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Bioinformatics: Life Sciences on Your Computer

from coursera (Free)

What makes bioinformatics education exciting is that people of a variety of education levels can get started quickly, with just a computer and internet access.

Developing Data Products

from coursera (Free)

Learn the basics of creating data products using Shiny, R packages, and interactive graphics. This is the ninth course in the Johns Hopkins Data Science Specialization.

Exploratory Data Analysis

from coursera (Free)

Learn the essential exploratory techniques for summarizing data. This is the fourth course in the Johns Hopkins Data Science Specialization.

Getting and Cleaning Data

from coursera (Free)

Learn how to gather and clean data from a variety of sources. This is the third course in the Johns Hopkins Data Science Specialization.

Mathematical Biostatistics Boot Camp 2

from coursera (Free)

Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples.

Practical Machine Learning

from coursera (Free)

Learn the basic components of building and applying prediction functions with an emphasis on practical applications. This is the eighth course in the Johns Hopkins Data Science...

R Programming

from coursera (Free)

Learn how to program in R and how to use R for effective data analysis. This is the second course in the Johns Hopkins Data Science Specialization.

Regression Models

from coursera (Free)

Learn how to use regression models, the most important statistical analysis tool in the data scientist's toolkit. This is the seventh course in the Johns Hopkins Data Science...

Reproducible Research

from coursera (Free)

Learn the concepts and tools behind reporting modern data analyses in a reproducible manner. This is the fifth course in the Johns Hopkins Data Science Specialization.

Statistical Inference

from coursera (Free)

Learn how to draw conclusions about populations or scientific truths from data. This is the sixth course in the Johns Hopkins Data Science Course Track.