Johns Hopkins University
Data Science Specialization
Johns Hopkins University

Data Science Specialization

Launch Your Career in Data Science. A ten-course introduction to data science, developed and taught by leading professors.

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

Instructors: Roger D. Peng, PhD

489,449 already enrolled

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Specialization - 10 course series

Get in-depth knowledge of a subject

4.5

(38,687 reviews)

Beginner level

Recommended experience

7 months at 10 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

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

  • Navigate the entire data science pipeline from data acquisition to publication.

  • Use GitHub to manage data science projects.

  • Perform regression analysis, least squares and inference using regression models.

Details to know

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Taught in English

Specialization - 10 course series

Get in-depth knowledge of a subject

4.5

(38,687 reviews)

Beginner level

Recommended experience

7 months at 10 hours a week
Flexible schedule
Learn at your own pace

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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
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Specialization - 10 course series

The Data Scientist’s Toolbox

Course 118 hours4.6 (33,873 ratings)

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: Statistics
Category: Statistical Inference
Category: Statistical Hypothesis Testing

R Programming

Course 257 hours4.5 (22,213 ratings)

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: Random Forest
Category: Machine Learning (ML) Algorithms
Category: Machine Learning
Category: R Programming

Getting and Cleaning Data

Course 319 hours4.5 (8,055 ratings)

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 Science
Category: Github
Category: R Programming
Category: Rstudio

Exploratory Data Analysis

Course 454 hours4.7 (6,063 ratings)

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: Interactivity
Category: Plotly
Category: Web Application
Category: R Programming

Reproducible Research

Course 57 hours4.6 (4,168 ratings)

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: Data Science
Category: Machine Learning
Category: R Programming
Category: Natural Language Processing

Statistical Inference

Course 654 hours4.2 (4,428 ratings)

What you'll learn

  • Understand the process of drawing conclusions about populations or scientific truths from data

  • Describe variability, distributions, limits, and confidence intervals

  • Use p-values, confidence intervals, and permutation tests

  • Make informed data analysis decisions

Skills you'll gain

Category: Knitr
Category: Data Analysis
Category: R Programming
Category: Markup Language

Regression Models

Course 753 hours4.4 (3,344 ratings)

What you'll learn

  • Use regression analysis, least squares and inference

  • Understand ANOVA and ANCOVA model cases

  • Investigate analysis of residuals and variability

  • Describe novel uses of regression models such as scatterplot smoothing

Skills you'll gain

Category: Data Analysis
Category: Debugging
Category: R Programming
Category: Rstudio

Practical Machine Learning

Course 88 hours4.5 (3,246 ratings)

What you'll learn

  • Use the basic components of building and applying prediction functions

  • Understand concepts such as training and tests sets, overfitting, and error rates

  • Describe machine learning methods such as regression or classification trees

  • Explain the complete process of building prediction functions

Skills you'll gain

Category: Cluster Analysis
Category: Ggplot2
Category: R Programming
Category: Exploratory Data Analysis

Developing Data Products

Course 910 hours4.6 (2,254 ratings)

What you'll learn

  • Develop basic applications and interactive graphics using GoogleVis

  • Use Leaflet to create interactive annotated maps

  • Build an R Markdown presentation that includes a data visualization

  • Create a data product that tells a story to a mass audience

Skills you'll gain

Category: Data Manipulation
Category: Regular Expression (REGEX)
Category: R Programming
Category: Data Cleansing

Data Science Capstone

Course 105 hours4.5 (1,225 ratings)

What you'll learn

  • Create a useful data product for the public

  • Apply your exploratory data analysis skills

  • Build an efficient and accurate prediction model

  • Produce a presentation deck to showcase your findings

Skills you'll gain

Category: Model Selection
Category: Generalized Linear Model
Category: Linear Regression
Category: Regression Analysis

Instructors

Roger D. Peng, PhD
Johns Hopkins University
37 Courses1,587,513 learners
Brian Caffo, PhD
Johns Hopkins University
30 Courses1,612,987 learners
Jeff Leek, PhD
Johns Hopkins University
32 Courses1,638,446 learners

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