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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

    • U

      University of Pennsylvania

      Introduction to Financial Accounting

      Skills you'll gain: Financial Statements, Financial Statement Analysis, Financial Reporting, Financial Accounting, Balance Sheet, Accrual Accounting, Financial Analysis, Cash Flows, Income Statement, Bookkeeping, Revenue Recognition, Depreciation

      4.7
      Rating, 4.7 out of 5 stars
      ·
      8.3K reviews

      Mixed · Course · 1 - 4 Weeks

    • Status: New
      New
      M

      Microsoft

      Microsoft Data Visualization

      Skills you'll gain: Data Storytelling, Data Analysis Expressions (DAX), Data Presentation, Power BI, Data Ethics, Dashboard, Data Visualization Software, Data Modeling, Data Governance, Extract, Transform, Load, Interactive Data Visualization, Business Intelligence, Data Analysis, Statistical Analysis, Data Architecture, Correlation Analysis, Data Cleansing, Data Transformation, Database Design, Statistical Visualization

      4.6
      Rating, 4.6 out of 5 stars
      ·
      59 reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • D

      DeepLearning.AI

      Introduction to Data Engineering

      Skills you'll gain: Data Pipelines, Data Architecture, Requirements Analysis, Amazon Web Services, Data Warehousing, Data Modeling, Data Processing, System Requirements, Performance Tuning, Security Controls, Data Transformation, Scalability

      4.8
      Rating, 4.8 out of 5 stars
      ·
      359 reviews

      Intermediate · Course · 1 - 4 Weeks

    • Status: New
      New
      V

      Vanderbilt University

      Generative AI Data Analyst

      Skills you'll gain: Data Storytelling, Prompt Engineering, Data Presentation, ChatGPT, Data Synthesis, Microsoft Excel, Productivity, Infographics, Document Management, SQL, Generative AI, Artificial Intelligence, Data Visualization, Data Cleansing, Large Language Modeling, Data Import/Export, Statistical Reporting, Data Integration, Data Transformation, Data Analysis

      4.8
      Rating, 4.8 out of 5 stars
      ·
      6.2K reviews

      Beginner · Specialization · 3 - 6 Months

    • A

      Amazon Web Services

      Getting Started with Data Analytics on AWS

      Skills you'll gain: Descriptive Analytics, Analytics, Data Analysis, Dashboard, Data Literacy, AWS Identity and Access Management (IAM), Data Visualization, Amazon Web Services, Big Data, Amazon S3, Business Intelligence, Cloud Computing

      4.5
      Rating, 4.5 out of 5 stars
      ·
      887 reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: AI skills
      AI skills
      I

      IBM

      IBM AI Product Manager

      Skills you'll gain: Prompt Engineering, Generative AI, New Product Development, Product Management, Product Lifecycle Management, Product Development, Innovation, Product Roadmaps, ChatGPT, Large Language Modeling, Product Planning, Commercialization, Product Strategy, Artificial Intelligence, Project Management Life Cycle, Data Ethics, OpenAI, Stakeholder Engagement, Stakeholder Management, Competitive Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      26K reviews

      Beginner · Professional Certificate · 3 - 6 Months

    • G

      Google

      Foundations of Cybersecurity

      Skills you'll gain: Cybersecurity, Security Controls, Cyber Attacks, Security Management, Cyber Security Strategy, Incident Response, Cyber Risk, Security Information and Event Management (SIEM), Information Assurance, Data Ethics, Network Analysis, Ethical Standards And Conduct

      Build toward a degree

      4.9
      Rating, 4.9 out of 5 stars
      ·
      33K reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Pennsylvania

      Business Foundations

      Skills you'll gain: Operations Management, Return On Investment, Operational Efficiency, Financial Statements, Financial Statement Analysis, Human Capital, Go To Market Strategy, Process Analysis, Branding, People Management, Financial Reporting, Capital Budgeting, Financial Accounting, Financial Modeling, Balance Sheet, Consumer Behaviour, Human Resource Strategy, Marketing Planning, Peer Review, Analysis

      4.7
      Rating, 4.7 out of 5 stars
      ·
      26K reviews

      Beginner · Specialization · 3 - 6 Months

    • D

      DeepLearning.AI

      Supervised Machine Learning: Regression and Classification

      Skills you'll gain: Supervised Learning, Jupyter, Scikit Learn (Machine Learning Library), Machine Learning, NumPy, Predictive Modeling, Feature Engineering, Artificial Intelligence, Classification And Regression Tree (CART), Python Programming, Regression Analysis, Unsupervised Learning, Statistical Modeling

      4.9
      Rating, 4.9 out of 5 stars
      ·
      28K reviews

      Beginner · Course · 1 - 4 Weeks

    • U

      University of Michigan

      Statistics with Python

      Skills you'll gain: Statistical Hypothesis Testing, Sampling (Statistics), Statistical Modeling, Statistical Methods, Statistical Inference, Statistics, Bayesian Statistics, Data Visualization, Matplotlib, Statistical Visualization, Probability & Statistics, Statistical Analysis, Jupyter, Statistical Programming, Regression Analysis, Data Visualization Software, Predictive Modeling, Data Analysis, Exploratory Data Analysis, Descriptive Statistics

      4.6
      Rating, 4.6 out of 5 stars
      ·
      3.3K reviews

      Beginner · Specialization · 1 - 3 Months

    • G

      Google

      Foundations of Digital Marketing and E-commerce

      Skills you'll gain: Data Storytelling, Customer experience strategy (CX), Performance Measurement, Data-Driven Decision-Making, E-Commerce, Marketing Strategies, Digital Marketing, Branding, Marketing Channel, Marketing Analytics, Search Engine Marketing, Search Engine Optimization, Social Media Marketing, Target Audience

      4.8
      Rating, 4.8 out of 5 stars
      ·
      27K reviews

      Beginner · Course · 1 - 4 Weeks

    • Status: Free
      Free
      T

      The Hong Kong University of Science and Technology

      Python and Statistics for Financial Analysis

      Skills you'll gain: Statistical Inference, Statistical Methods, Pandas (Python Package), Probability & Statistics, Risk Analysis, Financial Trading, Financial Data, Data Manipulation, Statistical Analysis, Regression Analysis, Financial Analysis, Jupyter, Financial Modeling

      4.4
      Rating, 4.4 out of 5 stars
      ·
      4.5K reviews

      Intermediate · Course · 1 - 4 Weeks

    Exploratory Data Analysis learners also search

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

    • Introduction to Financial Accounting: University of Pennsylvania
    • Microsoft Data Visualization: Microsoft
    • Introduction to Data Engineering: DeepLearning.AI
    • Generative AI Data Analyst: Vanderbilt University
    • Getting Started with Data Analytics on AWS: Amazon Web Services
    • IBM AI Product Manager: IBM
    • Foundations of Cybersecurity: Google
    • Business Foundations: University of Pennsylvania
    • Supervised Machine Learning: Regression and Classification : DeepLearning.AI
    • Statistics with Python: University of Michigan

    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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