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学生对 约翰霍普金斯大学 提供的 获取和整理数据 的评价和反馈

4.6
7,813 个评分
1,276 条评论

课程概述

Before you can work with data you have to get some. This course will cover the basic ways that data can be obtained. The course will cover obtaining data from the web, from APIs, from databases and from colleagues in various formats. It will also cover the basics of data cleaning and how to make data “tidy”. Tidy data dramatically speed downstream data analysis tasks. The course will also cover the components of a complete data set including raw data, processing instructions, codebooks, and processed data. The course will cover the basics needed for collecting, cleaning, and sharing data....

热门审阅

HS
May 2, 2020

This course provides an introduction of some important concepts and tools on a very important aspect of data science: cleaning and organizing data before any analysis. A must for any data scientist.

BE
Oct 25, 2016

This course is really a challenging and compulsory for any one who wants to be a data scientist or working in any sort of data. It teaches you how to make very palatable data-set fro ma messy data.

筛选依据:

251 - 获取和整理数据 的 275 个评论(共 1,238 个)

创建者 Rosane H G

Sep 7, 2020

Very good and hands-on course for those willing to work with database construction and analysis.

创建者 juan p d

May 15, 2016

i apprecciate the learning by doing and the lot of support that the staff and classmates provide

创建者 Manuel M M

Sep 9, 2019

Very practical and useful. I really enjoyed doing the test and learning a lot of R programming

创建者 Sérgio A V G P

Jun 6, 2016

Excelent course. It is a great oportunity to develop the aknowkedge in R and System analysis.

创建者 Hector M

May 19, 2016

Great way to exercise and practice something that is often neglected in data science courses.

创建者 Alfonso L

Mar 19, 2020

Need to take some time to learn. A lot of stuff in so short period of time but really worthy

创建者 Hariharan D

Jul 13, 2017

Excellent course. Interesting to learn through this experiential hands-on pedagogy approach.

创建者 Rodrigo C

Oct 11, 2017

Se enseña el uso de herramientas muy útiles a la hora de transformar raw data en tidy data.

创建者 Jhon A R G

Aug 3, 2020

Es un curso que no se hace pesado, ya que se explican las temáticas con mucha naturalidad.

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 José E T

Aug 5, 2017

Very good course. Taught at the correct depth. He added a lot of knowledge to my training.

创建者 Eric F

Nov 6, 2016

Excellent cours pour la mise en œuvre de méthodes robustes sur la préparation des données.

创建者 Gayathri N

May 7, 2020

Good course that helped to get the skill of reading and different ways to work with Data.

创建者 Кирилл С

Sep 27, 2018

Helped a lot form my understanding of a good workflow for data collection and processing.

创建者 Lee W

Oct 4, 2017

Great course to introduce you to the various methods of loading and processing data in R.

创建者 Ryan H

Sep 23, 2017

I really enjoyed this course. The instructors were good and the assignment was enjoyable.

创建者 Alessandro P

Feb 9, 2016

Great course!

I think that cleaning the data in the right way is the key of data science.

创建者 Patrícia A F

Apr 6, 2019

Great Course for you to learn in more detail how data processing works in Data Science.

创建者 Rimi Z

Jun 22, 2018

A great course and you learn exactly what the title says . I found it fun to work on It

创建者 Tine M

Nov 14, 2017

Great course, I've learned a lot about analyzing data sets and creating tidy data sets.

创建者 Robert K

May 1, 2017

Fantastic! I find myself using the information I learned in the class on a daily basis.

创建者 Boris B

Feb 6, 2016

Great Course if want to know what data really is and how to handle it in the right way.

创建者 Maxim S

Aug 26, 2020

Thanks, this course was great. It gave me enough knowledge and motivation to go forth.

创建者 Alia E

Feb 27, 2017

Wasn't excited about it. But I've used and re-used the materials for work. Good stuff.

创建者 Tseliso I M

Feb 2, 2017

Of the courses I have done so far in the specialization, this was the most practical.