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Emilee McWilliams
Ready to understand table analysis in R? This course will teach you how to code and create important data tables in R.
Read more
Ready to understand table analysis in R? This course will teach you how to code and create important data tables in R.
Read more
Ready to understand table analysis in R? This course will teach you how to code and create important data tables in R.
Quickly understanding variables in a dataset can be time-consuming. In this course, Building Tables in R, you’ll learn to develop tables, proportions, and marginal frequencies in R. First, you’ll explore the table function with two- and three-way tables. Next, you’ll discover proportions for these tables. Finally, you’ll learn how to build a marginal frequency table. When you’re finished with this course, you’ll have the skills and knowledge of developing tables for data in R needed to understand customer data and prepare for advanced analysis.
This course is no longer available. Find something similar by browsing:
R
Table Analysis
Data Tables
Proportions
Marginal Frequency
Data Visualization
Register for this course and see more details by visiting:
OpenCourser.com/course/jdbymv/building
What's inside
Syllabus
Course Overview
Creating a Two-way and a Three-way Frequency Table in R
Creating Proportions for a Table in R
Developing Marginal Frequency Table in R
Read more
Syllabus
Course Overview
Creating a Two-way and a Three-way Frequency Table in R
Creating Proportions for a Table in R
Developing Marginal Frequency Table in R
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Strengthens an existing foundation for intermediate learners
Helps learners do their work more quickly
Develops core skills for data analysis
Taught by Emilee McWilliams, who is recognized for their work in data analysis
Examines data analysis, which is highly relevant to data analysis
Uses R, which is standard in the data analysis industry
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Reviews summary
Practical r tables for data analysis
According to learners, this course offers a clear and concise overview of building tables in R, making it particularly useful for those looking for practical application in data analysis. Students find the instructor's explanations to be highly digestible, aiding in quickly grasping how to interpret data, especially for customer data analysis. While many praise its to-the-point approach and effectiveness for quick skill acquisition, some suggest it might be too basic for experienced R users or those seeking deeper statistical context, occasionally requiring external resources for true beginners. Overall, it provides a solid foundation for data summarization.
Commended for its directness and efficient teaching of table building.
"The course is concise and gets straight to the point without unnecessary fluff."
"Excellent and to the point. This course focuses specifically on building tables, and it does that very well."
"If you're looking for a quick and effective way to master table creation in R, this is it."
Praised for clear explanations and immediate real-world applicability.
"The instructor's explanations were incredibly clear, especially when covering two-way and three-way frequency tables."
"I learned practical skills that I can apply immediately in my job."
"Very practical for data summary. It definitely helps in understanding variables quickly, which is crucial for my work."
Some felt the need for additional context or practice exercises.
"I had to look up external resources for clearer understanding of some statistical concepts behind the tables."
"My only minor feedback is that a few more challenge exercises would have been beneficial."
"More case studies would improve it. Good for a quick overview, but not for comprehensive understanding."
Mixed reviews on suitability for true beginners; some seek more depth.
"I felt the instructor assumed too much prior knowledge of R or statistics. As a true beginner, I struggled with some of the concepts..."
"I wish there were more advanced use cases or discussions on how these tables fit into larger data analysis workflows."
"It covers the fundamentals well, making it suitable for beginners, but might be a bit too basic for those with prior R experience looking for depth."
"Felt like it barely scratched the surface... don't expect deep dives."
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 Building Tables with R with these
activities:
Refresh your knowledge of R basics
Show steps
Refreshing your knowledge of R basics will help you to better understand the material covered in this course.
Browse courses on
R Programming
Show steps
-
Review the R documentation.
-
Complete a few online tutorials on R basics.
-
Practice writing R code.
Review R for Data Science
Show steps
Reviewing this book will provide a solid foundation in R programming, which is essential for understanding table analysis in R covered in this course.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the first three chapters of the book.
-
Complete the exercises at the end of each chapter.
-
Create a small dataset and practice manipulating it using the techniques described in the book.
Join a study group to help you with this course
Show steps
Joining a study group will allow you to connect with other students who are taking this course and can provide support and assistance.
Show steps
-
Find a study group that meets your needs.
-
Attend the study group meetings regularly.
-
Participate in the discussions and ask questions.
Two other activities
Expand to see all activities and additional details
Show all five activities
Volunteer at a local data science organization
Show steps
Volunteering at a local data science organization will allow you to gain hands-on experience with table analysis in R.
Show steps
-
Find a local data science organization that you are interested in volunteering for.
-
Contact the organization and inquire about volunteer opportunities.
-
Attend the volunteer training.
-
Volunteer at the organization on a regular basis.
Contribute to an open-source project related to table analysis in R
Show steps
Contributing to an open-source project related to table analysis in R will allow you to learn from other developers and gain experience with real-world projects.
Show steps
-
Find an open-source project related to table analysis in R that you are interested in contributing to.
-
Contact the project maintainers and inquire about how you can contribute.
-
Make a contribution to the project.
-
Submit your contribution to the project.
Refresh your knowledge of R basics
Show steps
Refreshing your knowledge of R basics will help you to better understand the material covered in this course.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Review the R documentation.
- Complete a few online tutorials on R basics.
- Practice writing R code.
Review R for Data Science
Show steps
Reviewing this book will provide a solid foundation in R programming, which is essential for understanding table analysis in R covered in this course.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book.
- Complete the exercises at the end of each chapter.
- Create a small dataset and practice manipulating it using the techniques described in the book.
Join a study group to help you with this course
Show steps
Joining a study group will allow you to connect with other students who are taking this course and can provide support and assistance.
Show steps
Show steps
Show steps
- Find a study group that meets your needs.
- Attend the study group meetings regularly.
- Participate in the discussions and ask questions.
Two other activities
Expand to see all activities and additional details
Show all five activities
Volunteer at a local data science organization
Show steps
Volunteering at a local data science organization will allow you to gain hands-on experience with table analysis in R.
Show steps
Show steps
Show steps
- Find a local data science organization that you are interested in volunteering for.
- Contact the organization and inquire about volunteer opportunities.
- Attend the volunteer training.
- Volunteer at the organization on a regular basis.
Contribute to an open-source project related to table analysis in R
Show steps
Contributing to an open-source project related to table analysis in R will allow you to learn from other developers and gain experience with real-world projects.
Show steps
Show steps
Show steps
- Find an open-source project related to table analysis in R that you are interested in contributing to.
- Contact the project maintainers and inquire about how you can contribute.
- Make a contribution to the project.
- Submit your contribution to the project.
Career center
Learners who complete Building Tables with R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use tables to organize and summarize data for use by other professionals. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Business Analyst
Business Analyst
Business Analysts use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Statistician
Statistician
Statisticians use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Data Scientist
Data Scientist
Data Scientists use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Software Engineer
Software Engineer
Software Engineers use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Product Manager
Product Manager
Product Managers use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Project Manager
Project Manager
Project Managers use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Financial Analyst
Financial Analyst
Financial Analysts use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Investment Analyst
Investment Analyst
Investment Analysts use tables to organize and understand complex data. A strong foundation in building tables is an essential skill for this role. This course will help you prepare for this career by teaching you how to organize data into tables for easier analysis.
Market Research Analyst
Market Research Analyst
A strong foundation in building tables can be helpful for Market Research Analysts, who use tables to organize and understand complex data. This course will help you build the foundational skills you need to create different types of tables and work with data more efficiently.
Data Journalist
Data Journalist
A strong foundation in building tables may be useful for Data Journalists, who use tables to organize and understand complex data. This course will help you build the foundational skills you need to create different types of tables and work with data more efficiently.
Technical Writer
Technical Writer
A strong foundation in building tables may be useful for Technical Writers, who often use tables to organize and present complex data. This course will help you build the foundational skills you need to create different types of tables and work with data more efficiently.
For more career information including salaries, visit:
OpenCourser.com/course/jdbymv/building
Reading list
We've selected 14 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
Building Tables with R.
Save
Provides a comprehensive introduction to R, the programming language for data science. It covers all the basics of R, including data import, manipulation, and visualization. The book is especially useful for beginners who want to learn how to use R for data analysis.
More advanced guide to R programming. It covers topics such as data structures, algorithms, and object-oriented programming. The book is especially useful for experienced R programmers who want to learn more about the language.
Save
Collection of recipes for common tasks in R. It useful reference for both beginners and experienced R programmers.
Classic textbook on applied statistics. It covers a wide range of topics, including data exploration, statistical modeling, and hypothesis testing. The book is especially useful for students and researchers who want to learn more about statistical methods.
Comprehensive introduction to statistical data analysis. It covers a wide range of topics, including data collection, data exploration, and statistical inference. The book is especially useful for students and researchers who want to learn more about statistical methods.
Save
Practical guide to data analysis with R. It covers a wide range of topics, including data import, manipulation, and visualization. The book is especially useful for beginners who want to learn how to use R for data analysis.
Hands-on guide to R programming. It covers a wide range of topics, including data import, manipulation, and visualization. The book is especially useful for beginners who want to learn how to use R for data analysis.
Save
Gentle introduction to R programming. It covers all the basics of R, including data import, manipulation, and visualization. The book is especially useful for beginners who want to learn how to use R for data analysis.
Save
Quick introduction to RStudio, the popular IDE for R. It covers all the basics of RStudio, including how to install it, how to use it to edit and run R code, and how to use its built-in tools for data analysis and visualization. The book is especially useful for beginners who want to learn how to use RStudio for data analysis.
Guide to using R for introductory statistics. It covers a wide range of topics, including data exploration, statistical modeling, and hypothesis testing. The book is especially useful for students and researchers who want to learn how to use R for statistical analysis.
Guide to data manipulation with R. It covers a wide range of topics, including data import, cleaning, and transformation. The book is especially useful for data analysts and researchers who want to learn how to use R for data manipulation.
Save
Collection of recipes for creating common types of graphs in R. It useful reference for both beginners and experienced R programmers.
Save
Collection of common mistakes made by R programmers. It useful resource for both beginners and experienced R programmers who want to avoid making these mistakes.
Save
More advanced guide to R programming. It covers topics such as data structures, algorithms, and object-oriented programming. The book is especially useful for experienced R programmers who want to learn more about the language.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jdbymv/building
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