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.

閲覧
Statistical Analysis

フィルター

「statistical analysis」の1502件の結果

Imperial College London
Statistical Analysis with R for Public Health
習得できるスキル： Algebra, Analysis, Basic Descriptive Statistics, Bayesian Statistics, Business Analysis, Correlation And Dependence, Data Analysis, Econometrics, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Probability & Statistics, Probability Distribution, Regression, Statistical Analysis, Statistical Programming, Statistical Tests, Supply Chain
4.7
（1.5k件のレビュー）
Beginner · Specialization · 3+ Months
University of Michigan
Inferential Statistical Analysis with Python
習得できるスキル： Hypothesis, Python Programming, General Statistics, Computer Programming, Probability & Statistics, Statistical Tests, Statistical Programming, Data Analysis, Statistical Analysis, Confidence, Business Analysis, Statistical Hypothesis Testing, Inference, Experiment, Hypothesis Testing, Statistical Inference
4.6
（781件のレビュー）
Intermediate · Course · 1-4 Weeks
Google
Google Data Analytics
習得できるスキル： Algorithms, Analysis, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computer Networking, Computer Programming, Computer Programming Tools, Cryptography, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Model, Data Structures, Data Visualization, Data Visualization Software, Database Administration, Database Design, Databases, Decision Making, Design and Product, Econometrics, Entrepreneurship, Experiment, Extract, Transform, Load, Feature Engineering, Finance, General Statistics, Leadership and Management, Machine Learning, 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, Spreadsheet Software, Statistical Analysis, Statistical Programming, Statistical Visualization, Storytelling, Strategy and Operations, Theoretical Computer Science
4.8
（57.6k件のレビュー）
Beginner · Professional Certificate · 3+ Months
IBM
IBM Data Science
習得できるスキル： Algebra, Algorithms, Analysis, Business Analysis, Cloud API, Cloud Computing, 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, Machine Learning, Machine Learning Algorithms, Marketing, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Probability & Statistics, Python Programming, R Programming, Regression, SPSS, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science
4.6
（91.6k件のレビュー）
Beginner · Professional Certificate · 3+ Months
IBM
IBM Data Analyst
習得できるスキル： Algebra, Analysis, 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 Visualization, Data Visualization Software, Data Warehousing, Database Administration, Database Application, Databases, Econometrics, Exploratory Data Analysis, Extract, Transform, Load, General Statistics, Machine Learning, Mathematical Theory & Analysis, Mathematics, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, Python Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, System Programming, Theoretical Computer Science
4.6
（48.8k件のレビュー）
Beginner · Professional Certificate · 3+ Months
IBM
IBM Data Analytics with Excel and R
習得できるスキル： Advertising, Analysis, Apache, Big Data, Business Analysis, Communication, Computer Programming, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Visualization, Data Visualization Software, Data Warehousing, Databases, Extract, Transform, Load, General Statistics, Marketing, Mathematics, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Probability & Statistics, R Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Programming, Statistical Visualization, System Programming
4.7
（8.8k件のレビュー）
Beginner · Professional Certificate · 3+ Months
IBM
Data Analysis and Visualization Foundations
習得できるスキル： Analysis, Apache, Big Data, Business Analysis, Computer Programming, Data Analysis, Data Analysis Software, Data Management, Data Mining, Data Visualization, Data Visualization Software, Data Warehousing, Databases, Extract, Transform, Load, General Statistics, Microsoft Excel, NoSQL, Operating Systems, Plot (Graphics), Spreadsheet Software, Statistical Analysis, Statistical Visualization, System Programming
4.7
（8.7k件のレビュー）
Beginner · Specialization · 3+ Months
IBM
Applied Data Science
習得できるスキル： Algebra, Analysis, Business Analysis, Cloud API, Cloud Computing, 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, Machine Learning, Mathematical Theory & Analysis, Mathematics, Plot (Graphics), Probability & Statistics, Python Programming, Regression, SQL, Spreadsheet Software, Statistical Analysis, Statistical Machine Learning, Statistical Programming, Statistical Visualization, Theoretical Computer Science
4.6
（37.2k件のレビュー）
Beginner · Specialization · 3+ Months
University of Virginia Darden School Foundation
IBM & Darden Digital Strategy
習得できるスキル： Analysis, Apache, Applied Machine Learning, Big Data, BlockChain, Business Analysis, Business Strategy, Cloud Computing, Cloud Infrastructure, Cloud Platforms, Cloud Storage, Computer Architecture, Computer Graphics, Computer Networking, Computer Programming, Computer Vision, Cryptography, Data Analysis, Data Management, Data Mining, Data Visualization, Data Warehousing, Databases, Deep Learning, Design and Product, DevOps, Entrepreneurship, Extract, Transform, Load, Finance, General Statistics, Human Computer Interaction, Interactive Design, Leadership and Management, Machine Learning, Machine Learning Algorithms, Market (Economics), Market Research, Marketing, Network Architecture, Network Security, NoSQL, Operating Systems, Product Strategy, Research and Design, Sales, Security Engineering, Software Architecture, Software As A Service, Software Engineering, Software Framework, Statistical Analysis, Strategy, Strategy and Operations, System Programming, Theoretical Computer Science
4.7
（17.1k件のレビュー）
Beginner · Specialization · 3+ 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

sas statistical business analyst

business statistics and analysis

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
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
IBM Data Analytics with Excel and R: IBM
Data Analysis and Visualization Foundations: IBM

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.‎