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Emmanuel Segui
In this 1-hour long project-based course, you will learn how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips
Read more
In this 1-hour long project-based course, you will learn how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips
Read more
In this 1-hour long project-based course, you will learn how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/0bht5r/exploratory
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What's inside
Syllabus
Project Overview
At the end of this project, you will know how to do basic exploratory data analysis (EDA) in R, automate your EDA reports and learn advanced EDA tips
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops the core data science skill of exploratory data analysis in R
Meant for beginners in data science
Suitable for learners without prior knowledge of R
Covers advanced EDA tips, making it relevant to intermediate learners as well
Provides hands-on experience through project-based learning
Focuses on automation, which is a valuable skill in data science
Save this course
Save Exploratory Data Analysis in R to your list so you can find it easily later:
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Reviews summary
Engaging data analysis using r
Learners say this engaging course in exploratory data analysis with R has well presented lectures and informative assignments. Though some learners experienced technical difficulties, most were satisfied and learned a lot.
The course is well presented and informative.
"well presented"
"I will attend all courses by Mr Emmanuel Segui"
Learners found the course valuable.
"simply amazing"
"learned a lot about R that I didn't know before"
"great value"
Some learners faced technical issues.
"most packages fail to install"
"the file used in this project cannot be accessed using the cloud workspace"
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 Exploratory Data Analysis in R with these
activities:
Attend data science meetups
Show steps
Connect with others in the field and learn from their experiences.
Show steps
-
Find data science meetups in your area
-
Attend the meetups and interact with other data scientists
Review R basics
Show steps
Start the course with a strong foundation in R basics.
Browse courses on
R Programming
Show steps
-
Go through online tutorials on R basics
-
Complete practice problems on R basics
Read 'Exploratory Data Analysis with R'
Show steps
Supplement your learning with an in-depth book on EDA.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
-
Read the book and take notes
-
Complete the exercises in the book
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice EDA techniques in R
Show steps
Practice the techniques discussed in the course to gain proficiency.
Browse courses on
Exploratory Data Analysis
Show steps
-
Follow along with the EDA examples in the course
-
Complete practice exercises on EDA techniques
-
Create your own EDA visualizations
Participate in peer review
Show steps
Get feedback on your work from peers.
Show steps
-
Exchange EDA reports with a peer
-
Provide feedback and ask for feedback on the report
Help other students with EDA
Show steps
Reinforce your knowledge by helping others.
Show steps
-
Join a study group or online forum
-
Answer questions and provide guidance to other students
Attend data science meetups
Show steps
Connect with others in the field and learn from their experiences.
Show steps
Show steps
Show steps
- Find data science meetups in your area
- Attend the meetups and interact with other data scientists
Review R basics
Show steps
Start the course with a strong foundation in R basics.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Go through online tutorials on R basics
- Complete practice problems on R basics
Read 'Exploratory Data Analysis with R'
Show steps
Supplement your learning with an in-depth book on EDA.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes
- Complete the exercises in the book
Three other activities
Expand to see all activities and additional details
Show all six activities
Practice EDA techniques in R
Show steps
Practice the techniques discussed in the course to gain proficiency.
Browse courses on
Exploratory Data Analysis
Show steps
Show steps
Show steps
- Follow along with the EDA examples in the course
- Complete practice exercises on EDA techniques
- Create your own EDA visualizations
Participate in peer review
Show steps
Get feedback on your work from peers.
Show steps
Show steps
Show steps
- Exchange EDA reports with a peer
- Provide feedback and ask for feedback on the report
Help other students with EDA
Show steps
Reinforce your knowledge by helping others.
Show steps
Show steps
Show steps
- Join a study group or online forum
- Answer questions and provide guidance to other students
Career center
Learners who complete Exploratory Data Analysis in R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data to help companies make better decisions. They collect, clean, and analyze data to identify trends and patterns. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Data Scientist
Data Scientist
Data Scientists use their knowledge of math, statistics, and computer science to solve business problems. They use data to build models and make predictions. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to collect, clean, and analyze data, as well as how to use R to automate your work.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. They use their knowledge of math and statistics to solve problems and make decisions. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Business Analyst
Business Analyst
Business Analysts use data to help companies make better decisions. They analyze data to identify trends and patterns and then make recommendations based on their findings. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and trends. They use this information to help companies make better decisions about their products and services. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Financial Analyst
Financial Analyst
Financial Analysts use data to help companies make better decisions about their finances. They analyze data to identify trends and patterns and then make recommendations based on their findings. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Consultant
Consultant
Consultants use data to help companies solve problems and make better decisions. They analyze data to identify trends and patterns and then make recommendations based on their findings. A course in Exploratory Data Analysis in R can provide you with the skills and knowledge necessary to succeed in this role by teaching you how to clean and analyze data, as well as how to use R to automate your work.
Data Engineer
Data Engineer
Data Engineers build and maintain the infrastructure that is used to store and process data. They use their knowledge of programming and data management to ensure that data is accessible and reliable. A course in Exploratory Data Analysis in R may be useful for Data Engineers who want to learn more about how to clean and analyze data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use math and statistics to solve financial problems. They develop and use models to make predictions about the future. A course in Exploratory Data Analysis in R may be useful for Quantitative Analysts who want to learn more about how to clean and analyze data.
Actuary
Actuary
Actuaries use math and statistics to assess risk and uncertainty. They use their knowledge to develop and price insurance products. A course in Exploratory Data Analysis in R may be useful for Actuaries who want to learn more about how to clean and analyze data.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use math and statistics to solve problems in a variety of industries. They develop and use models to make decisions about how to allocate resources and improve efficiency. A course in Exploratory Data Analysis in R may be useful for Operations Research Analysts who want to learn more about how to clean and analyze data.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They use their knowledge of programming and computer science to create software that meets the needs of users. A course in Exploratory Data Analysis in R may be useful for Software Engineers who want to learn more about how to clean and analyze data.
Computer Scientist
Computer Scientist
Computer Scientists research and develop new computing technologies. They use their knowledge of math, science, and engineering to create new ways to solve problems. A course in Exploratory Data Analysis in R may be useful for Computer Scientists who want to learn more about how to clean and analyze data.
Mathematician
Mathematician
Mathematicians study the properties of numbers, shapes, and structures. They use their knowledge of math to solve problems and make predictions. A course in Exploratory Data Analysis in R may be useful for Mathematicians who want to learn more about how to clean and analyze data.
Physicist
Physicist
Physicists study the laws of nature. They use their knowledge of math and science to explain how the universe works. A course in Exploratory Data Analysis in R may be useful for Physicists who want to learn more about how to clean and analyze data.
For more career information including salaries, visit:
OpenCourser.com/course/0bht5r/exploratory
Reading list
We've selected 12 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
Exploratory Data Analysis in R.
Comprehensive guide to exploratory data analysis in R, and it covers a wide range of topics, including data visualization, statistical modeling, and machine learning. It valuable resource for anyone who wants to learn more about EDA in R.
Save
Comprehensive introduction to the R programming language, and it covers a wide range of topics, including data manipulation, statistical modeling, and graphical visualization. It valuable resource for anyone who wants to learn more about R.
Bayesian statistics textbook that uses R and Stan to teach Bayesian modeling. It valuable resource for anyone who wants to learn more about Bayesian statistics.
Machine learning textbook that uses R to teach machine learning concepts. It valuable resource for anyone who wants to learn more about machine learning.
Statistical learning textbook that covers a wide range of topics, including supervised learning, unsupervised learning, and statistical modeling. It valuable resource for anyone who wants to learn more about statistical learning.
Save
Predictive modeling textbook that covers a wide range of topics, including data preprocessing, model selection, and model evaluation. It valuable resource for anyone who wants to learn more about predictive modeling.
Save
Data science textbook that uses R to teach data science concepts. It valuable resource for anyone who wants to learn more about data science.
Save
Data mining textbook that uses R to teach data mining concepts. It valuable resource for anyone who wants to learn more about data mining.
Save
Is an advanced R programming textbook that covers a wide range of topics, including R programming techniques, data structures, and algorithms. It valuable resource for anyone who wants to learn more about advanced R programming.
Ggplot2 textbook that covers a wide range of topics, including ggplot2 graphics, data visualization, and statistical modeling. It valuable resource for anyone who wants to learn more about ggplot2.
Is an R programming textbook that covers a wide range of topics, including R programming techniques, data structures, and algorithms. It valuable resource for anyone who wants to learn more about R programming.
Save
Is an R programming cookbook that covers a wide range of topics, including data manipulation, data analysis, and data visualization. It valuable resource for anyone who wants to learn more about R programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0bht5r/exploratory
Share
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Emmanuel Segui
Language
English
Good to know
Develops the core data science skill of exploratory data analysis in R
Meant for beginners in data science
Suitable for learners without prior knowledge of R
Covers advanced EDA tips, making it relevant to intermediate learners as well
Provides hands-on experience through project-based learning
Focuses on automation, which is a valuable skill in data science
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