Coping with Missing, Invalid, and Duplicate Data in R

Focused View

Martin Burger

2:00:48

45 View

01 - Course Overview

01 - Course Overview.mp4

01:56

02 - Managing Duplicate Data

02 - Intro.mp4

01:13

03 - Managing Expectations.mp4

04:22

04 - Course Dataset.mp4

02:07

05 - Data Import.mp4

02:52

06 - Factors vs. Character Data.mp4

03:38

07 - Succession of Steps in Data Pre-processing.mp4

05:56

08 - Duplicate Data in R Base and dplyr.mp4

07:17

09 - Summary.mp4

01:15

03 - Managing Missing Data

10 - Intro.mp4

01:45

11 - Understanding Missing Values.mp4

06:20

12 - Quick and Simple Methods for Missing Values.mp4

05:09

13 - Imputation Methods.mp4

03:59

14 - Using visdat for NA Visualizations.mp4

02:24

15 - MICE for Missing Values.mp4

03:18

16 - Machine Learning for Missing Values.mp4

07:58

17 - Working on the Carparts Dataset.mp4

05:31

18 - Summary.mp4

01:28

04 - Outlier and Invalid Data Detection

19 - Intro.mp4

01:10

20 - Understanding Statistical Outliers.mp4

04:52

21 - Methods for Outlier Detection.mp4

05:22

22 - The 6 Sigma Rule.mp4

05:45

23 - The Boxplot Method.mp4

03:44

24 - Hypothesis Tests for Outliers.mp4

05:02

25 - Outliers in High Dimensionality.mp4

05:13

26 - Plausibility Checks and Replacement.mp4

04:38

27 - Summary.mp4

02:02

05 - Further Resources and Summary

28 - Intro.mp4

01:14

29 - Reproducibility in Pseudo Random Processes.mp4

02:51

30 - Data Pre-processing Task Views.mp4

05:17

31 - Course Summary.mp4

05:10

Description

Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.

What You'll Learn?

Data preparation is part of nearly any data analytics project, therefore the skills are highly valuable. In this course, Coping with Missing, Invalid, and Duplicate Data in R, you will learn the main steps of data preparation. First, you will learn how to handle duplicate data. Next, you will discover that missing values prevent a lot of R functions from working properly, therefore you are limited in your R toolset as long as you do not take care of all these NA's. Finally, you will explore outlier and invalid data detection and how they can introduce bias into your analysis. When you’re finished with this course, you will understand why missing values, outliers, and duplicates are problematic, how to detect them, and how to remove them from the dataset.

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R Programming

Martin Burger

Instructor's Courses

Martin studied biostatistics and worked for several pharmaceutical companies before he became a data science consultant and author. He published over 15 courses on R, Tableau 9 and other data science related subjects. His main focus lies on analytics software like R and SPSS but he is also interested in modern data visualization tools like Tableau. If he is not busy coding, blogging or working out new teaching concepts you may find him skiing or hiking in the Alps.

Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.