Johns Hopkins University
Johns Hopkins enrolls more than 24,000 full- and part-time students throughout nine academic divisions. Our faculty and students study, teach, and learn across more than 400 programs in the arts and music, the humanities, the social and natural sciences, engineering, international studies, education, business, and the health professions.
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Resources from Johns Hopkins University
Bioconductor for Genomic Data Science
Learn to use tools from the Bioconductor project to perform analysis of genomic data. This is the fifth course in the Genomic Big Data Specialization from Johns Hopkins University....
Chemicals and Health
Syllabus Week 1 Week 1: Welcome & Introduction Welcome to Chemicals and Health - Megan Latshaw Chemicals in our environment: What is a chemical and how are we exposed? - Tom Burke Production, Occupational Health, and Regulation &...
Statistics for Genomic Data Science
An introduction to the statistics behind the most popular genomic data science projects. This is the sixth course in the Genomic Big Data Science Specialization from Johns Hopkins University....
Command Line Tools for Genomic Data Science
Introduces to the commands that you need to manage and analyze directories, files, and large sets of genomic data. This is the fourth course in the Genomic Big Data Science Specialization from Johns Hopkins University....
Data Science in Real Life
Have you ever had the perfect data science experience? The data pull went perfectly. There were no merging errors or missing data. Hypotheses were clearly defined prior to analyses. Randomization was performed for the treatment of interest. The analy...
Exploratory Data Analysis
This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are a...
Introduction to Systematic Review and Meta-Analysis
We will introduce methods to perform systematic reviews and meta-analysis of clinical trials. We will cover how to formulate an answerable research question, define inclusion and exclusion criteria, search for the evidence, extract data, assess the r...
Advanced Linear Models for Data Science 1 : Linear Models
Welcome to the Advanced Linear Models for Data Science Class 1: Least Squares. This class is an introduction to least squares from a linear algebraic and mathematical perspective. Before beginning the class make sure that you have the following: R...
Building a Data Science Team
Data science is a team sport. As a data science executive it is your job to recruit, organize, and manage the team to success. In this one-week course, we will cover how you can find the right people to fill out your data science team, how to organiz...
Design and Interpretation of Clinical Trials
Clinical trials are experiments designed to evaluate new interventions to prevent or treat disease in humans. The interventions evaluated can be drugs, devices (e.g., hearing aid), surgeries, behavioral interventions (e.g., smoking cessation program)...
Python for Genomic Data Science
This class provides an introduction to the Python programming language and the iPython notebook. This is the third course in the Genomic Big Data Science Specialization from Johns Hopkins University....
Health for All Through Primary Health Care
About this Course This course explores why primary health care is central for achieving Health for All. It provides examples of how primary health care has been instrumental in approaching this goal in selected populations and how the principles of...