#1 Specialization

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

Johns Hopkins University

4.7

3,638 ratings

•

550 reviews

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

- Apply cluster analysis techniques to locate patterns in data
- Make graphical displays of very high dimensional data
- Understand analytic graphics and the base plotting system in R
- Use advanced graphing systems such as the Lattice system

R ProgrammingCluster AnalysisData VisualizationData Analysis

Section

This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already. ...

15 videos (Total 109 min), 6 readings, 7 quizzes

Installing R on Windows (3.2.1)3m

Installing R on a Mac (3.2.1)1m

Installing R Studio (Mac)3m

Setting Your Working Directory (Windows)7m

Setting Your Working Directory (Mac)7m

Principles of Analytic Graphics12m

Exploratory Graphs (part 1)9m

Exploratory Graphs (part 2) 5m

Plotting Systems in R9m

Base Plotting System (part 1)11m

Base Plotting System (part 2)6m

Base Plotting Demonstration16m

Graphics Devices in R (part 1)5m

Graphics Devices in R (part 2)7m

Welcome to Exploratory Data Analysis10m

Syllabus10m

Pre-Course Survey10m

Exploratory Data Analysis with R Book10m

The Art of Data Science10m

Practical R Exercises in swirl Part 110m

Week 1 Quiz20m

Section

Welcome to Week 2 of Exploratory Data Analysis. This week covers some of the more advanced graphing systems available in R: the Lattice system and the ggplot2 system. While the base graphics system provides many important tools for visualizing data, it was part of the original R system and lacks many features that may be desirable in a plotting system, particularly when visualizing high dimensional data. The Lattice and ggplot2 systems also simplify the laying out of plots making it a much less tedious process....

7 videos (Total 61 min), 1 reading, 6 quizzes

Lattice Plotting System (part 2)6m

ggplot2 (part 1)6m

ggplot2 (part 2)13m

ggplot2 (part 3)9m

ggplot2 (part 4)10m

ggplot2 (part 5)8m

Practical R Exercises in swirl Part 210m

Week 2 Quiz20m

Section

Welcome to Week 3 of Exploratory Data Analysis. This week covers some of the workhorse statistical methods for exploratory analysis. These methods include clustering and dimension reduction techniques that allow you to make graphical displays of very high dimensional data (many many variables). We also cover novel ways to specify colors in R so that you can use color as an important and useful dimension when making data graphics. All of this material is covered in chapters 9-12 of my book Exploratory Data Analysis with R....

12 videos (Total 77 min), 1 reading, 4 quizzes

Hierarchical Clustering (part 2)5m

Hierarchical Clustering (part 3)7m

K-Means Clustering (part 1)5m

K-Means Clustering (part 2)4m

Dimension Reduction (part 1)7m

Dimension Reduction (part 2)9m

Dimension Reduction (part 3)6m

Working with Color in R Plots (part 1)4m

Working with Color in R Plots (part 2)7m

Working with Color in R Plots (part 3)6m

Working with Color in R Plots (part 4)3m

Practical R Exercises in swirl Part 310m

Section

This week, we'll look at two case studies in exploratory data analysis. The first involves the use of cluster analysis techniques, and the second is a more involved analysis of some air pollution data. How one goes about doing EDA is often personal, but I'm providing these videos to give you a sense of how you might proceed with a specific type of dataset. ...

2 videos (Total 55 min), 2 readings, 2 quizzes

Practical R Exercises in swirl Part 410m

Post-Course Survey10m

4.7

started a new career after completing these courses

got a tangible career benefit from this course

got a pay increase or promotion

By CC•Jul 29th 2016

This is the second course I have taken from Roger Peng and both were outstanding. I have a strong math background, but not much of a background in stats, but this course was very approachable for me.

By Y•Sep 24th 2017

Very good course! It provide me the foundation in learning how to plot and interpret data. This will definitely strengthen my "R programming" to generate publication type figure for my genomics data!

The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world....

When will I have access to the lectures and assignments?

Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

What will I get if I pay for this course?

If you pay for this course, you will have access to all of the features and content you need to earn a Course Certificate. If you complete the course successfully, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. Note that the Course Certificate does not represent official academic credit from the partner institution offering the course.

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