for beginners. If you wish to go ahead in the field of clinical research, this is the first step you need to take. You’ll be introduced to statistics, probability and their applications and methods in conducting medical and biological research.
The course is presented to you by the Yale School of Medicine, your tutor being James DZiura. He teaches Emergency Medicine, Endocrinology and Biostatistics as well as is the deputy director of Yale Centre for Analytical Sciences. After completing his PhD in 2001 from the same university, he became a faculty member there in 2002.
- Introduce yourself to data types, probability and descriptive statistics.
- Learn about hypothesis testing, confidence intervals and comparing means.
- Study the analysis of variance, repeated measures, correlation and regression.
- Gain insights into nonparametric statistics, survival analysis and categorical outcomes
8. Mathematical Biostatistics Boot Camp 2
Starting from hypothesis testing, you’ll be learning all the fundamental concepts required for biostatistics. This is a 4-week program where you’ll be gaining knowledge on statistics, various statistical methods and their applications on research in the field of healthcare, medicine and biology.
This course is also guided by Brian Caffo from Johns Hopkins University (refer to point 3). You may even say that this course is a continuation of Boot Camp 1. Brian teaches several other courses on regression models, data analysis, statistical inference, R programming, data science, machine learning and reproducible research.
- Introduce yourself to the concept of hypothesis testing.
- Learn about the two binomials, which include odds ratio, relative risk and risk difference.
- Use Fisher’s exact test and discrete data settings for contingency table data.
- Study various techniques like the Simpson’s Paradox, CMH test and others for statistical analysis.
9. Biostatistics Fundamentals using Python
Are you willing to expand your skills in the field of healthcare and related research? If yes, then this course will teach you how to make biostatistical analysis with the help of python programming language. You might need a basic understanding in statistics, else no other knowledge base is required for this course.
Your instructor, Juan Klopper is the head of Postgraduate Surgical Research as well as Surgical Education at the University of Cape Town. In 2014, he won the Open Education Consortium Educator of the Year award for his excellence in online education. He has 3 other courses on mathematica and healthcare & life sciences.
- Install python and Jupyter Notebook in your machine.
- Start working with simple arithmetic, collections and data.
- Study descriptive statistics, data visualization and parametric tests.
- Learn correlation, linear regression and logistic regression.
10. Principles, Statistical and Computational Tools for Reproducible Data Science
Designed for students and professionals in the field of biostatistics, bioinformatics and data science, this intermediate course covers plenty of concepts. You’ll be learning analysis paradigms and statistical tools that are applied for reproducible data analysis, dynamic report generation and experimental design.
This course is presented by Harvard University and you have 4 of their professors guiding you – Curtis Huttenhower, John Quackenbush, Lorenzo Trippa and Christine Choirat. While Curtis and John teach computational biology and bioinformatics, Lorenzo teaches biostatistics. Christine, on the other hand, is a research associate in the biostatistics department.
- Introduce yourself to the fundamentals of reproducible science.
- Go through various case studies to understand reproducible reporting modules.
- Learn various statistical methods like prediction models and survival analysis.
- Study computational tools like R, Python and GitHub for reproducible science.
11. Doing Clinical Research: Biostatistics with the Wolfram Language
This course teaches you to conduct statistical tests for clinical research papers and presentations with the help of Wolfram programming language. You’ll learn to summarize data as well as create plots and charts for doing so. Towards the end of the 4th week, you’ll be ready to give a final exam on the subject too.
This course has also been conducted by Dr Juan H Klopper from the University of Cape Town (refer to point 1). He has trained more than 102,000 online only and several other batches in the University too. Apart from clinical research and biostatistics, he also conducts an online course on Julia scientific programming.
- Understand how statistical analysis can be done using Wolfram.
- Master the programming language as you work in different coding environments.
- Start your data analysis and summarize it with the help of Wolfram.
- Train yourself to conduct all kinds of statistical tests to conclude your research project.
- List published 06/16/2020 with 11 products.