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Martin Burger
Data exploration is one of the first things you do when analyzing data. It leads the way to further in depth analysis. With this course, you will learn how to use R for data exploration of a large dataset.
Read more
Data exploration is one of the first things you do when analyzing data. It leads the way to further in depth analysis. With this course, you will learn how to use R for data exploration of a large dataset.
Read more
Data exploration is one of the first things you do when analyzing data. It leads the way to further in depth analysis. With this course, you will learn how to use R for data exploration of a large dataset.
Do you want to perform data exploration on a large dataset? In this course, Exploring Data with Quantitative Techniques Using R, you will see why R is a great tool in getting to know your data. The course uses a 3 step approach to explore the NYC flights dataset. First, you will get an initial idea via summary statistics. Then, you will use hypothesis tests and visualizations to work on single variables. Finally, you will use techniques for correlations between multiple variables. On top of that, the course also has a module on data sampling which is especially useful for large datasets. When you are finished with this course, you will have the skills and knowledge of data exploration needed to understand a new dataset. You will also use some outstanding add-on packages for the topic.
Register for this course and see more details by visiting:
OpenCourser.com/course/2dxi3z/exploring
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What's inside
Syllabus
Course Overview
Understanding Data Exploration
Sampling a Dataset for Data Exploration
Summarizing Data to Get an Understanding of New Data
Read more
Syllabus
Course Overview
Understanding Data Exploration
Sampling a Dataset for Data Exploration
Summarizing Data to Get an Understanding of New Data
Read more
Using Correlation Analysis
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores data exploration techniques, providing foundational skills for data analysis
Emphasizes applying statistical techniques to analyze data, a valuable skill for researchers and analysts
Taught by Martin Burger, an experienced instructor who is recognized in the field of data exploration
Uses the NYC flights dataset, providing real-world context for data exploration techniques
Emphasizes correlation analysis, a valuable technique for identifying relationships between variables
Teaches data sampling techniques, which are crucial for handling large datasets
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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 Exploring Data with Quantitative Techniques Using R with these
activities:
Review R basics
Show steps
Review the basics of R to make sure you're comfortable with the syntax and data structures.
Browse courses on
R Programming
Show steps
-
Go through an online tutorial on R basics.
-
Work through some practice problems.
Read 'R for Data Science'
Show steps
This book provides a comprehensive overview of R for data science, covering topics such as data manipulation, visualization, and modeling.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Follow along with the course materials
Show steps
Make sure you're keeping up with the course material by following along with the tutorials.
Show steps
-
Watch the video lectures.
-
Read the course notes.
-
Complete the practice exercises.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve data exploration problems
Show steps
Practice solving data exploration problems to improve your skills in using R for data analysis.
Show steps
-
Find a dataset that you're interested in.
-
Load the dataset into R.
-
Explore the data using the techniques you've learned in the course.
-
Write a report on your findings.
Volunteer with a local data science organization
Show steps
Volunteer with a local data science organization to gain practical experience and network with other data scientists.
Show steps
-
Find a local data science organization that you're interested in.
-
Contact the organization and express your interest in volunteering.
-
Attend volunteer events and contribute your skills.
Contribute to an open-source data science project
Show steps
Contribute to an open-source data science project to gain experience in working with others and contributing to the community.
Browse courses on
R Packages
Show steps
-
Find an open-source data science project that you're interested in.
-
Fork the project on GitHub.
-
Make changes to the project.
-
Submit a pull request.
Review R basics
Show steps
Review the basics of R to make sure you're comfortable with the syntax and data structures.
Browse courses on
R Programming
Show steps
Show steps
Show steps
- Go through an online tutorial on R basics.
- Work through some practice problems.
Read 'R for Data Science'
Show steps
This book provides a comprehensive overview of R for data science, covering topics such as data manipulation, visualization, and modeling.
View
R for Data Science: Import, Tidy, Transform,...
on Amazon
Show steps
Show steps
Show steps
Follow along with the course materials
Show steps
Make sure you're keeping up with the course material by following along with the tutorials.
Show steps
Show steps
Show steps
- Watch the video lectures.
- Read the course notes.
- Complete the practice exercises.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve data exploration problems
Show steps
Practice solving data exploration problems to improve your skills in using R for data analysis.
Show steps
Show steps
Show steps
- Find a dataset that you're interested in.
- Load the dataset into R.
- Explore the data using the techniques you've learned in the course.
- Write a report on your findings.
Volunteer with a local data science organization
Show steps
Volunteer with a local data science organization to gain practical experience and network with other data scientists.
Show steps
Show steps
Show steps
- Find a local data science organization that you're interested in.
- Contact the organization and express your interest in volunteering.
- Attend volunteer events and contribute your skills.
Contribute to an open-source data science project
Show steps
Contribute to an open-source data science project to gain experience in working with others and contributing to the community.
Browse courses on
R Packages
Show steps
Show steps
Show steps
- Find an open-source data science project that you're interested in.
- Fork the project on GitHub.
- Make changes to the project.
- Submit a pull request.
Career center
Learners who complete Exploring Data with Quantitative Techniques Using R will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is someone who collects, analyzes, interprets, and presents data. A course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Data Analyst by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Market Researcher
Market Researcher
A Market Researcher conducts research on target markets, competitors, and industry trends. The course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Market Researcher by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Business Analyst
Business Analyst
A Business Analyst uses data to identify and solve business problems. The course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Business Analyst by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Financial Analyst
Financial Analyst
A Financial Analyst uses data to make investment decisions. The course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Financial Analyst by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Data Scientist
Data Scientist
A Data Scientist uses data to build models and make predictions. This course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Data Scientist by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Statistician
Statistician
A Statistician collects, analyzes, interprets, and presents data. This course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Statistician by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst uses data to make investment decisions. This course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Quantitative Analyst by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Actuary
Actuary
An Actuary uses data to assess risk and make financial decisions. This course on exploring data with quantitative techniques using R can help you build a foundation for a career as an Actuary by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Risk Analyst
Risk Analyst
A Risk Analyst uses data to assess risk and make decisions. This course on exploring data with quantitative techniques using R can help you build a foundation for a career as a Risk Analyst by teaching you how to use R to analyze data, perform hypothesis tests, and create visualizations. This course can also help you develop the skills you need to identify trends and patterns in data, which is essential for success in this field.
Data Engineer
Data Engineer
A Data Engineer builds and maintains data infrastructure. This course on exploring data with quantitative techniques using R may be useful for a career as a Data Engineer as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover data engineering topics such as data warehousing or data pipelines.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course on exploring data with quantitative techniques using R may be useful for a career as a Software Engineer as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover software engineering topics such as object-oriented programming or data structures.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst uses data to solve business problems. This course on exploring data with quantitative techniques using R may be useful for a career as an Operations Research Analyst as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover operations research topics such as linear programming or optimization.
Epidemiologist
Epidemiologist
An Epidemiologist studies the distribution and determinants of health-related states or events in specified populations. This course on exploring data with quantitative techniques using R may be useful for a career as an Epidemiologist as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover epidemiology topics such as disease surveillance or outbreak investigation.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to data in the field of biology. This course on exploring data with quantitative techniques using R may be useful for a career as a Biostatistician as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover biostatistics topics such as clinical trials or survival analysis.
Teacher
Teacher
A Teacher teaches students in a classroom setting. This course on exploring data with quantitative techniques using R may be useful for a career as a Teacher as it teaches you how to use R to analyze data, perform hypothesis tests, and create visualizations. However, it is important to note that this course does not cover teaching topics such as classroom management or curriculum development.
For more career information including salaries, visit:
OpenCourser.com/course/2dxi3z/exploring
Reading list
We've selected 21 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
Exploring Data with Quantitative Techniques Using R.
Classic text on statistical learning, and it covers a wide range of topics that are relevant to this course. It is particularly useful for those who want to get a deep understanding of the statistical methods that are used in data science.
Save
Provides a comprehensive introduction to R for data science. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use R for data science tasks.
Comprehensive introduction to data science, and it covers a wide range of topics that are relevant to this course. It is particularly useful for those who are new to the field, or who want to get a strong foundation in the fundamental concepts of data science.
Great introduction to the field of data science, and it covers a wide range of topics that are relevant to this course. It is particularly useful for those who are new to the field, or who want to get a broad overview of the different aspects of data science.
Provides a comprehensive introduction to R programming. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use R for data science tasks.
Provides a comprehensive introduction to data analysis using open source tools. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use open source tools for data analysis tasks.
Provides a practical introduction to data analysis using R. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use R for data analysis tasks.
Provides a comprehensive introduction to data analysis using Stata. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use Stata for data analysis tasks.
Save
Provides a comprehensive introduction to data analysis using SAS. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use SAS for data analysis tasks.
Provides a comprehensive introduction to data analysis using SPSS. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use SPSS for data analysis tasks.
Save
Provides a comprehensive introduction to statistical methods for data analysis. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use statistical methods for data analysis tasks.
Save
Provides a practical introduction to data mining using R. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use R for data mining tasks.
Provides a practical introduction to machine learning using R. It covers a wide range of topics that are relevant to this course, and it is particularly useful for those who want to learn how to use R for machine learning tasks.
Provides a comprehensive introduction to ggplot2, a powerful R package for data visualization. It is particularly useful for those who want to learn how to create beautiful and informative data visualizations.
Provides a comprehensive introduction to applied statistics with S-PLUS, covering topics such as data manipulation, data visualization, and statistical modeling. It valuable resource for anyone who wants to learn more about applied statistics and statistical modeling using S-PLUS.
Provides a comprehensive introduction to statistical learning, covering topics such as supervised learning, unsupervised learning, and statistical modeling. It valuable resource for anyone who wants to learn more about statistical learning and statistical modeling.
Provides a comprehensive introduction to statistical methods for data analysis, covering topics such as statistical methods, statistical techniques, and statistical applications. It valuable resource for anyone who wants to learn more about statistical methods and statistical techniques for data analysis.
Provides a comprehensive introduction to data visualization with R, covering topics such as data visualization techniques, data visualization methods, and data visualization applications. It valuable resource for anyone who wants to learn more about data visualization and data visualization techniques using R.
Save
Provides a comprehensive introduction to ggplot2, a powerful R package for data visualization. It covers topics such as ggplot2 syntax, ggplot2 functions, and ggplot2 applications. It valuable resource for anyone who wants to learn more about data visualization using ggplot2.
Save
Provides a comprehensive introduction to R, a popular programming language for data analysis. It covers topics such as R syntax, R functions, and R applications. It valuable resource for anyone who wants to learn more about R programming.
Provides a comprehensive introduction to data manipulation with R, covering topics such as data manipulation techniques, data manipulation methods, and data manipulation applications. It valuable resource for anyone who wants to learn more about data manipulation and data manipulation techniques using R.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/2dxi3z/exploring
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Time
122 hours
Level
Intermediate
Via
Pluralsight
Instructor
Martin Burger
Language
English
Good to know
Explores data exploration techniques, providing foundational skills for data analysis
Emphasizes applying statistical techniques to analyze data, a valuable skill for researchers and analysts
Taught by Martin Burger, an experienced instructor who is recognized in the field of data exploration
Uses the NYC flights dataset, providing real-world context for data exploration techniques
Emphasizes correlation analysis, a valuable technique for identifying relationships between variables
Teaches data sampling techniques, which are crucial for handling large datasets
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