Johns Hopkins University

Data Science: Foundations using R Specialization

Johns Hopkins University

Data Science: Foundations using R Specialization

Roger D. Peng, PhD
Brian Caffo, PhD
Jeff Leek, PhD

Instructors: Roger D. Peng, PhD

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Get in-depth knowledge of a subject

from 48,205 reviews of courses in this program

Beginner level
No prior experience required
4 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject

from 48,205 reviews of courses in this program

Beginner level
No prior experience required
4 months to complete
at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Use R to clean, analyze, and visualize data.

  • Learn how to ask the right questions, obtain data, and perform reproducible research.

  • Use GitHub to manage data science projects.

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Taught in English

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  • Develop a deep understanding of key concepts
  • Earn a career certificate from Johns Hopkins University

Specialization - 5 course series

The Data Scientist’s Toolbox

The Data Scientist’s Toolbox

Course 1, 18 hours

What you'll learn

  • Set up R, R-Studio, Github and other useful tools

  • Understand the data, problems, and tools that data analysts use

  • Explain essential study design concepts

  • Create a Github repository

R Programming

R Programming

Course 2, 58 hours

What you'll learn

  • Understand critical programming language concepts

  • Configure statistical programming software

  • Make use of R loop functions and debugging tools

  • Collect detailed information using R profiler

Getting and Cleaning Data

Getting and Cleaning Data

Course 3, 20 hours

What you'll learn

  • Understand common data storage systems

  • Apply data cleaning basics to make data "tidy"

  • Use R for text and date manipulation

  • Obtain usable data from the web, APIs, and databases

Exploratory Data Analysis

Exploratory Data Analysis

Course 4, 56 hours

What you'll learn

  • Understand analytic graphics and the base plotting system in R

  • Use advanced graphing systems such as the Lattice system

  • Make graphical displays of very high dimensional data

  • Apply cluster analysis techniques to locate patterns in data

Reproducible Research

Reproducible Research

Course 5, 8 hours

What you'll learn

  • Organize data analysis to help make it more reproducible

  • Write up a reproducible data analysis using knitr

  • Determine the reproducibility of analysis project

  • Publish reproducible web documents using Markdown

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Instructors

Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,685,612 learners
Brian Caffo, PhD
Johns Hopkins University
30 Courses1,714,100 learners

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