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.
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About this Course
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessWhat 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 Manipulation
- Regular Expression (REGEX)
- R Programming
- Data Cleansing
Could your company benefit from training employees on in-demand skills?
Try Coursera for BusinessOffered by
Syllabus - What you will learn from this course
Week 1
Week 2
Week 3
Week 4
Reviews
- 5 stars67.54%
- 4 stars23.63%
- 3 stars5.85%
- 2 stars1.62%
- 1 star1.33%
TOP REVIEWS FROM GETTING AND CLEANING DATA
The Swirl practice part is great! But there is a big gap between what we learned from video/swirl and the course project! The project is much harder than what I learn from the course.
This course is amazing! I have spent the majority of my time in R merely doing analytics. This course taught me the tools needed to go out and grab the data that I need for those analytics.
I found the last project insufficiently explained. I was struggling in understanding what the task is. A bit more clear task description (as in Course 2) would be really appreciated.
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.
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