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.
This course is part of the Data Science Specialization
Getting and Cleaning Data
4.6
(5,977 ratings)
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About this Course
107,468
107,468What you will learn
Apply data cleaning basics to make data "tidy"
Obtain usable data from the web, APIs, and databases
Understand common data storage systems
Use R for text and date manipulation
Skills you will gain
Data ManipulationRegular Expression (REGEX)R ProgrammingData Cleansing
Syllabus - What you will learn from this course
Week
12 hours to complete
Week 1
In this first week of the course, we look at finding data and reading different file types.
9 videos (Total 67 min), 4 readings, 1 quiz
9 videos
Raw and Processed Data7m
Components of Tidy Data9m
Downloading Files7m
Reading Local Files4m
Reading Excel Files3m
Reading XML12m
Reading JSON5m
The data.table Package11m
4 readings
Welcome to Week 110m
Syllabus10m
Pre-Course Survey10m
Practical R Exercises in swirl Part 110m
1 practice exercise
Week 1 Quiz10m
Week
21 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.
5 videos (Total 41 min), 1 quiz
5 videos
Reading from HDF56m
Reading from The Web6m
Reading From APIs7m
Reading From Other Sources4m
1 practice exercise
Week 2 Quiz10m
Week
310 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.
7 videos (Total 60 min), 1 reading, 4 quizzes
7 videos
Summarizing Data11m
Creating New Variables10m
Reshaping Data9m
Managing Data Frames with dplyr - Introduction3m
Managing Data Frames with dplyr - Basic Tools12m
Merging Data6m
1 reading
Practical R Exercises in swirl Part 210m
1 practice exercise
Week 3 Quiz10m
Week
46 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 videos (Total 34 min), 2 readings, 3 quizzes
5 videos
Regular Expressions I5m
Regular Expressions II8m
Working with Dates6m
Data Resources3m
2 readings
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
1 practice exercise
Week 4 Quiz10m
4.6
928 Reviews41%
started a new career after completing these courses
36%
got a tangible career benefit from this course
Top Reviews
Easy, mostly instructive Course. The Assignments and quizzes are quite good, and illustrates the lessons very well.\n\nSee the videos for general presentation, but use the energy on the excersizes.
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.
Instructors
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
About 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.
About the Data Science Specialization
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.
Frequently Asked Questions
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 subscribe to this Specialization?
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.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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