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    • Exploratory Data Analysis

    Exploratory Data Analysis Courses Online

    Learn exploratory data analysis (EDA) for uncovering patterns and insights. Understand data visualization, summary statistics, and hypothesis testing.

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    Explore the Exploratory Data Analysis Course Catalog

    • J

      Johns Hopkins University

      Exploratory Data Analysis

      Skills you'll gain: Exploratory Data Analysis, Data Visualization, Plot (Graphics), Ggplot2, Dimensionality Reduction, Data Visualization Software, R Programming, Scatter Plots, Graphing, Box Plots, Data Analysis, Histogram, Statistical Analysis, Unsupervised Learning, Color Theory

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.1K reviews

      Mixed · Course · 1 - 4 Weeks

    • I

      IBM

      Exploratory Data Analysis for Machine Learning

      Skills you'll gain: Exploratory Data Analysis, Feature Engineering, Data Cleansing, Data Access, Data Analysis, Statistical Inference, Statistical Hypothesis Testing, Data Quality, Data Science, Probability & Statistics, Jupyter, Machine Learning, Data Manipulation, Pandas (Python Package), Statistical Analysis, Data Transformation, Artificial Intelligence

      4.6
      Rating, 4.6 out of 5 stars
      ·
      2.2K reviews

      Intermediate · Course · 1 - 3 Months

    • Status: Free
      Free
      U

      University of Leeds

      Exploratory Data Analysis

      Skills you'll gain: Exploratory Data Analysis, Data Cleansing, Statistical Modeling, Statistics, Data Analysis, Statistical Inference, R Programming, Descriptive Statistics, Box Plots, Data Visualization Software, Histogram, Probability, Simulations

      4.6
      Rating, 4.6 out of 5 stars
      ·
      9 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Unlock Access to 10,000+ courses with a subscription.

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    • C

      Coursera Project Network

      Perform exploratory data analysis on retail data with Python

      Skills you'll gain: Data-Driven Decision-Making, Business Analytics, Data Analysis, Data Cleansing, Statistical Analysis, Exploratory Data Analysis, Descriptive Statistics, Data Manipulation, Matplotlib, Customer Analysis, Analytical Skills, Trend Analysis, Pandas (Python Package), Python Programming, Jupyter

      4.5
      Rating, 4.5 out of 5 stars
      ·
      15 reviews

      Intermediate · Guided Project · Less Than 2 Hours

    • I

      IBM

      Data Analysis with Python

      Skills you'll gain: Data Wrangling, Data Cleansing, Data Analysis, Data Manipulation, Data Import/Export, Exploratory Data Analysis, Data Science, Statistical Analysis, Descriptive Statistics, Regression Analysis, Predictive Modeling, Pandas (Python Package), Scikit Learn (Machine Learning Library), Machine Learning Methods, Data Pipelines, NumPy

      4.7
      Rating, 4.7 out of 5 stars
      ·
      19K reviews

      Intermediate · Course · 1 - 3 Months

    What brings you to Coursera today?

    • I

      IBM

      Python for Data Science, AI & Development

      Skills you'll gain: Jupyter, Automation, Web Scraping, Python Programming, Data Manipulation, Data Import/Export, Scripting, Data Structures, Data Processing, Data Collection, Application Programming Interface (API), Pandas (Python Package), Programming Principles, NumPy, Object Oriented Programming (OOP), Computer Programming

      4.6
      Rating, 4.6 out of 5 stars
      ·
      41K reviews

      Beginner · Course · 1 - 3 Months

    • U

      University of Illinois Urbana-Champaign

      Tools for Exploratory Data Analysis in Business

      Skills you'll gain: Extract, Transform, Load, Alteryx, Data Presentation, Interactive Data Visualization, Business Analytics, Statistical Visualization, Data Visualization Software, Power BI, Exploratory Data Analysis, Analytical Skills, Business Intelligence, Ggplot2, Data Processing, Data Analysis, Data Transformation, R Programming, Data Manipulation

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      50 reviews

      Beginner · Course · 1 - 4 Weeks

    • G

      Google

      Google Advanced Data Analytics

      Skills you'll gain: Exploratory Data Analysis, Data Storytelling, Statistical Hypothesis Testing, Data Ethics, Data Presentation, Data Visualization Software, Sampling (Statistics), Regression Analysis, Feature Engineering, Data Transformation, Descriptive Statistics, Data Visualization, Tableau Software, Data Manipulation, Statistical Analysis, Probability Distribution, Statistical Methods, Applied Machine Learning, Object Oriented Programming (OOP), Data Analysis

      Build toward a degree

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6K reviews

      Advanced · Professional Certificate · 3 - 6 Months

    • C

      Coursera Project Network

      Exploratory Data Analysis With Python and Pandas

      Skills you'll gain: Exploratory Data Analysis, Correlation Analysis, Matplotlib, Seaborn, Data Cleansing, Data Visualization, Pandas (Python Package), Data Analysis, Data Manipulation, NumPy, Statistical Analysis

      4.5
      Rating, 4.5 out of 5 stars
      ·
      435 reviews

      Beginner · Guided Project · Less Than 2 Hours

    • Status: Free
      Free
      S

      Stanford University

      Introduction to Statistics

      Skills you'll gain: Descriptive Statistics, Statistics, Statistical Methods, Sampling (Statistics), Statistical Analysis, Data Analysis, Statistical Modeling, Statistical Hypothesis Testing, Regression Analysis, Statistical Inference, Probability, Exploratory Data Analysis, Quantitative Research, Data Collection, Probability Distribution

      4.6
      Rating, 4.6 out of 5 stars
      ·
      4K reviews

      Beginner · Course · 1 - 3 Months

    • C

      Coursera Project Network

      Conducting Exploratory Data Analysis

      Skills you'll gain: Matplotlib, Seaborn, Exploratory Data Analysis, Data Visualization, Pandas (Python Package), Plot (Graphics), Statistical Visualization, Data Visualization Software, Scatter Plots, Data Analysis, Box Plots, Python Programming

      Intermediate · Guided Project · Less Than 2 Hours

    • G

      Google

      Data Analysis with R Programming

      Skills you'll gain: Rmarkdown, Ggplot2, R Programming, Data Visualization, Data Analysis, Tidyverse (R Package), Data Visualization Software, Statistical Programming, Data Cleansing, Data Manipulation, Programming Principles, Data Transformation, Integrated Development Environments, Data Structures

      4.8
      Rating, 4.8 out of 5 stars
      ·
      11K reviews

      Beginner · Course · 1 - 3 Months

    What brings you to Coursera today?

      Exploratory Data Analysis learners also search

      Data Analysis
      Analysis
      R
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      Beginner Data Analysis
      Beginner Data Analysis
      Data Analysis Projects
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      In summary, here are 10 of our most popular exploratory data analysis courses

      • Exploratory Data Analysis: Johns Hopkins University
      • Exploratory Data Analysis for Machine Learning: IBM
      • Exploratory Data Analysis: University of Leeds
      • Perform exploratory data analysis on retail data with Python: Coursera Project Network
      • Data Analysis with Python: IBM
      • Python for Data Science, AI & Development: IBM
      • Tools for Exploratory Data Analysis in Business: University of Illinois Urbana-Champaign
      • Google Advanced Data Analytics: Google
      • Exploratory Data Analysis With Python and Pandas: Coursera Project Network
      • Introduction to Statistics: Stanford University

      Skills you can learn in Probability And Statistics

      R Programming (19)
      Inference (16)
      Linear Regression (12)
      Statistical Analysis (12)
      Statistical Inference (11)
      Regression Analysis (10)
      Biostatistics (9)
      Bayesian (7)
      Logistic Regression (7)
      Probability Distribution (7)
      Bayesian Statistics (6)
      Medical Statistics (6)

      Frequently Asked Questions about Exploratory Data Analysis

      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.‎

      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.‎

      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.‎

      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.‎

      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.‎

      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.‎

      This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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