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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

    • I

      IBM

      Excel Basics for Data Analysis

      Skills you'll gain: Excel Formulas, Microsoft Excel, Data Cleansing, Data Analysis, Data Import/Export, Spreadsheet Software, Data Wrangling, Data Quality, Pivot Tables And Charts, Google Sheets, Data Manipulation, Data Visualization Software, Information Privacy

      4.8
      Rating, 4.8 out of 5 stars
      ·
      9.5K reviews

      Beginner · Course · 1 - 3 Months

    • T

      Tableau Learning Partner

      Data Analysis with Tableau

      Skills you'll gain: Exploratory Data Analysis, Tableau Software, Statistical Visualization, Data Analysis, Descriptive Statistics, Predictive Analytics, Data Processing, Box Plots, Statistical Analysis, Data Cleansing, Scatter Plots, Data Visualization Software, Trend Analysis, Histogram, Data Manipulation, Regression Analysis, Correlation Analysis

      4.4
      Rating, 4.4 out of 5 stars
      ·
      87 reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      What is Data Science?

      Skills you'll gain: Data Literacy, Data Mining, Big Data, Cloud Computing, Data Analysis, Data Science, Digital Transformation, Data-Driven Decision-Making, Deep Learning, Machine Learning, Artificial Intelligence

      4.7
      Rating, 4.7 out of 5 stars
      ·
      75K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Digital Marketing & E-commerce

      Skills you'll gain: Media Planning, Search Engine Marketing, Email Marketing, Data Storytelling, Social Media Strategy, Search Engine Optimization, Social Media Marketing, Content Creation, Order Fulfillment, Google Ads, Online Advertising, A/B Testing, Social Media Campaigns, Target Audience, Digital Marketing, E-Commerce, Loyalty Programs, Customer Retention, Customer experience strategy (CX), Campaign Management

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      39K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: New AI skills
      New AI skills
      G

      Google

      Google Project Management:

      Skills you'll gain: Quality Management, Project Management Life Cycle, Project Scoping, Project Closure, Project Management, Project Planning, Continuous Improvement Process, Agile Project Management, Project Controls, Backlogs, Quality Assessment, Team Management, Project Documentation, Agile Methodology, Communication Planning, Project Schedules, Change Management, Milestones (Project Management), Organizational Structure, Data Storytelling

      Build toward a degree

      4.8
      Rating, 4.8 out of 5 stars
      ·
      128K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • Status: AI skills
      AI skills
      I

      IBM

      IBM Data Engineering

      Skills you'll gain: NoSQL, Data Warehousing, Apache Hadoop, Extract, Transform, Load, Apache Airflow, Web Scraping, Linux Commands, Database Design, SQL, IBM Cognos Analytics, MySQL, Apache Spark, Data Pipelines, Apache Kafka, Database Management, Bash (Scripting Language), Data Store, Jupyter, Generative AI, Professional Networking

      Build toward a degree

      4.6
      Rating, 4.6 out of 5 stars
      ·
      58K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • I

      IBM

      Generative AI for Data Analysts

      Skills you'll gain: Prompt Engineering, Generative AI, ChatGPT, Data Storytelling, OpenAI, Analytics, Data Analysis, Artificial Intelligence and Machine Learning (AI/ML), Dashboard, Large Language Modeling, Data Ethics, Artificial Intelligence, Program Development, Data Visualization Software, SQL, Python Programming, Query Languages, Image Analysis, Content Creation, Virtual Environment

      4.7
      Rating, 4.7 out of 5 stars
      ·
      6.3K reviews

      Intermediate · Specialization · 1 - 3 Months

    • S

      S.P. Jain Institute of Management and Research

      Data Analysis

      Skills you'll gain: Data Analysis, Quantitative Research, Statistical Hypothesis Testing, Sampling (Statistics), Descriptive Statistics, Business Analytics, Regression Analysis, Business Mathematics, Data-Driven Decision-Making, Statistical Analysis, Probability Distribution, Microsoft Excel, Statistical Inference, Probability, Variance Analysis, Estimation

      Build toward a degree

      Beginner · Course · 1 - 3 Months

    • U

      University of California San Diego

      Data Structures and Algorithms

      Skills you'll gain: Data Structures, Graph Theory, Algorithms, Network Routing, Program Development, Debugging, Network Model, Bioinformatics, Operations Research, Data Storage, Development Testing, Test Engineering, Software Testing, Theoretical Computer Science, Computational Thinking, Network Analysis, Test Case, Programming Principles, Computer Programming, Epidemiology

      4.6
      Rating, 4.6 out of 5 stars
      ·
      17K reviews

      Intermediate · Specialization · 3 - 6 Months

    • I

      IBM

      Introduction to Data Engineering

      Skills you'll gain: Data Store, Extract, Transform, Load, Data Architecture, Data Pipelines, Big Data, Data Warehousing, Data Governance, Apache Hadoop, Relational Databases, Apache Spark, Data Lakes, Databases, SQL, NoSQL, Data Security

      4.7
      Rating, 4.7 out of 5 stars
      ·
      3.1K reviews

      Beginner · Course · 1 - 4 Weeks

    • I

      IBM

      Data Analysis with R

      Skills you'll gain: Data Wrangling, Exploratory Data Analysis, Data Analysis, Data Transformation, R Programming, Data Manipulation, Data Visualization, Box Plots, Predictive Analytics, Statistical Analysis, Regression Analysis, Statistical Modeling, Correlation Analysis, Data Cleansing, Tidyverse (R Package), Supervised Learning

      4.7
      Rating, 4.7 out of 5 stars
      ·
      332 reviews

      Intermediate · Course · 1 - 3 Months

    • M

      Macquarie University

      Excel Skills for Data Analytics and Visualization

      Skills you'll gain: Pivot Tables And Charts, Data Storytelling, Power BI, Excel Formulas, Data Analysis Expressions (DAX), Data Visualization Software, Interactive Data Visualization, Microsoft Excel, Spreadsheet Software, Dashboard, Data Modeling, Data Cleansing, Data Manipulation, Excel Macros, Infographics, Data Transformation, Data Analysis Software, Data Validation, Data Analysis, Microsoft Power Platform

      4.8
      Rating, 4.8 out of 5 stars
      ·
      5.7K reviews

      Intermediate · Specialization · 3 - 6 Months

    Exploratory Data Analysis learners also search

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

    • Excel Basics for Data Analysis: IBM
    • Data Analysis with Tableau: Tableau Learning Partner
    • What is Data Science? : IBM
    • Google Digital Marketing & E-commerce: Google
    • Google Project Management:: Google
    • IBM Data Engineering: IBM
    • Generative AI for Data Analysts: IBM
    • Data Analysis: S.P. Jain Institute of Management and Research
    • Data Structures and Algorithms: University of California San Diego
    • Introduction to Data Engineering: IBM

    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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