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.
Offered By
Johns Hopkins UniversityAbout this Course
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Learner Career Outcomes
37%
started a new career after completing these courses
37%
got a tangible career benefit from this course
13%
got a pay increase or promotion
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
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Approx. 20 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, English, Spanish
What you will 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 will gain
Data ManipulationRegular Expression (REGEX)R ProgrammingData Cleansing
Learner Career Outcomes
37%
started a new career after completing these courses
37%
got a tangible career benefit from this course
13%
got a pay increase or promotion
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Approx. 20 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Chinese (Simplified), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Offered by
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.
Syllabus - What you will learn from this course
2 hours to complete
Week 1
In this first week of the course, we look at finding data and reading different file types.
2 hours to complete
9 videos (Total 67 min), 4 readings, 1 quiz
1 hour to complete
Week 2
Welcome to Week 2 of Getting and Cleaning Data! The primary goal is to introduce you to the most common data storage systems and the appropriate tools to extract data from web or from databases like MySQL.
1 hour to complete
5 videos (Total 41 min)
11 hours to complete
Week 3
Welcome to Week 3 of Getting and Cleaning Data! This week the lectures will focus on organizing, merging and managing the data you have collected using the lectures from Weeks 1 and 2.
11 hours to complete
7 videos (Total 60 min), 1 reading, 4 quizzes
5 hours to complete
Week 4
Welcome to Week 4 of Getting and Cleaning Data! This week we finish up with lectures on text and date manipulation in R. In this final week we will also focus on peer grading of Course Projects.
5 hours to complete
5 videos (Total 34 min), 2 readings, 3 quizzes
Reviews
TOP REVIEWS FROM GETTING AND CLEANING DATA
by HSMay 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.
by BEOct 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.
by CWAug 6, 2020
I think that the level of difficulty of the exercises and final assignment does not match with the depth of the lectures; without a textbook, I feel lost, don't have a reference, and have to guess.
by ATNov 19, 2017
Very interesting and enjoyed doing the Assignment. but the assignment instructions are not clear.A lot of time was wasted trying to figure out what data is what are what are we interested in.
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