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Exploring Data with Quantitative Techniques Using R

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.

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.

This course is no longer available. Find something similar by browsing:

Data Exploration R Data Sampling Data Visualization Correlation Analysis NYC Flights Dataset Hypothesis Testing

What's inside

Syllabus

Course Overview

Understanding Data Exploration

Sampling a Dataset for Data Exploration

Summarizing Data to Get an Understanding of New Data

Traffic lights

Read about what's good

what should give you pause

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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Reviews summary

Practical r for data exploration fundamentals

According to students, this course offers a strong foundation in data exploration using R. Learners appreciate the clear, practical approach to understanding new datasets through quantitative techniques. The course provides hands-on experience with a large dataset and is lauded for its coverage of summary statistics, hypothesis testing, and correlation analysis. Some learners, however, noted that it serves as a foundational course, suggesting those with prior R experience may find the initial pace or depth of certain topics more suitable, while beginners might need to dedicate extra time.

Focuses on foundational skills rather than advanced topics.

"The course provides a good introduction, but I would look elsewhere for more advanced topics in R."

"It builds a solid base in data exploration but doesn't delve deeply into complex methodologies."

"Great for initial exploration and getting started, not for becoming an R expert immediately."

Covers essential data sampling and useful R packages.

"The module on data sampling was especially helpful, particularly for dealing with very large datasets."

"I discovered some outstanding R add-on packages for data exploration that will save me a lot of time."

"It highlighted useful tools beyond just base R, expanding my toolkit for advanced analysis."

Builds a strong understanding of key quantitative methods.

"The explanations of summary statistics and hypothesis tests were very clear and easy to follow."

"I now grasp the fundamentals of correlation analysis well, which is crucial for my work."

"It's a great course for understanding the initial steps of data analysis and preparing for deeper dives."

Provides hands-on experience for data exploration using R.

"I learned practical data exploration techniques using R that I can apply immediately to my projects."

"The course gave me solid hands-on experience with a large dataset, which was incredibly valuable."

"It really helped me get familiar with R for quantitative data analysis in a real-world context."

Some prior R exposure might enhance the learning experience.

"As a complete beginner to R, I found the pace a bit fast initially, requiring extra self-study."

"It's best if you have a basic understanding of R syntax and environment setup before starting."

"While comprehensive, a little prior coding background in R is beneficial to keep up easily."

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

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Review the basics of R to make sure you're comfortable with the syntax and data structures.

Browse courses on R Programming

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Go through an online tutorial on R basics.
Work through some practice problems.

Read 'R for Data Science'

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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

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Follow along with the course materials

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Make sure you're keeping up with the course material by following along with the tutorials.

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Watch the video lectures.
Read the course notes.
Complete the practice exercises.

Three other activities

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Show all six activities

Solve data exploration problems

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Practice solving data exploration problems to improve your skills in using R for data analysis.

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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

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Volunteer with a local data science organization to gain practical experience and network with other data scientists.

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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

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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

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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

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Market Researcher

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.

See salaries and explore the career path for Business 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.

See salaries and explore the career path for Financial Analyst

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Actuary

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.

See salaries and explore the career path for Risk Analyst

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Epidemiologist

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.

See salaries and explore the career path for Biostatistician

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.

See salaries and explore the career path for Teacher

Reading list

We've selected 17 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.

The Elements of Statistical Learning

Save

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.

The Elements of Statistical Learning: Data Mining,...

Hardcover

$$$