Introduction to the Tidyverse
Tidyverse Skills for Data Science in R,
This course introduces a powerful set of data science tools known as the Tidyverse. The Tidyverse has revolutionized the way in which data scientists do almost every aspect of their job. We will cover the simple idea of "tidy data" and how this idea serves to organize data for analysis and modeling. We will also cover how non-tidy can be transformed to tidy data, the data science project life cycle, and the ecosystem of Tidyverse R packages that can be used to execute a data science project. If you are new to data science, the Tidyverse ecosystem of R packages is an excellent way to learn the different aspects of the data science pipeline, from importing the data, tidying the data into a format that is easy to work with, exploring and visualizing the data, and fitting machine learning models. If you are already experienced in data science, the Tidyverse provides a power system for streamlining your workflow in a coherent manner that can easily connect with other data science tools. In this course it is important that you be familiar with the R programming language. If you are not yet familiar with R, we suggest you first complete R Programming before returning to complete this course.
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Rating | Not enough ratings |
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Length | 5 weeks |
Effort | 4 weeks of study, 1-3 hours/week |
Starts | Jul 3 (43 weeks ago) |
Cost | $49 |
From | Johns Hopkins University via Coursera |
Instructors | Carrie Wright, PhD, Shannon Ellis, PhD, Stephanie Hicks, PhD, Roger D. Peng, PhD |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Mathematics |
Tags | Data Science Data Analysis Probability And Statistics |
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Rating | Not enough ratings |
---|---|
Length | 5 weeks |
Effort | 4 weeks of study, 1-3 hours/week |
Starts | Jul 3 (43 weeks ago) |
Cost | $49 |
From | Johns Hopkins University via Coursera |
Instructors | Carrie Wright, PhD, Shannon Ellis, PhD, Stephanie Hicks, PhD, Roger D. Peng, PhD |
Download Videos | On all desktop and mobile devices |
Language | English |
Subjects | Data Science Mathematics |
Tags | Data Science Data Analysis Probability And Statistics |
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