| 01.Data is messy.en.srt | 1.9 KB | ||
| 01.Data is messy.mp4 | 5.8 MB | ||
| 02.What you need to know.en.srt | 2.1 KB | ||
| 02.What you need to know.mp4 | 1.6 MB | ||
| 03.Types of missing data.en.srt | 5.9 KB | ||
| 03.Types of missing data.mp4 | 5.5 MB | ||
| 04.Missing values.en.srt | 20 KB | ||
| 04.Missing values.mp4 | 21.6 MB | ||
| 05.Missing rows.en.srt | 9.6 KB | ||
| 05.Missing rows.mp4 | 14.5 MB | ||
| 06.Aggregations and missing values.en.srt | 7.9 KB | ||
| 06.Aggregations and missing values.mp4 | 8.9 MB | ||
| 07.Duplicated rows and values.en.srt | 8.2 KB | ||
| 07.Duplicated rows and values.mp4 | 8.3 MB | ||
| 08.Aggregations in the data set.en.srt | 6.1 KB | ||
| 08.Aggregations in the data set.mp4 | 9 MB | ||
| 09.Converting dates.en.srt | 9.3 KB | ||
| 09.Converting dates.mp4 | 10.2 MB | ||
| 10.Unit conversions.en.srt | 6.5 KB | ||
| 10.Unit conversions.mp4 | 7.5 MB | ||
| 11.Numbers stored as text.en.srt | 5.7 KB | ||
| 11.Numbers stored as text.mp4 | 8.3 MB | ||
| 12.Text improperly converted to numbers.en.srt | 5.2 KB | ||
| 12.Text improperly converted to numbers.mp4 | 6.1 MB | ||
| 13.Inconsistent spellings.en.srt | 12.4 KB | ||
| 13.Inconsistent spellings.mp4 | 15.6 MB | ||
| 14.Screening for outliers.en.srt | 8.1 KB | ||
| 14.Screening for outliers.mp4 | 6.4 MB | ||
| 15.Handling outliers.en.srt | 3.5 KB | ||
| 15.Handling outliers.mp4 | 2.8 MB | ||
| 16.Outliers use case.en.srt | 5.8 KB | ||
| 16.Outliers use case.mp4 | 8 MB | ||
| 17.Outliers in subgroups.en.srt | 6.5 KB | ||
| 17.Outliers in subgroups.mp4 | 7.2 MB | ||
| 18.Detecting illogical values.en.srt | 5.8 KB | ||
| 18.Detecting illogical values.mp4 | 6 MB | ||
| 19.What is tidy data.en.srt | 6.4 KB | ||
| 19.What is tidy data.mp4 | 10.5 MB | ||
| 20.Variables, observations, and values.en.srt | 8.3 KB | ||
| 20.Variables, observations, and values.mp4 | 7.9 MB | ||
| 21.Common data problems.en.srt | 13.4 KB | ||
| 21.Common data problems.mp4 | 13.9 MB | ||
| 22.Wide vs. long data sets.en.srt | 5.9 KB | ||
| 22.Wide vs. long data sets.mp4 | 5.5 MB | ||
| 23.Making wide data sets long.en.srt | 8.3 KB | ||
| 23.Making wide data sets long.mp4 | 10.6 MB | ||
| 24.Making long data sets wide.en.srt | 6.6 KB | ||
| 24.Making long data sets wide.mp4 | 7.6 MB | ||
| 25.Suspicious values.en.srt | 8.2 KB | ||
| 25.Suspicious values.mp4 | 8.8 MB | ||
| 26.Suspicious multiples.en.srt | 4 KB | ||
| 26.Suspicious multiples.mp4 | 4.8 MB | ||
| 27.What's next.en.srt | 2.1 KB | ||
| 27.What's next.mp4 | 2.6 MB | ||
| Ex_Files_Cleaning_Bad_Data_R.zip | 39.8 MB | ||
| ▲ 55 total files | |||
Code:
Title: Cleaning Bad Data in R
Publisher: Lynda
Type: Big Data
URL: https://www.lynda.com/course-tutorials/Cleaning-Bad-Data-R/711824-2.html
Author: Mike Chapple
Duration: 1h 54m
Skill: Beginner
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