Question 1

What is exploratory data analysis?

Accepted Answer

Exploratory data analysis (EDA) is an approach to data analysis used to investigate sets of data, summarize their characteristics, and figure out how to best work with data to get answers while providing a visual to help businesses, scientists, researchers, and analysts learn more from that data. Exploratory data analysis makes it easier to find patterns and anomalies in data, and it can be used to determine what the data reveals beyond modeling. It's useful as a step in creating sophisticated data models and analysis. EDA tools include clustering/dimension reduction techniques to create graphs, K-means clustering, and predictive modeling, including linear regression. There are four main types of exploratory data analysis, including univariate non-graphical, univariate graphical, multivariate nongraphical, and multivariate graphical. All of these types describe the data, but graphical exploratory data analysis provides a more complete picture created by the data.

Question 2

Why learn exploratory data analysis?

Accepted Answer

If you're passionate about working with numbers and transforming them to tell a story that influences others, learning about exploratory data analysis can help you forge a career based on that passion. It's a solid start to jobs in data science, but you'll also gain a variety of related skills, including coding using Python and R, data cleansing, and predictive modeling. Beyond starting a new career or advancing your existing one, there are benefits for anyone who chooses to learn about exploratory data analysis, including solid problem-solving skills, the ability to find connections between data and real-world problems, and gaining useful tools to guide major decisions ranging from getting the most out of marketing campaigns to maximizing project executions to hiring key players for organizations.

Question 3

What career opportunities can arise from learning exploratory data analysis?

Accepted Answer

If you're looking for a career in transforming large volumes of data into actionable advice and solutions, a career in exploratory data analytics could be your ideal path, particularly if you're passionate about using data to evaluate whether the statistical methods you intend to use for analyzing that data are the most effective options. This fast-growing field is in-demand, with skilled, knowledgeable exploratory data analysts among the most highly sought professionals across multiple industries. Exploratory data analysis is somewhat like solving puzzles, piecing data-driven insights together to help employers and clients make well-informed business decisions based on sound data that's been evaluated for assumptions, errors, and trends. You might work on Wall Street for a hedge fund or investment bank. You could work in healthcare, insurance, retail, or marketing, among other industries.

Question 4

How can online courses on Coursera help me learn exploratory data analysis?

Accepted Answer

Online courses on Coursera give you the opportunity to do everything from gaining experience in fundamentals to earning professional certification. If you're new to the field, beginner courses like Exploratory Data Analysis with MATLAB can help you build a foundation in data analysis and data visualization. If you're looking to advance your skills, you might explore your options to gain professional certification through IBM's IBM Data Analyst offering. Or you could opt for a specialization, like the Data Science option from Johns Hopkins, which combines courses and applied learning to help you build firsthand knowledge and skills that you'll be able to apply in business settings.

Question 5

What are the benefits of taking an online Exploratory Data Analysis course?

Accepted Answer

Online Exploratory Data Analysis courses offer a convenient and flexible way to enhance your knowledge or learn new Exploratory Data Analysis skills. Choose from a wide range of Exploratory Data Analysis courses offered by top universities and industry leaders tailored to various skill levels.

Question 6

What Exploratory Data Analysis courses are best for training and upskilling employees or the workforce?

Accepted Answer

When looking to enhance your workforce's skills in Exploratory Data Analysis, it's crucial to select a course that aligns with their current abilities and learning objectives. Our Skills Dashboard is an invaluable tool for identifying skill gaps and choosing the most appropriate course for effective upskilling. For a comprehensive understanding of how our courses can benefit your employees, explore the enterprise solutions we offer. Discover more about our tailored programs at Coursera for Business here.

Exploratory Data Analysis Courses Online

Filter by

Subject
Required

Language
Required

Learning Product
Required

Level
Required

Duration
Required

Skills
Required

Subtitles
Required

Educator
Required

Explore the Exploratory Data Analysis Course Catalog

Introduction to Data Analysis Using Excel

Google Data Analytics Capstone: Complete a Case Study

Data Warehousing for Business Intelligence

Data Analysis Using Python

Introduction to Data Science in Python

Data Science: Foundations using R

Python Data Structures

Introduction to Data Science

IBM Data Warehouse Engineer

Foundations of Data Science

Data Science Methodology

Share Data Through the Art of Visualization

Exploratory Data Analysis learners also search

In summary, here are 10 of our most popular exploratory data analysis courses

Skills you can learn in Probability And Statistics

Frequently Asked Questions about Exploratory Data Analysis

What is exploratory data analysis?‎

Why learn exploratory data analysis?‎

What career opportunities can arise from learning exploratory data analysis?‎

How can online courses on Coursera help me learn exploratory data analysis?‎

What are the benefits of taking an online Exploratory Data Analysis course?‎

What Exploratory Data Analysis courses are best for training and upskilling employees or the workforce?‎

Other topics to explore