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Exploratory Data Analysis

Roger D. Peng, PhD, Jeff Leek, PhD, and Brian Caffo, PhD

4.7

Based on 6,071 ratings

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Roger D. Peng, PhD, Jeff Leek, PhD, and Brian Caffo, PhD

This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data. We will cover in detail the plotting systems in R as well as some of the basic principles of constructing data graphics. We will also cover some of the common multivariate statistical techniques used to visualize high-dimensional data.

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What's inside

Syllabus

Week 1

This week covers the basics of analytic graphics and the base plotting system in R. We've also included some background material to help you install R if you haven't done so already.

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Provides foundational knowledge of exploratory data analysis techniques

Covers specific multivariate statistical techniques for visualizing high-dimensional data

Instructors are recognized for their expertise in exploratory data analysis

Taught using R, which has strong support for exploratory data analysis

Provides case studies to demonstrate real-world applications of exploratory data analysis

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Reviews summary

Practical exploratory data analysis in r

According to learners, this course provides a solid introduction to Exploratory Data Analysis concepts and techniques using R. Many found the coverage of the ggplot2 plotting system particularly valuable for creating effective data visualizations. Students appreciate the hands-on approach with practical assignments that help solidify understanding. While the course is well-structured across four weeks, some students felt certain topics could benefit from greater depth or that the pace was uneven. Setup issues with R or packages were mentioned by a few. Overall, it's considered a strong foundational course for those new to EDA in R, though prior R experience is beneficial.

Pace felt uneven for some learners.

"Week 1 and 2 were great, but Week 3 felt rushed with complex topics."

"The structure is logical, but the difficulty seemed to jump in the later weeks."

"Found the pace challenging at times, especially integrating the different concepts."

Some topics could benefit from more detail.

"While good for an intro, I felt some advanced topics were covered a bit too quickly."

"Could use more in-depth coverage on certain techniques, particularly for higher dimensions."

"It's a good overview, but not sufficient if you need deep theoretical understanding."

Easier for those already familiar with R.

"Highly recommend having some basic R knowledge before starting this course."

"As someone new to R, I found myself struggling more than others with the code."

"The course assumes a minimal level of R familiarity which might be tough for complete beginners."

Hands-on exercises aid learning and application.

"The assignments were practical and reinforced the concepts taught in the lectures."

"I really benefited from the hands-on coding exercises and projects."

"The practical nature of the homework helped me apply what I learned."

Provides a good introduction to EDA principles.

"This course gave me a great introduction to the core concepts of Exploratory Data Analysis."

"I feel like I have a solid foundation now after taking this course."

"It covers the essential techniques needed to get started with EDA."

Detailed coverage of key R plotting systems.

"The sections on ggplot2 were incredibly helpful and immediately applicable."

"Learning the different R plotting systems (base, lattice, ggplot2) was a major plus."

"The course does a great job explaining how to create various plots in R."

Potential difficulties with software installation.

"Had some trouble getting R and the necessary packages installed initially."

"The setup instructions weren't always clear for different operating systems."

"Encountered errors related to package dependencies during the exercises."

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 with these activities:

Read 'Exploratory Data Analysis with R' by Roger D. Peng

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Gain a deeper understanding of the course concepts by reading the foundational book on exploratory data analysis with R.

View Exploratory Data Analysis with R on Amazon

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Read chapters covering basic exploratory techniques, data visualization, and multivariate statistical methods.
Work through the practice exercises provided in the book.

Review probability theory

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Brush up on probability theory to strengthen your foundation for this course's statistical concepts.

Browse courses on Probability Theory

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Review probability concepts such as conditional probability, Bayes' theorem, and random variables.
Solve practice problems involving probability distributions and statistical inference.

Practice plotting techniques in R

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Improve your ability to create clear and informative data visualizations for exploratory data analysis.

Browse courses on R

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Follow the tutorials in Week 1 and Week 2 of the course
Experiment with the different plotting systems in R
Try creating your own data visualizations from scratch

Eight other activities

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Visualize Data with R

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Practice visualizing data with R to enhance your understanding of data distributions and relationships.

Browse courses on Data Visualization

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Import data into R
Create basic plots (e.g., histograms, scatterplots)
Customize plot appearance (e.g., colors, labels)
Use R packages for advanced visualization (e.g., ggplot2)

Complete practice exercises on data visualization

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Reinforce your understanding of data visualization techniques by completing practice exercises.

Browse courses on Data Visualization

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Use the ggplot2 library to create various types of plots, such as scatterplots, histograms, and box plots.
Practice customizing plots by changing colors, adding titles, and adjusting axes.

Attend study groups with classmates

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Enhance your understanding of course concepts by discussing them with peers.

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Form study groups with classmates who have similar interests or learning styles.
Discuss course material, work on practice problems together, and provide mutual support.

Analyze real-world datasets with R

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Develop your skills in using R to explore and analyze real-world datasets, enhancing your understanding of data analysis techniques.

Browse courses on Data Analysis

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Find a dataset of interest from a reputable source
Load the dataset into R and explore its structure
Perform exploratory data analysis using the techniques covered in the course
Write a report or presentation summarizing your findings

Follow tutorials on advanced R programming

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Expand your R programming skills by following tutorials on advanced topics.

Browse courses on R Programming

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Learn how to work with large datasets using the dplyr and tidyr libraries.
Practice creating interactive data visualizations using the Shiny package.

Attend a workshop on data wrangling

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Develop practical skills in data wrangling by attending a workshop dedicated to the topic.

Browse courses on Data Wrangling

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Participate in hands-on exercises to learn techniques for data cleaning, transformation, and preparation.
Interact with experts and ask questions to enhance your understanding.

Create a data visualization project

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Apply your data visualization skills to a real-world dataset by creating a project.

Browse courses on Data Analysis

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Choose a dataset and explore it using exploratory data analysis techniques.
Create a set of visualizations that effectively communicate the insights gained from the data analysis.
Present your project to a peer or mentor for feedback.

Contribute to an open-source data visualization project

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Apply your skills and contribute to the open-source community by participating in a data visualization project.

Browse courses on Open Source

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Identify an open-source data visualization project that aligns with your interests.
Join the project community, learn about their codebase, and identify areas where you can contribute.
Make code contributions, participate in discussions, and collaborate with other developers.

Career center

Learners who complete Exploratory Data Analysis will develop knowledge and skills that may be useful to these careers:

Data Analyst

A Data Analyst collects, processes, and analyzes data to extract meaningful insights. The Exploratory Data Analysis course from Johns Hopkins University can help aspiring Data Analysts develop the skills needed to summarize data, identify patterns, and visualize results. This course covers essential techniques for exploratory data analysis, including plotting, clustering, and dimension reduction, which are crucial for understanding data and making informed decisions.

See salaries and explore the career path for Data Analyst

Data Visualization Specialist

A Data Visualization Specialist designs and creates visual representations of data to communicate insights and trends. The Exploratory Data Analysis course from Johns Hopkins University provides a strong foundation for aspiring Data Visualization Specialists by covering essential techniques for summarizing data, identifying patterns, and visualizing results. This course helps individuals develop the skills needed to create effective and engaging data visualizations that clearly communicate insights and support decision-making.

See salaries and explore the career path for Data Visualization Specialist

Statistician

A Statistician uses mathematical and statistical methods to analyze data, draw conclusions, and make predictions. The Exploratory Data Analysis course from Johns Hopkins University provides a solid foundation for aspiring Statisticians by covering essential exploratory techniques for summarizing data, identifying patterns, and visualizing results. This course helps build a strong understanding of data analysis principles and prepares individuals for more advanced statistical modeling and inference.

See salaries and explore the career path for Statistician

Data Scientist

A Data Scientist combines expertise in data analysis, programming, and machine learning to extract insights from data. The Exploratory Data Analysis course from Johns Hopkins University can be beneficial for aspiring Data Scientists as it provides a strong foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for data exploration and analysis.

See salaries and explore the career path for Data Scientist

Information Architect

An Information Architect designs and organizes information systems to ensure they are easy to find and use. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Information Architects as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding user needs, organizing information effectively, and designing user-friendly information systems.

See salaries and explore the career path for Information Architect

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Machine Learning Engineers as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and manipulating data, which is crucial for building effective machine learning models.

See salaries and explore the career path for Machine Learning Engineer

User Experience Researcher

A User Experience Researcher studies how users interact with products and services to improve their usability and experience. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring User Experience Researchers as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding user behavior, identifying pain points, and making data-driven decisions to improve user experience.

See salaries and explore the career path for User Experience Researcher

Business Analyst

A Business Analyst uses data to identify opportunities and solve problems for businesses. The Exploratory Data Analysis course from Johns Hopkins University can be beneficial for aspiring Business Analysts as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for analyzing business data, identifying trends, and making data-driven recommendations.

See salaries and explore the career path for Business Analyst

Quality Assurance Analyst

A Quality Assurance Analyst ensures that products and services meet quality standards. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Quality Assurance Analysts as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for analyzing data, identifying defects, and ensuring that products and services meet customer requirements.

See salaries and explore the career path for Quality Assurance Analyst

Data Engineer

A Data Engineer designs, builds, and maintains data infrastructure and systems. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Data Engineers as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and manipulating data, which is crucial for building and maintaining effective data infrastructure.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical models to analyze financial data and make investment decisions. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Quantitative Analysts as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for analyzing financial data, identifying trends, and making data-driven investment decisions.

See salaries and explore the career path for Quantitative Analyst

Market Researcher

A Market Researcher gathers and analyzes data to understand consumer behavior and market trends. The Exploratory Data Analysis course from Johns Hopkins University can be beneficial for aspiring Market Researchers as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and analyzing market data, identifying trends, and making data-driven marketing decisions.

See salaries and explore the career path for Market Researcher

Actuary

An Actuary uses mathematical and statistical models to assess risk and uncertainty. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Actuaries as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and analyzing risk data, making data-driven decisions, and developing insurance and financial products.

See salaries and explore the career path for Actuary

Epidemiologist

An Epidemiologist investigates the distribution and determinants of health-related states or events in specified populations. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Epidemiologists as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and analyzing health data, identifying trends, and making data-driven public health decisions.

See salaries and explore the career path for Epidemiologist

Environmental Scientist

An Environmental Scientist studies the environment and its components and processes. The Exploratory Data Analysis course from Johns Hopkins University may be helpful for aspiring Environmental Scientists as it provides a foundation in data exploration and visualization. By learning essential techniques for summarizing data, identifying patterns, and visualizing results, individuals can gain valuable skills for understanding and analyzing environmental data, identifying trends, and making data-driven environmental decisions.

See salaries and explore the career path for Environmental Scientist

Reading list

We've selected 35 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.

Exploratory Data Analysis with R

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Provides a comprehensive overview of exploratory data analysis techniques in R, covering both the theoretical foundations and practical applications. It is an excellent resource for students and practitioners who want to learn more about EDA.

Exploratory Data Analysis

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Reviews summary

Practical exploratory data analysis in r

Activities

Career center

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