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By the end of this project, you will create a data quality report file (exported to Excel in CSV format) from a dataset loaded in R, a free, open-source program that you can download. You will learn how to use the following descriptive statistical metrics in order to describe a dataset and how to calculate them in basic R with no additional libraries.
- minimum value
- maximum value
- average value
- standard deviation
- total number of values
- missing values
- unique values
- data types
You will then learn how to record the statistical metrics for each column of a dataset using a custom function created by you in R. The output of the function will be a ready-to-use data quality report. Finally, you will learn how to export this report to an external file.
A data quality report can be used to identify outliers, missing values, data types, anomalies, etc. that are present in your dataset. This is the first step to understand your dataset and let you plan what pre-processing steps are required to make your dataset ready for analysis.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
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1 - 8 de 8 revisiones para Using Descriptive Statistics to Analyze Data in R
por Rajkumar R
•18 de jul. de 2020
Enjoyed practicing
por Jesus M Z F
•20 de jul. de 2020
Good course
por Simbarashe W
•19 de jul. de 2020
It was a bit challenging to follow towards the end, but overall it was a good project.
por Rodrigo G Z
•28 de ago. de 2020
Concise and direct with cool tips on the way
por TUSHAR R
•2 de oct. de 2020
I explore new things there and learn new thing.
por Karlo A L
•6 de oct. de 2020
It was easy enough to follow.
por Surbhi K
•7 de oct. de 2020
this courseis nice
por Deepak K
•5 de nov. de 2020
Thank You
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