Johns Hopkins University
Data Science: Foundations using R Specialization

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Johns Hopkins University

Data Science: Foundations using R Specialization

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

Instructors: Roger D. Peng, PhD

115,441 already enrolled

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Get in-depth knowledge of a subject
4.6

(6,169 reviews)

Beginner level
No prior experience required
4 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers
Get in-depth knowledge of a subject
4.6

(6,169 reviews)

Beginner level
No prior experience required
4 months at 10 hours a week
Flexible schedule
Earn a career credential
Share your expertise with employers

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.

Overview

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English
35 practice exercises

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Johns Hopkins University

Specialization - 5 course series

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

Skills you'll gain

Category: Version Control
Category: Data Science
Category: Rmarkdown
Category: R Programming
Category: Git (Version Control System)
Category: Data Analysis
Category: GitHub
Category: Integrated Development Environments
Category: Statistical Programming
Category: Software Installation
Category: Development Environment

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

Skills you'll gain

Category: R Programming
Category: Simulations
Category: Debugging
Category: Performance Tuning
Category: Data Structures
Category: Statistical Analysis
Category: Statistical Programming
Category: Data Import/Export
Category: Program Development
Category: Data Analysis
Category: Computer Programming Tools

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

Skills you'll gain

Category: Data Manipulation
Category: Data Cleansing
Category: R Programming
Category: Data Import/Export
Category: SQL
Category: Data Management
Category: Application Programming Interface (API)
Category: Data Wrangling
Category: File Management
Category: Data Integration
Category: Data Quality
Category: Web Scraping
Category: Exploratory Data Analysis
Category: Data Collection
Category: Data Access
Category: MySQL
Category: Data Transformation

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

Skills you'll gain

Category: R Programming
Category: Plot (Graphics)
Category: Ggplot2
Category: Exploratory Data Analysis
Category: Graphing
Category: Data Analysis
Category: Histogram
Category: Data Visualization
Category: Unsupervised Learning
Category: Statistical Analysis
Category: Data Visualization Software
Category: Dimensionality Reduction
Category: Box Plots
Category: Color Theory
Category: Scatter Plots

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

Skills you'll gain

Category: Knitr
Category: Rmarkdown
Category: Data Validation
Category: Verification And Validation
Category: R Programming
Category: Technical Documentation
Category: Statistical Analysis
Category: Data Analysis
Category: Data Sharing
Category: Statistical Reporting

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Instructors

Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,656,140 learners
Brian Caffo, PhD
Johns Hopkins University
30 Courses1,684,383 learners
Jeff Leek, PhD
Johns Hopkins University
32 Courses1,720,006 learners

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