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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Statistical Analysis

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「statistical analysis」の1891件の結果

Imperial College London
Statistical Analysis with R for Public Health
習得できるスキル： Probability & Statistics, General Statistics, Regression, Business Analysis, Statistical Analysis, Data Analysis, Machine Learning, Machine Learning Algorithms, Statistical Programming, Mathematics, Statistical Tests, R Programming, Probability Distribution, Correlation And Dependence, Basic Descriptive Statistics, Econometrics, Estimation, Bayesian Statistics, Exploratory Data Analysis, Critical Thinking, Data Analysis Software, Forecasting
4.7
（1.7k件のレビュー）
Beginner · Specialization · 3-6 Months
University of Michigan
Inferential Statistical Analysis with Python
習得できるスキル： Probability & Statistics, Business Analysis, Computer Programming, General Statistics, Statistical Programming, Python Programming, Data Analysis, Statistical Analysis, Statistical Tests, Experiment, Basic Descriptive Statistics, Data Analysis Software, Mathematics
4.6
（837件のレビュー）
Intermediate · Course · 1-4 Weeks
SAS
Introduction to Statistical Analysis: Hypothesis Testing
習得できるスキル： Business Analysis, Data Analysis, General Statistics, Probability & Statistics, SAS (Software), Statistical Analysis, Statistical Programming, Statistical Tests, Regression, Correlation And Dependence, Advertising, Algebra, Communication, Journalism, Marketing
4.6
（80件のレビュー）
Intermediate · Course · 1-4 Weeks
Google
Google Data Analytics
習得できるスキル： Data Analysis, Data Science, Statistical Programming, Business Analysis, SQL, Spreadsheet Software, Business, Data Visualization, Data Management, R Programming, Exploratory Data Analysis, Statistical Visualization, Communication, Statistical Analysis, Data Analysis Software, Business Communication, Data Structures, Data Visualization Software, Tableau Software, Big Data, Cloud Computing, Collaboration, Conflict Management, Critical Thinking, Customer Analysis, General Statistics, Leadership and Management, Plot (Graphics), Probability & Statistics, Small Data, Algorithms, Application Development, Budget Management, Change Management, Computational Logic, Computer Architecture, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Mining, Data Model, Database Administration, Database Design, Databases, Decision Making, Design and Product, Distributed Computing Architecture, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, Full-Stack Web Development, Interactive Data Visualization, Machine Learning, Mathematical Theory & Analysis, Mathematics, Network Security, Other Programming Languages, Problem Solving, Product Design, Programming Principles, Project Management, Research and Design, Security Engineering, Security Strategy, Software Engineering, Software Security, Storytelling, Strategy and Operations, Theoretical Computer Science, Visual Design, Visualization (Computer Graphics), Web Development
4.8
（101.4k件のレビュー）
Beginner · Professional Certificate · 3-6 Months
IBM Skills Network
IBM Data Science
習得できるスキル： Python Programming, Data Science, Data Analysis, Data Structures, Statistical Programming, Machine Learning, Data Mining, Regression, Machine Learning Algorithms, Data Visualization, General Statistics, Basic Descriptive Statistics, SQL, Applied Machine Learning, Statistical Analysis, Computer Programming Tools, Data Analysis Software, Machine Learning Software, Software Visualization, Databases, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Data Management, Database Theory, Data Visualization Software, R Programming, Statistical Machine Learning, Statistical Tests, Deep Learning, Probability & Statistics, Database Application, Extract, Transform, Load, Plot (Graphics), Devops Tools, SPSS, Estimation, Interactive Data Visualization, Algorithms, Database Administration, Geovisualization, Reinforcement Learning, Theoretical Computer Science, Big Data, Business Analysis, Computational Logic, Correlation And Dependence, Econometrics, Entrepreneurship, Marketing, Mathematical Theory & Analysis, Mathematics, Spreadsheet Software, Storytelling, Supply Chain Systems, Supply Chain and Logistics, Writing
4.6
（106.1k件のレビュー）
Beginner · Professional Certificate · 3-6 Months
IBM Skills Network
IBM Data Analyst
習得できるスキル： Data Analysis, Python Programming, Data Visualization, Exploratory Data Analysis, Basic Descriptive Statistics, Data Structures, Statistical Programming, Data Science, Statistical Visualization, Plot (Graphics), Data Management, General Statistics, SQL, Databases, Business Analysis, Microsoft Excel, Spreadsheet Software, Data Mining, Statistical Analysis, Programming Principles, Probability & Statistics, Regression, Machine Learning, Algebra, Computer Programming, Data Analysis Software, Database Theory, Probability Distribution, Applied Machine Learning, Statistical Tests, Big Data, Correlation And Dependence, Data Visualization Software, Database Application, Estimation, NoSQL, Geovisualization, ArcGIS, Cloud Computing, Data Warehousing, Database Administration, Extract, Transform, Load, HTML and CSS, Knitr, Mathematics, Minitab, PostgreSQL, R Programming, SAS (Software), SPSS, Apache, Computational Logic, Computer Programming Tools, Econometrics, Leadership and Management, Mathematical Theory & Analysis, Operating Systems, Professional Development, Statistical Machine Learning, System Programming, Theoretical Computer Science
4.6
（60.7k件のレビュー）
Beginner · Professional Certificate · 3-6 Months
IBM Skills Network
Data Science Fundamentals with Python and SQL
習得できるスキル： Data Structures, Python Programming, Data Analysis, SQL, Statistical Programming, Computer Programming Tools, Data Analysis Software, General Statistics, Machine Learning Software, Probability & Statistics, Software Visualization, Databases, Programming Principles, Algebra, Database Theory, Business Analysis, Data Visualization Software, R Programming, Statistical Analysis, Statistical Tests, Data Management, Data Science, Basic Descriptive Statistics, Probability Distribution, Database Application, Devops Tools, Machine Learning Algorithms, SPSS, Correlation And Dependence, Data Visualization, Exploratory Data Analysis, Plot (Graphics), Regression, Statistical Visualization, Computer Programming, Database Administration, Extract, Transform, Load, Computational Logic, Machine Learning, Mathematical Theory & Analysis, Mathematics, Theoretical Computer Science
4.5
（52.7k件のレビュー）
Beginner · Specialization · 3-6 Months
IBM Skills Network
IBM Data Engineering
習得できるスキル： Data Management, Databases, Data Architecture, Data Structures, Big Data, Database Theory, SQL, Database Administration, Apache, Extract, Transform, Load, Python Programming, Database Application, Data Model, Data Warehousing, Data Analysis, NoSQL, Data Engineering, Distributed Computing Architecture, Database Design, Operating Systems, System Programming, System Software, Programming Principles, Statistical Programming, Computer Architecture, Algebra, PostgreSQL, Applied Machine Learning, Correlation And Dependence, Feature Engineering, General Statistics, Graph Theory, Machine Learning, Machine Learning Algorithms, Machine Learning Software, Regression, Statistical Analysis, Statistical Machine Learning, Data Visualization, Data Visualization Software, Basic Descriptive Statistics, Exploratory Data Analysis, Cloud Applications, Cloud Computing, Data Science, DevOps, Kubernetes, Leadership and Management, Network Architecture, Network Security, Other Programming Languages, Professional Development, Security Engineering, Accounting, Algorithms, Business Analysis, Cloud Engineering, Computational Logic, Computational Thinking, Computer Networking, Computer Programming, Computer Programming Tools, Hardware Design, IBM Cloud, Interactive Data Visualization, Linux, Mathematical Theory & Analysis, Mathematics, Microarchitecture, Project Management, Security Strategy, Software Architecture, Software Engineering, Strategy and Operations, Theoretical Computer Science
4.6
（40.4k件のレビュー）
Beginner · Professional Certificate · 3-6 Months
IBM Skills Network
Applied Data Science
習得できるスキル： Python Programming, Data Analysis, Data Science, Statistical Programming, Data Structures, Data Visualization, Basic Descriptive Statistics, Programming Principles, Exploratory Data Analysis, Computer Programming, Statistical Visualization, Algebra, Machine Learning, Applied Machine Learning, Data Mining, General Statistics, Regression, Statistical Analysis, Statistical Tests, Data Management, Extract, Transform, Load, Plot (Graphics), Interactive Data Visualization, Machine Learning Algorithms, SQL, Geovisualization, Algorithms, Business Analysis, Computational Logic, Computer Programming Tools, Correlation And Dependence, Data Analysis Software, Databases, Econometrics, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Spreadsheet Software, Statistical Machine Learning, Theoretical Computer Science
4.6
（43.2k件のレビュー）
Beginner · Specialization · 3-6 Months
IBM Skills Network
IBM Data Analytics with Excel and R
習得できるスキル： Data Analysis, R Programming, Data Visualization, Plot (Graphics), Data Management, SQL, Exploratory Data Analysis, Data Mining, Databases, Basic Descriptive Statistics, General Statistics, Data Visualization Software, Statistical Programming, Data Analysis Software, Interactive Data Visualization, Statistical Visualization, Statistical Analysis, Big Data, Microsoft Excel, Probability & Statistics, Business Analysis, Spreadsheet Software, Database Theory, Regression, Statistical Tests, Data Science, Data Structures, Software Visualization, Machine Learning, User Experience, Probability Distribution, NoSQL, Python Programming, Applied Machine Learning, Deep Learning, Estimation, Geovisualization, Linear Algebra, Machine Learning Algorithms, Machine Learning Software, SAS (Software), Spatial Data Analysis, Statistical Machine Learning, Cloud Computing, Data Architecture, Data Model, Data Warehousing, Database Administration, Database Application, Database Design, Mathematics, Visualization (Computer Graphics), Advertising, Apache, Communication, Computational Logic, Computer Programming, Extract, Transform, Load, Leadership and Management, Marketing, Operating Systems, Professional Development, Programming Principles, System Programming, Theoretical Computer Science
4.7
（14.7k件のレビュー）
Beginner · Professional Certificate · 3-6 Months

statistical analysisに関連する検索

statistical analysis with r for public health

statistical analysis using python numpy

inferential statistical analysis with python

introduction to statistical analysis: hypothesis testing

visualization for statistical analysis

business statistics and analysis

sas statistical business analyst

business statistics and analysis capstone

要約して、statistical analysis の人気コース10選をご紹介します。

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
Visualization for Statistical Analysis: Coursera Project Network
Statistical Analysis using Python Numpy: Coursera Project Network
Google Data Analytics: Google
IBM Data Science: IBM Skills Network
IBM Data Analyst: IBM Skills Network
Data Science Fundamentals with Python and SQL: IBM Skills Network
IBM Data Engineering: IBM Skills Network

Business Essentialsで学べるスキル

統計解析に関するよくある質問

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.‎