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Martin Burger
Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.
Read more
Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.
Read more
Learn about the most essential steps of data preparation: Missing value imputation, outlier detection, and duplicate removal.
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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Data Preparation
Missing Value Imputation
Outlier Detection
Duplicate Data Removal
R Programming
Data Analysis
Register for this course and see more details by visiting:
OpenCourser.com/course/txqm5b/coping
What's inside
Syllabus
Course Overview
Managing Duplicate Data
Managing Missing Data
Outlier and Invalid Data Detection
Read more
Syllabus
Course Overview
Managing Duplicate Data
Managing Missing Data
Outlier and Invalid Data Detection
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Having a background in data analytics disciplines is recommended for this course
Can assist with the understanding of handling missing values, outliers, and duplicates in R
Managers, Data Scientists, Data Analysts, and those who implement and interpret data analytics pipelines and other such products may benefit from this course
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Reviews summary
Practical r for data preparation
According to learners, this course offers a solid and practical introduction to managing common data quality issues in R. Students frequently praise its clear explanations and hands-on examples, making it highly applicable for those new to data preparation or looking to solidify their R skills in this area. While the instructor's expertise and structured modules are consistently highlighted as positive traits, some suggest that more advanced imputation methods or real-world project-based exercises could further enhance the learning experience.
Good for beginners, but limited advanced depth.
"As a beginner, I found the pace just right for understanding the basics of data preparation in R."
"The course is an excellent introduction, but I felt it could benefit from more advanced topics and methods."
"It's a solid foundation, though experienced R users might find some parts too slow or basic for their needs."
"While thorough on fundamentals, I wished for a deeper dive into more complex imputation algorithms."
Knowledgeable instructor, effective teaching.
"The instructor clearly knows their stuff and conveys the information very effectively."
"I was impressed by the instructor's depth of knowledge and ability to simplify difficult concepts."
"Great instructor, very engaging and made learning about data cleaning interesting."
Concepts are explained with great clarity.
"The instructor explained complex topics like imputation methods in a very clear and understandable way."
"I really benefited from the step-by-step breakdown of each data quality challenge."
"The explanations were concise and to the point, making it easy to grasp the core ideas quickly."
Focuses on immediate application with R code.
"I found the practical R examples incredibly useful; I could apply them directly to my datasets at work."
"The course provides a very hands-on approach to data cleaning in R, which is exactly what I needed to improve my scripts."
"It gave me actionable code and strategies for dealing with messy data using R functions."
"I appreciated the direct application of R code to address missing, invalid, and duplicate data issues."
Could use more challenging practice.
"The exercises were helpful, but I would have liked more challenging scenarios to solidify my understanding."
"I felt the practice problems were a bit too simple; more complex, real-world datasets would be great."
"While the concepts are well-covered, there weren't enough opportunities for me to apply them in varied situations."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Coping with Missing, Invalid, and Duplicate Data in R with these
activities:
Work through RStudio tutorial
Show steps
Learn the basics of RStudio, the IDE used in the course.
Show steps
-
Follow the steps in the RStudio tutorial
-
Complete the practice exercises
Review Data Science for Business
Show steps
Introduce concepts and terminology related to the course.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
-
Read chapters 1-3
-
Complete review exercises at the end of each chapter
Connect with data scientists
Show steps
Seek guidance and advice from experienced professionals in the field.
Show steps
-
Attend industry events
-
Reach out to data scientists on LinkedIn
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Discuss course material and assignments with peers.
Show steps
-
Find a study group or form one
-
Meet regularly to discuss course material
Practice data cleaning in R
Show steps
Develop proficiency in data cleaning techniques taught in the course.
Show steps
-
Find and fix missing values
-
Identify and remove outliers
-
Handle duplicate data
Contribute to open source data science projects
Show steps
Gain practical experience and contribute to the community.
Show steps
-
Find open source projects on GitHub
-
Contribute code or documentation
Build a data cleaning pipeline
Show steps
Apply data cleaning skills to real-world data.
Show steps
-
Collect a dataset
-
Write R code to clean the data
-
Test and evaluate the pipeline
Participate in data science competitions
Show steps
Test skills and knowledge against other data scientists.
Show steps
-
Register for a competition
-
Build a model and submit it for evaluation
Present data cleaning findings
Show steps
Communicate the results of data cleaning efforts to stakeholders.
Show steps
-
Prepare slides or a presentation
-
Present the findings to an audience
Work through RStudio tutorial
Show steps
Learn the basics of RStudio, the IDE used in the course.
Show steps
Show steps
Show steps
- Follow the steps in the RStudio tutorial
- Complete the practice exercises
Review Data Science for Business
Show steps
Introduce concepts and terminology related to the course.
View
Data Science for Business: What You Need to...
on Amazon
Show steps
Show steps
Show steps
- Read chapters 1-3
- Complete review exercises at the end of each chapter
Connect with data scientists
Show steps
Seek guidance and advice from experienced professionals in the field.
Show steps
Show steps
Show steps
- Attend industry events
- Reach out to data scientists on LinkedIn
Six other activities
Expand to see all activities and additional details
Show all nine activities
Join a study group
Show steps
Discuss course material and assignments with peers.
Show steps
Show steps
Show steps
- Find a study group or form one
- Meet regularly to discuss course material
Practice data cleaning in R
Show steps
Develop proficiency in data cleaning techniques taught in the course.
Show steps
Show steps
Show steps
- Find and fix missing values
- Identify and remove outliers
- Handle duplicate data
Contribute to open source data science projects
Show steps
Gain practical experience and contribute to the community.
Show steps
Show steps
Show steps
- Find open source projects on GitHub
- Contribute code or documentation
Build a data cleaning pipeline
Show steps
Apply data cleaning skills to real-world data.
Show steps
Show steps
Show steps
- Collect a dataset
- Write R code to clean the data
- Test and evaluate the pipeline
Participate in data science competitions
Show steps
Test skills and knowledge against other data scientists.
Show steps
Show steps
Show steps
- Register for a competition
- Build a model and submit it for evaluation
Present data cleaning findings
Show steps
Communicate the results of data cleaning efforts to stakeholders.
Show steps
Show steps
Show steps
- Prepare slides or a presentation
- Present the findings to an audience
Career center
Learners who complete Coping with Missing, Invalid, and Duplicate Data in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
As a Data Analyst, you will be responsible for understanding data, solving business problems, and communicating results. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the data analysis process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to produce more accurate and reliable results, which can lead to better decision-making.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, programming, and machine learning to extract insights from data. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the data science process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to produce more accurate and reliable results, which can lead to better decision-making.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the data engineering process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to build more reliable and efficient data systems.
Statistician
Statistician
Statisticians use their knowledge of mathematics and statistics to collect, analyze, and interpret data. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the statistical analysis process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to produce more accurate and reliable results, which can lead to better decision-making.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the machine learning process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to build more accurate and reliable machine learning models.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and data analysis to solve business problems. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the business analysis process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to produce more accurate and reliable results, which can lead to better decision-making.
Data Architect
Data Architect
Data Architects design and build data systems. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the data architecture process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to build more reliable and efficient data systems.
Database Administrator
Database Administrator
Database Administrators design, build, and maintain databases. This course, Coping with Missing, Invalid, and Duplicate Data in R, will provide you with the necessary skills to prepare data for analysis, which is a crucial step in the database administration process. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This will help you to build more reliable and efficient databases.
Software Engineer
Software Engineer
Software Engineers design, build, and maintain software applications. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for preparing data for analysis, which is a common task in software development. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to write more reliable and efficient software applications.
Web Developer
Web Developer
Web Developers design, build, and maintain websites. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for preparing data for analysis, which is a common task in web development. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to write more reliable and efficient websites.
Analyst
Analyst
Analysts use their knowledge of business and data analysis to solve business problems. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for preparing data for analysis, which is a common task in many analyst roles. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to produce more accurate and reliable results, which can lead to better decision-making.
Consultant
Consultant
Consultants use their knowledge of business and data analysis to help clients solve business problems. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for preparing data for analysis, which is a common task in consulting. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to produce more accurate and reliable results, which can lead to better decision-making.
Project Manager
Project Manager
Project Managers plan, execute, and close projects. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for managing data in projects. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to make better decisions and manage projects more effectively.
Product Manager
Product Manager
Product Managers plan, develop, and launch products. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for managing data in product development. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to make better decisions and launch more successful products.
Operations Manager
Operations Manager
Operations Managers plan, execute, and control operations. This course, Coping with Missing, Invalid, and Duplicate Data in R, may provide you with some useful skills for managing data in operations. By learning how to handle missing values, outliers, and duplicates, you will be able to ensure that your data is clean and ready for analysis. This can help you to make better decisions and manage operations more effectively.
For more career information including salaries, visit:
OpenCourser.com/course/txqm5b/coping
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Coping with Missing, Invalid, and Duplicate Data in R.
Covers data analysis techniques in R, including data preparation, data visualization, and statistical analysis. It provides a comprehensive overview of the topic and is written in a clear and concise style.
Provides a comprehensive overview of data science, including data preparation, modeling, and communication. It is written in a clear and concise style and includes many examples.
Provides a comprehensive overview of data science, including data preparation, modeling, and communication. It is written in a clear and concise style and includes many examples.
Covers the entire data science pipeline, including data preparation, modeling, and communication. It provides a comprehensive overview of the topic and is written in a clear and engaging style.
Provides a comprehensive guide to machine learning in R, including techniques for dealing with missing data, outliers, and duplicate data. It is written in a clear and concise style and includes many examples.
Covers machine learning techniques, including data preparation, model selection, and evaluation. It provides a comprehensive overview of the topic and is written in a clear and concise style.
Covers data science techniques for business users, including data preparation, data mining, and predictive modeling. It provides a practical guide to the topic and is written in a clear and concise style.
Covers statistical methods for data analysis, including data preparation, exploratory data analysis, and inferential statistics. It provides a comprehensive overview of the topic and is written in a clear and concise style.
Save
Covers data mining techniques, including data preparation, feature selection, and model evaluation. It provides a comprehensive overview of the topic and is written in a clear and concise style.
Save
Provides a practical guide to data wrangling in R, including techniques for dealing with missing data, outliers, and duplicate data. It is written in a clear and concise style and includes many examples.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/txqm5b/coping
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Time
121 hours
Level
Intermediate
Via
Pluralsight
Instructor
Martin Burger
Language
English
This course belongs to a
collection
called
Cleaning Data with R. It's comprised of four courses:
- Querying and Converting Data Types in R
- Manipulating Dataframes in R
- Coping with Missing, Invalid, and Duplicate Data in R (viewing)
- Manipulating String Data in R
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