Analytics is the system of applying certain methods and techniques to understand the performance of data, generally in an organization or industry. The application of analytics can be used to uncover and interpret relevant patterns of data, which can be later used to make better decisions. Analytics relies on good data as part of its usefulness, as well as the aspects of statistics, computer programming, and operations research to quantify performance. Analytics exists in business when companies use statistics and software processes in assessing how a particular product, service, or project measures up in scope. Businesses perform analytics to pinpoint strengths, identify challenges or weaknesses that may exist, and pull out key data points to help make decisions regarding future growth for the organization.

Learning analytics is a key skill that helps you solve problems to existing challenges. When you use analytics tools to research data patterns, you use methods of analyzing performance characteristics that are essential to a business organization. This is important in today's world of big data. Understanding analytics can make you a valuable asset when assessing areas rich with recorded information, performance indicators, and target goals.

What are typical careers that use analytics?

Analytics is found in a wide range of careers, including business analyst, budget analyst, financial controller, data scientist, and operations researcher. Individuals who move into these and other careers in data analytics often work with raw data and algorithms through mechanical processes to make assumptions and theories. Essentially, with knowledge of analytics, you can move into a job that requires a familiarity with numeric relationships, data structures, and trends and metrics relevant to an organization.

How can taking online courses on Coursera help me learn analytics?

When you take online courses in analytics, you can gain a new understanding of key concepts like data analysis and visualization, mathematical optimization, regression analysis, predictive modeling, and other areas focused on business analytics. Taking online courses about analytics can help you achieve basic data literacy to help you make more informed decisions in any area you work in, like finance, operations, or marketing. As you branch out, you may advance your analytics understanding to include how to extract and manipulate data points, how to use different statistical analysis methods to break down the data, and how to eventually interpret and present your findings.

검색
Analytics

필터링 기준

"analytics"에 대한 4149개의 결과

Google
Google Data Analytics
획득할 기술: 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
(102.2k개의 검토)
Beginner · Professional Certificate · 3-6 Months
University of Pennsylvania
Business Analytics
획득할 기술: Business Analysis, Data Analysis, Probability & Statistics, Statistical Analysis, General Statistics, Research and Design, Forecasting, Strategy and Operations, Correlation And Dependence, Financial Analysis, Accounting, Human Resources, Marketing, Operational Analysis, Operations Management, Operations Research, Probability Distribution, Spreadsheet Software, Supply Chain and Logistics, Customer Analysis, Financial Accounting, Market Analysis, Market Research, Basic Descriptive Statistics, Exploratory Data Analysis, Finance, People Management, Performance Management, Regulations and Compliance, Statistical Tests, Talent Management, Collaboration, Communication, Critical Thinking, Data Management, Data Mining, Data Model, Data Visualization, Generally Accepted Accounting Principles (GAAP), HR Tech, Leadership Development, Leadership and Management, MarTech, Marketing Management, Media Strategy & Planning, Microsoft Excel, Organizational Development, Plot (Graphics), Process Analysis, Recruitment, Statistical Programming, Statistical Visualization, Applied Mathematics, Big Data, Business Psychology, Computational Logic, Computer Programming, Computer Programming Tools, Data Analysis Software, Data Structures, Decision Making, Entrepreneurship, Estimation, Mathematics, Network Analysis, People Analysis, People Development, Regression, Sales, Strategy, Theoretical Computer Science
4.6
(17.2k개의 검토)
Beginner · Specialization · 3-6 Months
IBM Skills Network
IBM Data Analyst
획득할 기술: 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
(61k개의 검토)
Beginner · Professional Certificate · 3-6 Months
University of Illinois at Urbana-Champaign
Business Analytics
획득할 기술: Data Analysis, Business Analysis, Machine Learning, Statistical Programming, Data Visualization, R Programming, Data Management, Accounting, Business Communication, Communication, Exploratory Data Analysis, Algorithms, Data Analysis Software, Data Visualization Software, Machine Learning Algorithms, Probability & Statistics, Theoretical Computer Science, Big Data, Data Mining, Natural Language Processing, Statistical Analysis, Storytelling, Audit, Basic Descriptive Statistics, BlockChain, Business Psychology, Customer Analysis, Extract, Transform, Load, Finance, Graph Theory, Management Accounting, Marketing Psychology, Mathematics, Network Analysis, Regression, Software Engineering, Software Testing, Spreadsheet Software, Statistical Visualization, Business Intelligence, Data Structures, Design and Product, Entrepreneurship, Human Computer Interaction, Market Research, Research and Design, User Research
4.6
(802개의 검토)
Beginner · Specialization · 3-6 Months
IBM Skills Network
IBM Data Analytics with Excel and R
획득할 기술: 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
(14.9k개의 검토)
Beginner · Professional Certificate · 3-6 Months
University of Pennsylvania
Customer Analytics
획득할 기술: Business Analysis, General Statistics, Marketing, Customer Analysis, Data Analysis, Market Analysis, Market Research, Probability & Statistics, Research and Design, Statistical Analysis, Exploratory Data Analysis, Basic Descriptive Statistics, Communication, Correlation And Dependence, Critical Thinking, Data Management, Data Mining, Data Model, Financial Analysis, Forecasting, MarTech, Marketing Management, Media Strategy & Planning, Statistical Programming, Big Data, Estimation, Probability Distribution, Regression
4.6
(11.3k개의 검토)
Mixed · Course · 1-3 Months
University of Michigan
Applied Data Science with Python
획득할 기술: Python Programming, Machine Learning, Data Analysis, Data Mining, Data Science, Machine Learning Algorithms, Computer Science, Statistical Programming, Applied Machine Learning, Graph Theory, Mathematics, General Statistics, Basic Descriptive Statistics, Statistical Machine Learning, Data Structures, Natural Language Processing, Regression, Dimensionality Reduction, Exploratory Data Analysis, Feature Engineering, Statistical Analysis, Statistical Tests, Correlation And Dependence, Estimation, Linear Algebra, Computer Programming, Data Architecture, Probability & Statistics, Statistical Visualization, Algorithms, Artificial Neural Networks, Computational Logic, Computer Graphics, Data Visualization, Econometrics, Machine Learning Software, Mathematical Theory & Analysis, Network Analysis, Plot (Graphics), Programming Principles, Theoretical Computer Science
4.5
(33k개의 검토)
Intermediate · Specialization · 3-6 Months
Meta
Coding Interview Preparation
획득할 기술: Algorithms, Research and Design, Theoretical Computer Science, Big Data, Calculus, Computer Programming, Data Management, Entrepreneurship, Market Research, Mathematics, Operations Research, Programming Principles, Strategy and Operations, Algebra, Communication, Computational Thinking, Computer Science, Python Programming, Statistical Programming
4.8
(22개의 검토)
Intermediate · Course · 1-4 Weeks
LearnQuest
Key Technologies in Data Analytics
획득할 기술: Data Management, Data Analysis, Big Data, Cloud Computing, Computer Networking, Data Warehousing, Databases, Human Resources, Operating Systems, Software As A Service, Systems Design, Theoretical Computer Science, Data Visualization, Extract, Transform, Load, Business Analysis, Design and Product, Entrepreneurship, Leadership and Management, Marketing, Process Analysis, Product Marketing, Research and Design, Sales, Strategy, Strategy and Operations
4.0
(75개의 검토)
Beginner · Specialization · 3-6 Months

analytics과(와) 관련된 검색

analytics for decision making

analytics en las organizaciones

analytics as a service for data sharing partners

data analytics

business analytics

google analytics

marketing analytics

people analytics

요약하자면, 여기에 가장 인기 있는 analytics 강좌 10개가 있습니다.

Google Data Analytics: Google
Business Analytics: University of Pennsylvania
IBM Data Analyst: IBM Skills Network
Business Analytics: University of Illinois at Urbana-Champaign
Create Charts and Dashboards Using Microsoft Excel: Coursera Project Network
IBM Data Analytics with Excel and R: IBM Skills Network
Getting Started in Google Analytics: Coursera Project Network
Customer Analytics: University of Pennsylvania
Applied Data Science with Python: University of Michigan
Coding Interview Preparation: Meta

Business Essentials에서 학습할 수 있는 스킬

분석에 대한 자주 묻는 질문

Analytics is the system of applying certain methods and techniques to understand the performance of data, generally in an organization or industry. The application of analytics can be used to uncover and interpret relevant patterns of data, which can be later used to make better decisions. Analytics relies on good data as part of its usefulness, as well as the aspects of statistics, computer programming, and operations research to quantify performance.
Analytics exists in business when companies use statistics and software processes in assessing how a particular product, service, or project measures up in scope. Businesses perform analytics to pinpoint strengths, identify challenges or weaknesses that may exist, and pull out key data points to help make decisions regarding future growth for the organization.‎
Learning analytics is a key skill that helps you solve problems to existing challenges. When you use analytics tools to research data patterns, you use methods of analyzing performance characteristics that are essential to a business organization. This is important in today's world of big data. Understanding analytics can make you a valuable asset when assessing areas rich with recorded information, performance indicators, and target goals.‎
Analytics is found in a wide range of careers, including business analyst, budget analyst, financial controller, data scientist, and operations researcher. Individuals who move into these and other careers in data analytics often work with raw data and algorithms through mechanical processes to make assumptions and theories. Essentially, with knowledge of analytics, you can move into a job that requires a familiarity with numeric relationships, data structures, and trends and metrics relevant to an organization.‎
When you take online courses in analytics, you can gain a new understanding of key concepts like data analysis and visualization, mathematical optimization, regression analysis, predictive modeling, and other areas focused on business analytics.
Taking online courses about analytics can help you achieve basic data literacy to help you make more informed decisions in any area you work in, like finance, operations, or marketing. As you branch out, you may advance your analytics understanding to include how to extract and manipulate data points, how to use different statistical analysis methods to break down the data, and how to eventually interpret and present your findings.‎