Advertisement
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.
Are you Johns Hopkins University? Claim this listing to add a verified badge, update your profile, and reach more families.
Resources from Johns Hopkins University
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...
Getting and Cleaning Data
Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cov...
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....
Capstone: Photo Tourist Web Application
In this Capstone project for the Photo Tourist you will implement a Ruby on Rails web application that makes use of both a relational and NoSQL database for the backend and expose the data through services to the Internet using Web services and a res...
Algorithms for DNA Sequencing
Algorithms for DNA Sequencing is course 4 of 8 in the Genomic Data Science Specialization. This specialization covers the concepts and tools to understand, analyze, and interpret data from next generation sequencing experiments. It teaches the most...
Regression Models
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This c...
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)...
Genomic Data Science Capstone
In this culminating project, you will deploy the tools and techniques that you've mastered over the course of the specialization. You'll work with a real data set to perform analyses and prepare a report of your findings....
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...
Statistical Reasoning for Public Health 2: Regression Methods
A practical and example filled tour of simple and multiple regression techniques (linear, logistic, and Cox PH) for estimation, adjustment and prediction....
Managing Data Analysis
This one-week course describes the process of analyzing data and how to manage that process. We describe the iterative nature of data analysis and the role of stating a sharp question, exploratory data analysis, inference, formal statistical modeling...