github
/
developer-roadmap
mirror of https://github.com/kamranahmedse/developer-roadmap.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Addition: [roadmaps/DataAnalyst] Add article for Finding Outliers (#5999)

Browse Source
pull/6010/head
Amirali Toori 5 months ago committed by GitHub
parent a347c1739b
commit e5ee35acee
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 5 additions and 1 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 4
      src/data/roadmaps/data-analyst/content/107-data-cleaning/102-finding-outliers.md

4
src/data/roadmaps/data-analyst/content/107-data-cleaning/102-finding-outliers.md
Unescape View File

@ -1,3 +1,7 @@
# Finding Outliers # Finding Outliers
In the field of data analysis, data cleaning is an essential and preliminary step. This process involves correcting or removing any errors, inaccuracy, or irrelevance present in the obtained raw data, making it more suitable for analysis. One crucial aspect of this process is "finding outliers". Outliers are unusual or surprising data points that deviate significantly from the rest of the data. While they may be the result of mere variability or error, they will often pull the aggregate data towards them, skewing the results and impeding the accuracy of data analysis. Therefore, identifying and appropriately handling these outliers is crucial to ensure the reliability of subsequent data analysis tasks. In the field of data analysis, data cleaning is an essential and preliminary step. This process involves correcting or removing any errors, inaccuracy, or irrelevance present in the obtained raw data, making it more suitable for analysis. One crucial aspect of this process is "finding outliers". Outliers are unusual or surprising data points that deviate significantly from the rest of the data. While they may be the result of mere variability or error, they will often pull the aggregate data towards them, skewing the results and impeding the accuracy of data analysis. Therefore, identifying and appropriately handling these outliers is crucial to ensure the reliability of subsequent data analysis tasks.
Visit the following resources to learn more:
- [@article@Outliers]([https://www.mathsisfun.com/data/outliers.html)

Write Preview
Loading…
Cancel
Save