Exploratory Data Analysis from Coursera

Statistical analysis is an indispensable aspect of data analysis because it allows us to collect, review and analyse data to draw valuable conclusions in various industries. This is why the market for statisticians is projected to grow in the future.

If you want to build your statistics and probability expertise and learn about data visualisation, this short course is a great introduction to statistics as the art of learning from data.

With real-life examples, you will explore the differences between data and information to discover the need for statistical models to gain objective and reliable inferences. You will consider what "unbiased" data collection means and explore various examples of data misrepresentation, misconception or incompleteness which will help you to develop statistical intuition and good practice skills.

Data visualisation is a sought-after skill. To create graphical and numerical summaries, you’ll learn and practice R software skills working in RStudio for exploratory data analysis. You will develop an intuitive concept of probability by completing probability experiments and computer simulations of binomial trails e.g., tossing a coin or rolling a die.

By the end of the course, you will be able to understand the role of statistical models in data analysis, develop numerical and graphical summaries using RStudio, and perform probability experiments in computer simulations.

No matter your current mathematics skill level, you will find something of interest in the course that offers many practical and real-life examples of statistics in action.

This course is a taster of the Online MSc in Data Science (Statistics) and it can also be completed by learners who want to understand the fundamentals of exploratory data analysis and data visualisation.

What's inside

Syllabus

Getting to know your data for graphical summaries

This first week introduces you to data types (categorical, discrete, and continuous) and representing data via graphical summaries (or data visualisation). You will go through the steps you need to take to prepare data for analysis and data cleaning, by identifying missing data and outliers. You learn about and practice common graphical summaries such as box plots, histograms, and kernel density estimation (KDE).

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces statistical concepts and probability distributions, forming a foundational understanding

Core audience: individuals interested in data analysis, probability, and statistics

Explores statistical intuition and data visualization techniques, developing essential skills for data analysis

In-demand field with projected growth, making the course relevant to job seekers

Interactive RStudio labs provide hands-on experience with statistical software

Requires no prior mathematics background, making it accessible to a wider audience

Reviews summary

Clear & practical eda with rstudio

According to learners, "Exploratory Data Analysis" offers a strong foundation for understanding statistical concepts and data visualization. Students consistently highlight the clarity of explanations and the use of real-world examples which make complex topics feel intuitive and accessible, especially for those new to statistics. The hands-on RStudio labs and projects are widely praised as invaluable for practical application, providing an engaging and effective learning experience. While generally well-regarded as an introduction, some with prior knowledge found the pace a tad slow or desired more depth. A few noted initial RStudio setup challenges, and the peer review process had mixed reception, though many found it helpful.

Peer assessment experience can be inconsistent.

"The peer review assignment in the final week was a bit nerve-wracking but ultimately very helpful for getting diverse perspectives on my work."

"The peer review seemed a bit subjective."

"The peer assessment could be improved with clearer guidelines."

Ideal starting point, though some desire more depth.

"As someone totally new to R and data analysis, this was a great starting point."

"It's probably perfect for absolute beginners. The course definitely delivered on its promise of making data intuition accessible."

"I was hoping for a bit more depth in certain areas. It's a stepping stone, but don't expect to be an expert."

"Felt a tad slow for someone with a very basic understanding of statistics."

Complex statistical concepts are made intuitive for beginners.

"The instructor broke down complex statistical concepts into digestible pieces with real-world examples."

"As someone new to statistics, this course was perfect. The explanations were easy to follow."

"It demystified statistics for me. The instructor's approach to explaining data types and concepts like KDE was brilliant."

"Absolutely brilliant! The most intuitive explanation of statistical concepts I've ever encountered."

Provides invaluable hands-on application of concepts.

"The hands-on exercises in RStudio were invaluable."

"The RStudio labs are thoughtfully designed... the RStudio assignments really cemented the concepts."

"The hands-on RStudio practice with real datasets made learning fun and effective."

"The material is very practical, and the labs are thoughtfully designed."

Initial technical hurdles may require troubleshooting for new users.

"I found the RStudio environment setup and some initial instructions a bit confusing. It took me a while to get everything running smoothly."

"I struggled a bit with the RStudio setup and debugging my code. I had to rely on the forums quite a bit for technical support."

"I did find the initial R setup slightly tricky, but once that was done, it was smooth sailing."

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:

Learn R for Data Analysis

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Gain proficiency in R, a powerful tool for statistical analysis and data visualization.

Browse courses on R Programming

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Follow the R for Beginners tutorial on DataCamp.
Complete the practice exercises and quizzes.

Compile a Glossary of Statistical Terms

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Build your understanding of statistical terminology by creating a glossary.

Browse courses on Statistics

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Create a list of key statistical terms.
Define each term in your own words.
Include examples to illustrate the usage of each term.

Review Probability and Statistics by Morris H. DeGroot and Mark J. Schervish

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Review the fundamental concepts of statistics and probability, which will provide a solid foundation for the course.

View Probability and Statistics on Amazon

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Read Chapters 1-3 to establish the principles of statistics and probability.
Complete the practice problems at the end of each chapter to test your understanding.

Six other activities

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Practice Probability Calculations

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Reinforce your understanding of probability by solving a variety of problems.

Browse courses on Probability

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Go to Khan Academy's Probability Exercises
Complete at least 20 exercises to gain proficiency.

Solve Statistical Problems Using R or Python

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Enhance your problem-solving skills by applying statistical techniques in R or Python.

Browse courses on Data Analysis

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Find a collection of statistical problems online.
Solve the problems using R or Python.
Check your answers against solutions provided online or in textbooks.

Create Visual Summaries of Data

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Develop your data visualization skills by creating graphical summaries of real-world datasets.

Browse courses on Data Visualization

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Find a dataset that interests you.
Use R or Python to create visualizations such as histograms, scatterplots, and box plots.
Write a brief summary of your findings.

Develop a Data Visualization Dashboard

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Utilize your data visualization skills to create an interactive dashboard that communicates insights from data.

Browse courses on Data Visualization

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Choose a dataset and identify the key insights you want to convey.
Design and develop interactive visualizations using tools like Tableau or Power BI.
Share your dashboard with others and gather feedback.

Develop a Statistical Model for a Real-World Problem

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Apply your understanding of statistical models to solve a practical problem.

Browse courses on Statistical Modeling

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Identify a problem that can be addressed with statistical modeling.
Collect and analyze data relevant to the problem.
Develop and validate a statistical model that fits the data.
Use the model to make predictions or draw conclusions.

Attend a Workshop on Advanced Statistical Techniques

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Expand your knowledge and skills by attending a workshop focused on advanced statistical techniques.

Browse courses on Statistics

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Research and identify a workshop that aligns with your interests.
Register and attend the workshop.
Actively participate in discussions and exercises.

Career center

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

Data Visualization Specialist

A Data Visualization Specialist creates visual representations of data that is easy for non-technical stakeholders to understand. This role typically requires strong communication and design skills, as well as some data manipulation skills. The Exploratory Data Analysis course will help build a foundation for working with **data visualization** software, RStudio.

See salaries and explore the career path for Data Visualization Specialist

UX Researcher

A UX Researcher studies the behavior of users to improve the design of websites and other products. This role requires strong analytical and communication skills, as well as some data manipulation skills. The Exploratory Data Analysis course will help build a foundation for working with data and may be useful for those who wish to become a UX Researcher.

See salaries and explore the career path for UX Researcher

Business Analyst

A Business Analyst uses data to understand the needs of a business and make recommendations for improvement. This role requires strong analytical skills and experience in business. The Exploratory Data Analysis course will help build a foundation for working with data and may be **helpful** for those who wish to become a Business Analyst.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses data to improve the efficiency of an organization. This role requires strong analytical and problem-solving skills, as well as some data manipulation skills. The Exploratory Data Analysis course will help build a foundation for working with data and may be useful for those who wish to become an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Data Management Analyst

A Data Management Analyst develops and implements strategies for managing data. This role requires strong analytical skills and experience in data management. The Exploratory Data Analysis course will help build a foundation for working with data and may be useful for those who wish to become a Data Management Analyst.

See salaries and explore the career path for Data Management Analyst

Data Analyst

A Data Analyst is someone who mines data, transforms it, and presents it in a way that helps businesses make better decisions. A Data Analyst requires someone with strong analytical skills, as well as experience in data science, computer science, business intelligence, or statistics. Exploratory Data Analysis is a course that provides foundational **data visualization** and **data analysis** skills, and can help build a foundation in working with data, and may therefore be useful for those who wish to become a Data Analyst.

See salaries and explore the career path for Data Analyst

Market Research Analyst

A Market Research Analyst collects and analyzes data to help businesses understand their target market. This role typically requires strong analytical skills and experience in statistics, and the Exploratory Data Analysis course may be useful for those in this field who wish to learn more about **data visualization**.

See salaries and explore the career path for Market Research Analyst

Survey Researcher

A Survey Researcher designs, conducts, and analyzes surveys to collect data on a particular topic. This role requires strong analytical skills and experience in statistics. The Exploratory Data Analysis course may be useful for those who wish to become a Survey Researcher, and can help build a foundation for working with **data visualization**.

See salaries and explore the career path for Survey Researcher

Statistician

A Statistician collects, analyzes, and interprets data to help businesses and organizations make informed decisions. This role requires strong analytical skills and experience in mathematics and statistics. The Exploratory Data Analysis course may be useful for those who wish to become a Statistician, and can help build a foundation for working with data and **data visualization**.

See salaries and explore the career path for Statistician

Information Architect

An Information Architect designs and organizes the structure of websites and other information systems. This role requires strong communication and design skills, as well as some data analysis skills. The Exploratory Data Analysis course may be helpful for those who wish to become an Information Architect and who want to learn more about using data to inform design decisions.

See salaries and explore the career path for Information Architect

Data Scientist

A Data Scientist mines large datasets for patterns and insights that can be used to solve business problems. Although this role typically requires an advanced degree (master's or phd), Exploratory Data Analysis may be **helpful** for those who are currently in this field and wish to learn more about data visualization.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer builds models to automate complex decisions using data. Although this role typically requires an advanced degree (master's or phd), Exploratory Data Analysis may be **helpful** for those who are currently in this field and wish to learn more about data visualization.

See salaries and explore the career path for Machine Learning Engineer

Quantitative Analyst

A Quantitative Analyst uses financial data to make investment decisions. This role requires strong analytical skills and experience in mathematics, statistics, and computer science. The Exploratory Data Analysis course may be **helpful** for those who are currently in this field and wish to learn more about data visualization.

See salaries and explore the career path for Quantitative Analyst

Risk Analyst

A Risk Analyst assesses the risks of a potential investment or project. This role requires strong analytical skills and experience in statistics and finance. The Exploratory Data Analysis course may be **helpful** for those who are currently in this field and wish to learn more about data visualization.

See salaries and explore the career path for Risk Analyst

Consultant

A Consultant provides advice to businesses and organizations on a variety of topics, including strategy, operations, and technology. This role requires strong analytical skills and experience in a specific field. The Exploratory Data Analysis course may be useful for those who wish to become a Consultant and who want to learn more about the use of data in making informed decisions.

See salaries and explore the career path for Consultant