What is 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.

Why learn exploratory data analysis?

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.

What career opportunities can arise from learning exploratory data analysis?

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.

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

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.

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149 résultats pour ''exploratory data analysis'

IBM Skills Network
Exploratory Data Analysis for Machine Learning
Compétences que vous acquerrez : Python Programming, NoSQL, Data Analysis, Analysis, Business Analysis, Bayesian Statistics, Hypothesis, General Statistics, Statistical Programming, Data Visualization, Machine Learning, Experiment, Computer Programming, Statistical Hypothesis Testing, Databases, Exploratory Data Analysis, Hypothesis Testing, Deep Learning, Probability & Statistics, Feature Engineering
4.6
(808 avis)
Intermediate · Course · 1-3 Months
Duke University
Excel to MySQL: Analytic Techniques for Business
Compétences que vous acquerrez : Algebra, Big Data, Business Analysis, Business Analytics, Business Communication, Business Process, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Critical Thinking, Data Analysis, Data Analysis Software, Data Management, Data Visualization, Data Visualization Software, Database Theory, Databases, Entrepreneurship, Exploratory Data Analysis, Finance, Financial Management, General Statistics, Investment Management, Leadership and Management, Marketing, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, Modeling, Presentation, Probability & Statistics, Probability Distribution, Problem Solving, Regression, Regression Analysis, Research and Design, Risk Management, SQL, Spreadsheet Software, Statistical Classification, Statistical Programming, Strategy and Operations, Tableau Software, Theoretical Computer Science
4.6
(14.7k avis)
Beginner · Specialization · 3-6 Months
IBM Skills Network
IBM Data Science
Compétences que vous acquerrez : Algebra, Algorithms, Business Analysis, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Clustering Algorithms, Data Management, Data Mining, Data Structures, Data Visualization, Database Administration, Database Application, Databases, Econometrics, Entrepreneurship, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Interactive Data Visualization, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, Recommender Systems, Regression, SPSS, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Supply Chain Systems, Supply Chain and Logistics, Theoretical Computer Science
4.6
(96.1k avis)
Beginner · Professional Certificate · 3-6 Months
University of Illinois at Urbana-Champaign
Tools for Exploratory Data Analysis in Business
Compétences que vous acquerrez : Analysis, Business Analytics, Business Analysis, Big Data, Analytics, Data Analysis, Data Visualization, Data Management, Statistical Programming, Rstudio, Interactivity, Probability & Statistics, R Programming, Exploratory Data Analysis
4.9
(15 avis)
Beginner · Course · 1-4 Weeks

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conducting exploratory data analysis

En résumé, voici 10 de nos cours exploratory data analysis les plus populaires

Exploratory Data Analysis: Johns Hopkins University
Exploratory Data Analysis with Seaborn: Coursera Project Network
Exploratory Data Analysis for Machine Learning: IBM Skills Network
Conducting Exploratory Data Analysis: Coursera Project Network
Excel to MySQL: Analytic Techniques for Business: Duke University
IBM Data Science: IBM Skills Network
Tools for Exploratory Data Analysis in Business: University of Illinois at Urbana-Champaign
Exploratory Data Analysis in R: Coursera Project Network
Exploratory Data Analysis with MATLAB: MathWorks
Exploratory Data Analysis With Python and Pandas: Coursera Project Network

Compétences que vous avez acquises en Probability And Statistics

La Programmation En R (19)

Inférence (16)

Régression Linéaire (12)

Analyse Statistique (12)

Inférence Statistique (11)

Analyse De Régression (10)

Biostatistiques (9)

Bayésien (7)

Régression Logistique (7)

Distribution De Probabilité (7)

Statistiques Bayésiennes (6)

Statistiques Médicales (6)

Questions fréquentes sur Exploration analytique de données

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.‎

Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.