Об этом курсе
4.7
3,847 ratings
578 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....
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Начните сейчас и учитесь по собственному графику.
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Предполагаемая нагрузка: 5 hours/week

Прибл. 15 ч. на завершение
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English

Субтитры: English, Chinese (Simplified)

Чему вы научитесь

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

Приобретаемые навыки

Exploratory Data AnalysisGgplot2R ProgrammingCluster Analysis
Globe

Только онлайн-курсы

Начните сейчас и учитесь по собственному графику.
Calendar

Гибкие сроки

Назначьте сроки сдачи в соответствии со своим графиком.
Clock

Предполагаемая нагрузка: 5 hours/week

Прибл. 15 ч. на завершение
Comment Dots

English

Субтитры: English, Chinese (Simplified)

Программа курса: что вы изучите

1

Раздел
Clock
20 ч. на завершение

Week 1

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. ...
Reading
15 видео (всего 109 мин.), 6 материалов для самостоятельного изучения, 7 тестов
Video15 видео
Installing R on Windows (3.2.1)3мин
Installing R on a Mac (3.2.1)1мин
Installing R Studio (Mac)3мин
Setting Your Working Directory (Windows)7мин
Setting Your Working Directory (Mac)7мин
Principles of Analytic Graphics12мин
Exploratory Graphs (part 1)9мин
Exploratory Graphs (part 2) 5мин
Plotting Systems in R9мин
Base Plotting System (part 1)11мин
Base Plotting System (part 2)6мин
Base Plotting Demonstration16мин
Graphics Devices in R (part 1)5мин
Graphics Devices in R (part 2)7мин
Reading6 материала для самостоятельного изучения
Welcome to Exploratory Data Analysis10мин
Syllabus10мин
Pre-Course Survey10мин
Exploratory Data Analysis with R Book10мин
The Art of Data Science10мин
Practical R Exercises in swirl Part 110мин
Quiz1 практическое упражнение
Week 1 Quiz20мин

2

Раздел
Clock
17 ч. на завершение

Week 2

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....
Reading
7 видео (всего 61 мин.), 1 материал для самостоятельного изучения, 6 тестов
Video7 видео
Lattice Plotting System (part 2)6мин
ggplot2 (part 1)6мин
ggplot2 (part 2)13мин
ggplot2 (part 3)9мин
ggplot2 (part 4)10мин
ggplot2 (part 5)8мин
Reading1 материал для самостоятельного изучения
Practical R Exercises in swirl Part 210мин
Quiz1 практическое упражнение
Week 2 Quiz20мин

3

Раздел
Clock
13 ч. на завершение

Week 3

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....
Reading
12 видео (всего 77 мин.), 1 материал для самостоятельного изучения, 4 тестов
Video12 видео
Hierarchical Clustering (part 2)5мин
Hierarchical Clustering (part 3)7мин
K-Means Clustering (part 1)5мин
K-Means Clustering (part 2)4мин
Dimension Reduction (part 1)7мин
Dimension Reduction (part 2)9мин
Dimension Reduction (part 3)6мин
Working with Color in R Plots (part 1)4мин
Working with Color in R Plots (part 2)7мин
Working with Color in R Plots (part 3)6мин
Working with Color in R Plots (part 4)3мин
Reading1 материал для самостоятельного изучения
Practical R Exercises in swirl Part 310мин

4

Раздел
Clock
6 ч. на завершение

Week 4

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. ...
Reading
2 видео (всего 55 мин.), 2 материалов для самостоятельного изучения, 2 тестов
Video2 видео
Air Pollution Case Study40мин
Reading2 материала для самостоятельного изучения
Practical R Exercises in swirl Part 410мин
Post-Course Survey10мин
4.7
Direction Signs

37%

начал новую карьеру, пройдя эти курсы
Briefcase

83%

получил значимые преимущества в карьере благодаря этому курсу
Money

18%

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Лучшие рецензии

автор: YSep 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!

автор: CCJul 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.

Преподавателя

Roger D. Peng, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Jeff Leek, PhD

Associate Professor, Biostatistics
Bloomberg School of Public Health

Brian Caffo, PhD

Professor, Biostatistics
Bloomberg School of Public Health

О Johns Hopkins University

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....

О специализации ''Data Science'

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material....
Data Science

Часто задаваемые вопросы

  • 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.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

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