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Arimoro Olayinka Imisioluwa
Welcome to this project-based course Introduction to EDA in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions.
By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R.
Read more
Welcome to this project-based course Introduction to EDA in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions.
By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R.
Read more
Welcome to this project-based course Introduction to EDA in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions.
By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R.
Note, you do not need to be a data scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided projects titled “Getting Started with R” and “Calculating Descriptive Statistics in R”
Register for this course and see more details by visiting:
OpenCourser.com/course/y4h4iu/introduction
What's inside
Syllabus
Project Overview
Welcome to this project-based course Introduction to Exploratory Data Analysis (EDA) in R. In this project, you will learn how to perform extensive exploratory data analysis on both quantitative and qualitative variables using basic R functions. By the end of this 2-hour long project, you will understand how to create different basic plots in R. Also, you will learn how to create plots for categorical variables and numeric or quantitative variables. By extension, you will learn how to plot three variables and save your plot as an image in R. Note, you do not need to be a data scientist to be successful in this guided project, just a familiarity with basic statistics and using R suffice for this project. If you are not familiar with R and want to learn the basics, start with my previous guided projects titled “Getting Started with R” and “Calculating Descriptive Statistics in R”
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Introduces students to exploratory data analysis (EDA), which is commonly used in data science and analytics
Provides a hands-on approach to learning EDA by guiding learners through project-based activities
Covers essential EDA techniques for analyzing both quantitative and qualitative variables
Requires familiarity with basic statistics and R programming
Suitable for individuals seeking to enhance their data analysis skills and knowledge
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Reviews summary
Great eda with r
According to students, this course offers engaging assignments and a great way to learn Exploratory Data Analysis (EDA) with R Programming.
This course has great staff.
"The course staff was great."
"I learned a lot from the course staff."
"The course staff was very helpful."
This course offers interesting projects in EDA with R
"The course has lots of interesting projects."
"I enjoyed working on the projects."
"The projects were a great way to learn EDA."
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 Introduction to EDA in R with these
activities:
Review basic statistics concepts
Show steps
Refresh your understanding of basic statistics concepts covered in the course
Browse courses on
Basic Statistics
Show steps
-
Revisit your notes or textbook from a previous statistics course
-
Take a practice quiz or exam to assess your current understanding
-
Review online resources or tutorials on basic statistics
-
Consider working with a tutor or study group for additional support
Read 'R for Data Science'
Show steps
Supplement your understanding of R programming and data analysis techniques by reading a comprehensive book
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Purchase or borrow a copy of 'R for Data Science'
-
Read through the chapters that align with the topics covered in the course
-
Work through the exercises and examples provided in the book
-
Consider discussing your key takeaways with classmates or a mentor
Develop an interactive data visualization dashboard
Show steps
Apply your skills to a hands-on project that involves creating an interactive data visualization dashboard, solidifying your understanding of data analysis and presentation
Browse courses on
Interactive Data Visualization
Show steps
-
Identify a dataset that you want to visualize and a specific question you want to answer
-
Choose appropriate visualization techniques and design the layout of your dashboard
-
Use R libraries to create interactive elements, such as filters and sliders
-
Present your dashboard to others and gather feedback for improvement
Show all three activities
Review basic statistics concepts
Show steps
Refresh your understanding of basic statistics concepts covered in the course
Browse courses on
Basic Statistics
Show steps
Show steps
Show steps
- Revisit your notes or textbook from a previous statistics course
- Take a practice quiz or exam to assess your current understanding
- Review online resources or tutorials on basic statistics
- Consider working with a tutor or study group for additional support
Read 'R for Data Science'
Show steps
Supplement your understanding of R programming and data analysis techniques by reading a comprehensive book
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Purchase or borrow a copy of 'R for Data Science'
- Read through the chapters that align with the topics covered in the course
- Work through the exercises and examples provided in the book
- Consider discussing your key takeaways with classmates or a mentor
Develop an interactive data visualization dashboard
Show steps
Apply your skills to a hands-on project that involves creating an interactive data visualization dashboard, solidifying your understanding of data analysis and presentation
Browse courses on
Interactive Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset that you want to visualize and a specific question you want to answer
- Choose appropriate visualization techniques and design the layout of your dashboard
- Use R libraries to create interactive elements, such as filters and sliders
- Present your dashboard to others and gather feedback for improvement
Show all three activities
Career center
Learners who complete Introduction to EDA in R will develop knowledge and skills
that may be useful to these careers:
Data Visualization Specialist
Data Visualization Specialist
Data Visualization Specialists create visual representations of data to help organizations understand their data and make better decisions. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any data visualization specialist. You will learn how to use R to plot data and identify patterns, which will help you to create more effective and informative data visualizations.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts typically have a strong understanding of statistical theory and methods. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any quantitative analyst. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better financial decisions.
Actuary
Actuary
Actuaries typically have a strong foundation in statistical theory and methods. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any actuary. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better financial decisions.
Statistician
Statistician
A Statistician typically has a strong foundation in statistical theory and methods. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any statistician. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better decisions. This course is a great way to build on your existing statistical knowledge and skills.
Risk Analyst
Risk Analyst
Risk Analysts typically have a strong understanding of data analysis and risk management techniques. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any risk analyst. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to better manage risk.
Research Analyst
Research Analyst
Research Analysts typically have a strong foundation in data analysis and research methods. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any research analyst. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better decisions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers typically have a strong foundation in data analysis and modeling. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any machine learning engineer. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to build better machine learning models.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts typically have a strong foundation in data analysis and optimization techniques. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any operations research analyst. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to improve operational efficiency.
Financial Analyst
Financial Analyst
Financial Analysts typically have a strong understanding of financial data and analysis techniques. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any financial analyst. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better financial decisions.
Market Researcher
Market Researcher
Market Researchers typically have a strong understanding of data analysis and visualization techniques. This course provides an introduction to exploratory data analysis (EDA), which is an essential tool for any market researcher. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to inform marketing decisions. This course is a great way to build on your existing market research skills and knowledge.
Data Scientist
Data Scientist
Data Scientists use data to solve problems. This course will help you build a foundation in exploratory data analysis (EDA), which is essential for any data scientist. You will learn how to use R to plot data and identify patterns, which will help you to develop insights that can be used to make better decisions. This course is a great way to start your journey to becoming a data scientist.
Business Analyst
Business Analyst
Business Analysts often use data analysis to inform their recommendations for a company or organization. The skills learned in this course, such as exploratory data analysis and creating basic plots in R, will give you a strong foundation for success as a business analyst. You will also be able to use these skills to communicate data insights to stakeholders in a clear and concise way.
Data Engineer
Data Engineer
Data Engineers often use data analysis to ensure data quality and build data pipelines. The skills learned in this course, such as exploratory data analysis and creating basic plots in R, will give you a strong foundation for success in this role.
Product Manager
Product Manager
Product Managers often use data analysis to understand user needs and improve product development. The skills learned in this course, such as exploratory data analysis and creating basic plots in R, will give you a strong foundation for success in this role.
Data Analyst
Data Analyst
A Data Analyst helps to build a foundation in data analysis that can be expanded and refined to succeed in this role. This course may be particularly useful for a Data Analyst who will be responsible for creating visual representations of data or who will need to perform exploratory data analysis on a regular basis as part of their job duties.
For more career information including salaries, visit:
OpenCourser.com/course/y4h4iu/introduction
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
Introduction to EDA in R.
Save
Provides a comprehensive overview of exploratory data analysis in R. It covers a wide range of topics, from data visualization to statistical modeling. It valuable resource for anyone who wants to learn more about EDA.
Save
Comprehensive introduction to the R programming language. It covers a wide range of topics, from basic syntax to advanced statistical techniques. It valuable resource for anyone who wants to learn more about R.
Provides a modern approach to statistics using R. It covers a wide range of topics, from Bayesian statistics to machine learning. It valuable resource for anyone who wants to learn more about modern statistics.
Provides a practical introduction to machine learning using R. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of statistical analysis using R. It covers a wide range of topics, from data exploration to statistical modeling. It valuable resource for anyone who wants to learn more about statistical analysis.
Save
Provides a practical introduction to data science using R. It covers a wide range of topics, from data manipulation to machine learning. It valuable resource for anyone who wants to learn more about data science.
Save
Provides a comprehensive overview of advanced R programming techniques. It covers a wide range of topics, from data visualization to statistical modeling. It valuable resource for anyone who wants to learn more about advanced R programming.
Provides a comprehensive overview of R programming for data science. It covers a wide range of topics, from data manipulation to statistical modeling. It valuable resource for anyone who wants to learn more about R programming for data science.
Provides a practical introduction to machine learning using R. It covers a wide range of topics, from supervised learning to unsupervised learning. It valuable resource for anyone who wants to learn more about machine learning.
Provides a comprehensive overview of statistical methods for data analysis using R. It covers a wide range of topics, from data exploration to statistical modeling. It valuable resource for anyone who wants to learn more about statistical methods for data analysis.
Save
Provides a comprehensive overview of data mining using R. It covers a wide range of topics, from data preparation to model evaluation. It valuable resource for anyone who wants to learn more about data mining.
Provides a comprehensive overview of the art of R programming. It covers a wide range of topics, from data visualization to statistical modeling. It valuable resource for anyone who wants to learn more about the art of R programming.
Save
Provides a comprehensive collection of recipes for R programming. It covers a wide range of topics, from data manipulation to statistical modeling. It valuable resource for anyone who wants to learn more about R programming.
Provides a comprehensive overview of R programming in action. It covers a wide range of topics, from data visualization to statistical modeling. It valuable resource for anyone who wants to learn more about R programming in action.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/y4h4iu/introduction
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Arimoro Olayinka Imisioluwa
Language
English
Good to know
Introduces students to exploratory data analysis (EDA), which is commonly used in data science and analytics
Provides a hands-on approach to learning EDA by guiding learners through project-based activities
Covers essential EDA techniques for analyzing both quantitative and qualitative variables
Requires familiarity with basic statistics and R programming
Suitable for individuals seeking to enhance their data analysis skills and knowledge
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