What is statistical analysis?

Statistical analysis is the process of collecting and organizing data in order to observe patterns in that data. This discipline examines numerical data for trends, and it has practical applications across multiple industries and business sectors. Statistical analysts can examine large or small amounts of data to determine what the data shows—and what should be done with that information. The business world has relied on statistical analysis for years, but the advent of computer technology has made analyzing large amounts of data increasingly easy. Computers allow statisticians to analyze copious amounts of data and look for more specific patterns and trends than ever before.

Why is statistical analysis important?

Statistical analysis is important because it helps you make sense—and make use—of data. No matter what industry you work in, you see data every day. You may not know how to interpret the data, or there may just be too much data to understand on its own merits. Using statistical analysis can help you interpret key patterns in your data and sort out extraneous information to understand what the data means and what you should do about it. If you have a background in statistical analysis, you can also test or disprove any analysis that you receive from someone else as well.

What are typical careers that use statistical analysis?

A thorough knowledge of statistical analysis can help you find a career in the financial sector, information technology, or other areas of the business world. You can find jobs giving businesses advice on how to invest or how to manage their money more efficiently, or you can consult with companies on how to budget for large projects. Market research jobs rely on statistical analysis skills, while software engineers analyze data to develop and test new and updated applications. Data scientists work with various organizations to help them identify patterns and trends in their field of business.

How can online courses on Coursera help me learn statistical analysis?

From courses that introduce you to the basics of statistical analysis to more specific topics like hypothesis testing, data analysis in the health care industry, or risk management, you can gain a confident base of knowledge from online courses on Coursera. Specific topics you can discover include using logistic regression in R, survival analysis in R, bias and validity in epidemiology, statistics with Python, and data analytics.

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1389 resultados referentes a "statistical analysis"

Imperial College London
Statistical Analysis with R for Public Health
Habilidades que você terá: Analysis, Bayesian Statistics, Business Analysis, Correlation And Dependence, Data Analysis, Econometrics, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Programming, Statistical Tests
4.7
(1.6k avaliações)
Beginner · Specialization
University of Michigan
Inferential Statistical Analysis with Python
Habilidades que você terá: Data Analysis, Analysis, Computer Programming, Statistical Hypothesis Testing, Hypothesis, Statistical Analysis, Hypothesis Testing, Confidence, Experiment, Statistical Tests, Probability & Statistics, Statistical Programming, General Statistics, Business Analysis, Statistical Inference, Python Programming
4.6
(789 avaliações)
Intermediate · Course
Google
Google Data Analytics
Habilidades que você terá: Algorithms, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Type, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, General Statistics, Ggplot2, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, Network Security, Other Programming Languages, Plot (Graphics), Presentation, Probability & Statistics, Problem Solving, Product Design, Programming Principles, Project Management, R Programming, Research and Design, SQL, Security Engineering, Security Strategy, Small Data, Software, Software Engineering, Software Security, Spreadsheet Software, Statistical Analysis, Statistical Programming, Storytelling, Strategy and Operations, Theoretical Computer Science, Visual Design
4.8
(63.4k avaliações)
Beginner · Professional Certificate
IBM
IBM Data Science
Habilidades que você terá: Algebra, Algorithms, Business Analysis, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Data Mining, Data Structures, Data Visualization, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Interactive Data Visualization, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Modeling, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, 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
(93.6k avaliações)
Beginner · Professional Certificate
IBM
IBM Data Analyst
Habilidades que você terá: Algebra, Analytics, Apache, Big Data, Business Analysis, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Leadership and Management, Linearity, Machine Learning, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Professional Development, Python Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science
4.6
(50.5k avaliações)
Beginner · Professional Certificate
IBM
Data Science Fundamentals with Python and SQL
Habilidades que você terá: Analysis, Basic Descriptive Statistics, Business Analysis, Computational Logic, Computer Programming, Data Analysis, Data Management, Data Visualization, Database Administration, Database Application, Databases, Extract, Transform, Load, General Statistics, Machine Learning, Mathematical Theory & Analysis, Mathematics, Pandas, Plot (Graphics), Probability & Statistics, Probability Distribution, Python Programming, R Programming, Regression, SPSS, SQL, Statistical Analysis, Statistical Programming, Statistical Tests, Statistical Visualization, Theoretical Computer Science
4.5
(46.4k avaliações)
Beginner · Specialization
IBM
IBM Data Analytics with Excel and R
Habilidades que você terá: Advertising, Analysis, Analytics, Apache, Big Data, Business Analysis, Communication, Computational Logic, Computer Programming, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Structures, Data Visualization, Data Visualization Software, Data Warehousing, Databases, Extract, Transform, Load, General Statistics, Leadership and Management, Marketing, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Professional Development, Programming Principles, R Programming, Regression, SQL, Software, Spreadsheet Software, Statistical Analysis, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science
4.7
(9.7k avaliações)
Beginner · Professional Certificate
IBM
Applied Data Science
Habilidades que você terá: Algebra, Algorithms, Analysis, Business Analysis, Computational Logic, Computer Programming, Computer Programming Tools, Correlation And Dependence, Data Analysis, Data Management, Data Mining, Data Visualization, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Geovisualization, Interactive Data Visualization, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Modeling, Pandas, Plot (Graphics), Probability & Statistics, Python Programming, Regression, Regression Analysis, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science
4.6
(38k avaliações)
Beginner · Specialization
Google
Foundations: Data, Data, Everywhere
Habilidades que você terá: Entrepreneurship, Spreadsheet, Analysis, Business Analysis, Leadership and Management, SQL, Statistical Analysis, Product Design, Databases, Analytics, Data-Informed Decision-Making, Spreadsheet Software, Software Engineering, Application Development, Data Management, Data Analysis, Data Visualization, Design and Product, Statistical Programming, Decision Making
4.8
(45.8k avaliações)
Beginner · Course

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Em resumo, aqui estão 10 dos nossos cursos mais populares statistical analysis

Statistical Analysis with R for Public Health: Imperial College London
Inferential Statistical Analysis with Python: University of Michigan
Introduction to Statistical Analysis: Hypothesis Testing: SAS
Statistical Analysis using Python Numpy: Coursera Project Network
Visualization for Statistical Analysis: Coursera Project Network
Google Data Analytics: Google
IBM Data Science: IBM
IBM Data Analyst: IBM
Data Science Fundamentals with Python and SQL: IBM
IBM Data Analytics with Excel and R: IBM

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Perguntas frequentes sobre Análise estatística

Statistical analysis is the process of collecting and organizing data in order to observe patterns in that data. This discipline examines numerical data for trends, and it has practical applications across multiple industries and business sectors. Statistical analysts can examine large or small amounts of data to determine what the data shows—and what should be done with that information. The business world has relied on statistical analysis for years, but the advent of computer technology has made analyzing large amounts of data increasingly easy. Computers allow statisticians to analyze copious amounts of data and look for more specific patterns and trends than ever before.‎
Statistical analysis is important because it helps you make sense—and make use—of data. No matter what industry you work in, you see data every day. You may not know how to interpret the data, or there may just be too much data to understand on its own merits. Using statistical analysis can help you interpret key patterns in your data and sort out extraneous information to understand what the data means and what you should do about it. If you have a background in statistical analysis, you can also test or disprove any analysis that you receive from someone else as well.‎
A thorough knowledge of statistical analysis can help you find a career in the financial sector, information technology, or other areas of the business world. You can find jobs giving businesses advice on how to invest or how to manage their money more efficiently, or you can consult with companies on how to budget for large projects. Market research jobs rely on statistical analysis skills, while software engineers analyze data to develop and test new and updated applications. Data scientists work with various organizations to help them identify patterns and trends in their field of business.‎
From courses that introduce you to the basics of statistical analysis to more specific topics like hypothesis testing, data analysis in the health care industry, or risk management, you can gain a confident base of knowledge from online courses on Coursera. Specific topics you can discover include using logistic regression in R, survival analysis in R, bias and validity in epidemiology, statistics with Python, and data analytics.‎

Este conteúdo de Perguntas frequentes foi disponibilizado apenas para fins informativos. Os aprendizes são aconselhados a realizar pesquisas adicionais para garantir que os cursos e outras credenciais buscadas atendam a seus objetivos pessoais, profissionais e financeiros.