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学生对 Johns Hopkins University 提供的 Exploratory Data Analysis 的评价和反馈

4.7
6,085 个评分

课程概述

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....

热门审阅

YF

Sep 23, 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!

CC

Jul 28, 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.

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176 - Exploratory Data Analysis 的 200 个评论(共 864 个)

创建者 Vincent G

Jul 3, 2017

very good and insightful course delivered at the right level with good examples and excersises

创建者 Wayne H

Mar 3, 2017

Good introduction to graphics systems and principles. Practical exercises are well conceived.

创建者 Jose L S V

Mar 16, 2021

Muy buen curso para comenzar a utilizar las herramientas de R para explorar y analizar datos.

创建者 Jamie M

Jul 28, 2018

Thoroughly enjoyable with an excellent method of measuring the learning outcomes at the end.

创建者 Atul G

Jan 29, 2018

It helps lerner to know whether the capture data can help him to get answer to his question.

创建者 Alberto G

Sep 12, 2018

Very interesting and useful for its applicability to a lot of different professional areas.

创建者 Ricardo R

Oct 24, 2016

Excelent course! Very direct and informative, leaving many doors open to individual quests.

创建者 Ghazouan S

Oct 2, 2016

I love all those courses, I have learned so much and I'm planning to complete all 10 units.

创建者 Pratama A A

Jul 23, 2020

I encourage to read the book ,and learn the unsupervised machine learning algortihm better

创建者 Thiago M

Aug 12, 2019

Course material and projects help a lot in learning and improving data analysis techniques

创建者 Gat T

Jan 26, 2019

I really liked this course. I felt like a learnt a lot especially with all of the projects

创建者 Damian

Jun 10, 2017

This was the course that I've enjoyed it the most. Especially the data visualization part

创建者 MIGUEL A S R

Apr 22, 2021

Great course with important lessons. Thanks for the lessons and the information provided.

创建者 Anusha V

May 30, 2017

Amazing course. challenging and you learn a LOT (particularly about graphics tools in R).

创建者 Nishchal S

May 17, 2016

Some functions are so tough to remember but general ideas that i received is wonder full.

创建者 Ray W

Feb 22, 2016

Pretty useful introduction to various plotting tools and some deep down insights as well.

创建者 Isaac C

Mar 12, 2018

Im really learning because of the exercises, they are very efficient!!

congratz coursera

创建者 niki a

Mar 13, 2017

Nice course! Workload was just about right. clear lectures and well-designed projects.

创建者 Gabriella J

Feb 26, 2017

Great course! It have been very useful for my job as data scientist! Thank u very much!

创建者 Andres D C

May 27, 2021

This is essential for every posterior course, take care of understanding the concepts.

创建者 Mumbi M

Mar 18, 2022

An excellent course and very informative. I have learnt a lot of new things from this

创建者 Thomas

Jan 15, 2020

As an auditor, this course taught me to see things more clearly from different angle.

创建者 Кирилл С

Jan 23, 2019

This is a great course on plotting data as well as finding underlying patterns in it.

创建者 Nannette S

Apr 16, 2018

Great course. The case studies are extremely helpful as well as the SWIRL exercises.

创建者 Warwick T

Jun 15, 2017

Very enjoyable course although I think the workload could be more evenly distributed.