Coursera
  • 온라인 학위학사 및 석사 학위 살펴보기
  • MasterTrack™석사 학위를 따기 위한 학점 얻기
  • 대학교 수료증대학원 수준의 학습을 통해 경력을 쌓으세요.
경력 찾기기업용 Coursera대학교용
  • 검색
  • 상위 강좌
  • 로그인
  • 무료 회원 가입
    Coursera
    • 검색
    • Statistics

    필터링 기준

    "statistics"에 대한 2865개의 결과

    • 무료

      Placeholder
      Eindhoven University of Technology

      Improving your statistical inferences

      획득할 기술: Probability & Statistics, Statistical Tests, General Statistics, R Programming, Statistical Programming, Bayesian Statistics, Data Analysis, Probability Distribution, Statistical Analysis, Bayesian Network, Business Analysis, Experiment, Machine Learning

      4.9

      (750개의 검토)

      Intermediate · Course · 1-3 Months

    • Placeholder
      University of Colorado Boulder

      Expressway to Data Science: Essential Math

      획득할 기술: Mathematics, Algebra, Calculus, Linear Algebra, Mathematical Theory & Analysis, Differential Equations, Theoretical Computer Science, Probability & Statistics, Algorithms, Entrepreneurship, Graph Theory, Leadership and Management, Probability Distribution, Problem Solving, Research and Design, Computer Graphic Techniques, Computer Graphics, General Statistics, Accounting, Big Data, Computer Programming, Corporate Accouting, Data Analysis, Data Management, Data Mining, Data Model, Data Structures, Design and Product, Finance, Investment Management, Product Design, Programming Principles, Software Architecture, Software Engineering

      4.5

      (197개의 검토)

      Intermediate · Specialization · 3-6 Months

    • Placeholder
      IBM Skills Network

      IBM Data Engineering

      획득할 기술: 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

      (40.5k개의 검토)

      Beginner · Professional Certificate · 3-6 Months

    • Placeholder
      Johns Hopkins University

      Advanced Linear Models for Data Science 1: Least Squares

      획득할 기술: Probability & Statistics, Mathematics, General Statistics, Linear Algebra, Regression, Econometrics, Experiment, Machine Learning, Algebra, Artificial Neural Networks, Dimensionality Reduction, Machine Learning Algorithms, Probability Distribution, Statistical Machine Learning, Communication

      4.4

      (173개의 검토)

      Advanced · Course · 1-3 Months

    • 무료

      Placeholder
      University of Zurich

      An Intuitive Introduction to Probability

      획득할 기술: Probability & Statistics, Probability Distribution, General Statistics, Basic Descriptive Statistics, Bayesian Network, Bayesian Statistics, Data Analysis, Machine Learning

      4.8

      (1.5k개의 검토)

      Beginner · Course · 1-3 Months

    • Placeholder
      Google Cloud

      Advanced Machine Learning on Google Cloud

      획득할 기술: Machine Learning, Cloud Computing, Google Cloud Platform, Cloud Platforms, Probability & Statistics, Business Psychology, General Statistics, Entrepreneurship, Statistical Programming, Deep Learning, Apache, Cloud Applications, Data Management, Machine Learning Software, Natural Language Processing, Python Programming, Reinforcement Learning, Tensorflow, Artificial Neural Networks, Computer Vision, Performance Management, Strategy and Operations, Applied Machine Learning, Cloud API, Computational Thinking, Computer Architecture, Computer Programming, Data Analysis, Data Engineering, Distributed Computing Architecture, Hardware Design, Machine Learning Algorithms, Other Cloud Platforms and Tools, Theoretical Computer Science

      4.5

      (1.4k개의 검토)

      Advanced · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      Johns Hopkins University

      Data Science

      획득할 기술: R Programming, Data Analysis, Statistical Programming, Data Science, General Statistics, Statistical Analysis, Probability & Statistics, Statistical Tests, Machine Learning, Exploratory Data Analysis, Basic Descriptive Statistics, Machine Learning Software, Linear Algebra, Bayesian Statistics, Correlation And Dependence, Econometrics, Estimation, Regression, Data Visualization Software, Software Visualization, Statistical Visualization, Probability Distribution, Theoretical Computer Science, Data Visualization, Interactive Data Visualization, Natural Language Processing, Plot (Graphics), Big Data, Computer Programming, Computer Programming Tools, Data Structures, Experiment, Machine Learning Algorithms, Software Engineering Tools, Spreadsheet Software, Algorithms, Application Development, Applied Machine Learning, Business Analysis, Data Management, Extract, Transform, Load, Knitr

      4.5

      (49.8k개의 검토)

      Beginner · Specialization · 3-6 Months

    • Placeholder

      무료

      Placeholder
      Duke University

      Data Science Math Skills

      획득할 기술: Mathematics, Probability & Statistics, General Statistics, Algebra, Bayesian Statistics, Computational Logic, Data Visualization, Graph Theory, Mathematical Theory & Analysis, Plot (Graphics), Probability Distribution, Theoretical Computer Science

      4.5

      (10.9k개의 검토)

      Beginner · Course · 1-3 Months

    • Placeholder
      Placeholder
      University of Michigan

      Master of Applied Data Science

      획득할 기술: Machine Learning, Mathematics, Data Analysis, Probability & Statistics, Data Mining, Databases, Machine Learning Algorithms, Network Analysis, Statistical Programming, Communication, PostgreSQL, Data Management, Natural Language Processing, SQL, Computer Programming, Deep Learning, General Statistics, Research and Design, Storytelling, Python Programming, Computer Networking, Data Visualization, Graph Theory, Network Model, Theoretical Computer Science, Algebra, Algorithms, Applied Machine Learning, Artificial Neural Networks, Business Communication, Computer Programming Tools, Database Design, Experiment, Javascript, Operating Systems, Probability Distribution, Scientific Visualization, Security Engineering, Software Architecture, Software Engineering, Software Security, System Security, Visual Design, Web Development, Advertising, Apache, Big Data, Business Psychology, Computational Logic, Data Structures, Econometrics, Extract, Transform, Load, Finance, Human Computer Interaction, Marketing, Mathematical Theory & Analysis, Mergers & Acquisitions, Other Programming Languages, Programming Principles, Regression, Systems Design, Training, User Experience

      학위 취득

      Degree · 1-4 Years

    • Placeholder
      Placeholder
      Imperial College London

      Mathematics for Machine Learning

      획득할 기술: Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model

      4.6

      (13.4k개의 검토)

      Beginner · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      University of Colorado Boulder

      Data Science Foundations: Statistical Inference

      획득할 기술: General Statistics, Probability & Statistics, Probability Distribution, Statistical Tests, Calculus, Estimation, Mathematics, Business Analysis, Differential Equations, Econometrics, Spreadsheet Software

      4.4

      (129개의 검토)

      Intermediate · Specialization · 3-6 Months

    • Placeholder
      Placeholder
      Databricks

      Introduction to Computational Statistics for Data Scientists

      획득할 기술: General Statistics, Probability & Statistics, Bayesian Statistics, Probability Distribution, Python Programming

      3.4

      (47개의 검토)

      Beginner · Specialization · 1-3 Months

    statistics과(와) 관련된 검색

    statistics for data science
    statistics with r
    statistics with python
    statistics for data science with python
    statistics with sas
    statistics for international business
    statistics for genomic data science
    statistics for marketing
    1234…84

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

    • Improving your statistical inferences: Eindhoven University of Technology
    • Expressway to Data Science: Essential Math: University of Colorado Boulder
    • IBM Data Engineering: IBM Skills Network
    • Advanced Linear Models for Data Science 1: Least Squares: Johns Hopkins University
    • An Intuitive Introduction to Probability: University of Zurich
    • Advanced Machine Learning on Google Cloud: Google Cloud
    • Data Science: Johns Hopkins University
    • Data Science Math Skills: Duke University
    • Master of Applied Data Science: University of Michigan
    • Mathematics for Machine Learning: Imperial College London

    통계에 대한 자주 묻는 질문

    • Statistics is the science of organizing, analyzing, and interpreting large numerical datasets, with a variety of goals. Descriptive statistics such as mean, median, mode and standard deviation summarize the characteristics of a dataset; statistical inference seeks to determine the characteristics of a large population from a representative sample through statistical hypothesis testing; and statistical regression techniques establish the correlations between an dependent variable and one or more independent variables.

      A familiarity with statistics is critically important for describing and understanding our world. From stock market volatility to political polling to the three-point percentage of your favorite basketball player, statistics help to make the complexity of the world comprehensible - and tell us what to expect. The era of big data has made the use of statistics even more necessary, and data science software like Python and R programming have made data analysis techniques more powerful and more accessible than ever.‎

    • Just as statistics have become more important for making sense of our world, an ability to understand and use statistics has become increasingly essential for a variety of careers. Whether you are working in business, government, or academia, it is increasingly expected that assertions and decisions are backed up by data. Thus, you’ll need a familiarity with statistics whether you’re an operations manager preparing a presentation on process improvements for a CEO or a policy analyst writing a research paper on criminal justice reform for a legislator.

      If you have a passion for building Markov chain models or debating the relative merits of frequentist and Bayesian statistics, you can pursue a career as a full-time statistician. According to the Bureau of Labor Statistics, statisticians earned a median annual salary of $91,160 as of May 2019, and these jobs are expected to grow much faster than average due to the demand for keen statistical analysis across all fields. Statisticians typically have at least a bachelor’s degree in mathematics, computer science, or other quantitative fields, and many positions require a master’s degree in statistics.‎

    • Yes, with absolute certainty. Coursera offers individual courses as well as Specializations in statistics, as well as courses focused on related topics such as programming in Python and R as well as the applied use of business statistics. These courses and Specializations are offered by top-ranked universities such as the University of Michigan, Duke University, and Johns Hopkins University, ensuring that you won’t sacrifice educational rigor to learn online. You can also learn about statistics through Coursera’s hands-on Guided Projects, which allow you to build skills with step-by-step tutorials from experienced instructors to help you learn with confidence.‎

    • Before starting to learn statistics, you should already have basic math skills and be able to do simple calculations. You also could take math courses in algebra or calculus to prepare for learning statistics, but many people are able to successfully complete basic statistics courses without experience using advanced math. Other skills that may be useful include analytical, problem-solving, and inferential skills. Experience working with computer programming languages can be helpful if you want to take a course to learn how to use a specific language like Python to analyze data sets.‎

    • The kind of people best suited for roles in statistics enjoy working with data and sharing their findings with others. They tend to be analytical thinkers who look for trends and patterns in the data they collect and spend time asking and answering the questions the data prompts. They're able to work with a variety of people, including team members who help them collect and analyze data and the business executives and researchers relying on the information derived from the data. People who have roles in statistics may also have strong communication and presentation skills.‎

    • If you are an analytical thinker who likes collecting, analyzing, and interpreting data, learning statistics may be right for you. Learning statistics can be a logical choice if you like to make predictions or solve problems. You may be able to use the information you learn in a statistics course as preparation for additional studies in fields like mathematics, data science, or marketing. Learning statistics may be for you if you want to work in a field where you’ll use data regularly, such as business administration, marketing, public policy, finance, or insurance. Feeling comfortable organizing information, analyzing data, and viewing it from multiple perspectives can give you an edge over your competition.‎

    이 FAQ 콘텐츠는 정보 전달 목적만으로 사용할 수 있습니다. 학습자는 과정 및 기타 학점 정보가 개인적, 직업적 및 재정적 목표에 부합하는지 확인하기 위해 추가 조사를 수행하는 것이 좋습니다.
    살펴볼 만한 다른 주제
    Placeholder
    예술 & 인문학
    338개의 강좌
    Placeholder
    비즈니스
    1095개의 강좌
    Placeholder
    컴퓨터 공학
    668개의 강좌
    Placeholder
    데이터 과학
    425개의 강좌
    Placeholder
    정보 기술
    145개의 강좌
    Placeholder
    건강
    471개의 강좌
    Placeholder
    수학 및 논리
    70개의 강좌
    Placeholder
    자기개발
    137개의 강좌
    Placeholder
    물리 과학 및 공학
    413개의 강좌
    Placeholder
    사회 과학
    401개의 강좌
    Placeholder
    언어 학습
    150개의 강좌

    Coursera Footer

    경력을 시작하거나 쌓기

    • Google 데이터 분석가
    • Google 디지털 마케팅 및 전자 상거래 전문 자격증
    • Python을 통한 Google IT 자동화 전문 자격증
    • Google IT 지원
    • Google 프로젝트 관리
    • Google UX 디자인
    • Google 클라우드 자격증: 클라우드 아키텍트 취득 준비
    • IBM 사이버 보안 분석가
    • IBM 데이터 분석가
    • IBM 데이터 엔지니어링
    • IBM 데이터 과학
    • IBM 풀스택 클라우드 개발자
    • IBM 기계 학습
    • Intuit 부기
    • Meta 프런트 엔드 개발자
    • DeepLearning.AI TensorFlow 개발자 전문 자격증
    • SAS 프로그래머 전문 자격증
    • 경력 시작
    • 수료증 취득 준비
    • 경력 쌓기
    • Python 구문 오류를 찾아내는 방법
    • Python 예외를 발견하는 방법
    • 모든 프로그래밍 자습서 보기

    인기 강좌 및 수료증

    • 무료 강좌
    • 인공 지능 강좌
    • 블록체인 강좌
    • 컴퓨터 공학 강좌
    • Cursos Gratis
    • 사이버 보안 강좌
    • 데이터 분석 강좌
    • 데이터 과학 강좌
    • 영어 말하기 강좌
    • 풀스택 웹 개발 강좌
    • Google 강좌
    • 인사 강좌
    • IT 강좌
    • 영어 학습 강좌
    • Microsoft Excel 강좌
    • 제품 관리 강좌
    • 프로젝트 관리 강좌
    • Python 강좌
    • SQL 강좌
    • 애자일 수료증
    • CAPM 수료증
    • CompTIA A+ 수료증
    • 데이터 분석 수료증
    • 스크럼 마스터 수료증
    • 모든 강좌 보기

    인기 컬렉션 및 문서

    • 하루만에 완료할 수 있는 무료 온라인 강좌
    • 인기 무료 강좌
    • 비즈니스 직무
    • 사이버 보안 직무
    • 신입 IT 직무
    • 데이터 분석가 면접 질문
    • 데이터 분석 프로젝트
    • 데이터 분석가로 취업하는 방법
    • 프로젝트 관리자로 취업하는 방법
    • IT 기술
    • 프로젝트 관리자 면접 질문
    • Python 프로그래밍 기술
    • 면접에서 물어보는 강점과 약점
    • 데이터 분석가가 하는 일
    • 소프트웨어 엔지니어가 하는 일
    • 데이터 엔지니어란
    • 데이터 과학자란
    • 제품 디자이너란
    • 스크럼 마스터란
    • UX 리서처란
    • PMP 수료증을 취득하는 방법
    • PMI 수료증
    • 인기 있는 사이버 보안 자격증
    • 인기 있는 SQL 자격증
    • 모든 Coursera 문서 읽기

    온라인으로 학위 또는 자격증 취득

    • Google 전문 자격증
    • 전문 자격증
    • 모든 자격증 보기
    • 학사 학위
    • 석사 학위
    • 컴퓨터 공학 학위
    • 데이터 과학 학위
    • MBA 및 경영학 학위
    • 데이터 분석 학위
    • 공중 보건 학위
    • 사회 과학 학위
    • 관리 학위
    • BA 및 BS 학위 비교
    • 학사 학위란 무엇인가요?
    • 개발해야 할 11가지 좋은 학습 습관
    • 추천서를 작성하는 방법
    • 비즈니스 학위 취득 후 취업 가능한 10개의 수요가 높은 직무
    • 컴퓨터 공학 석사 학위는 취득할 만한 가치가 있나요?
    • 모든 학위 프로그램 보기
    • Coursera 인도
    • Coursera 영국
    • Coursera 멕시코

    Coursera

    • 소개
    • 제공 내용
    • 리더십
    • 직업
    • 카탈로그
    • Coursera Plus
    • 전문 자격증
    • MasterTrack® 자격증
    • 학위
    • 기업용 Coursera
    • 정부용
    • 캠퍼스용
    • 파트너가 되기
    • 코로나바이러스감염증-19 대응

    커뮤니티

    • 학습자
    • 파트너
    • 베타 테스터
    • 번역가
    • 블로그
    • 기술 블로그
    • 지도 센터

    기타

    • 보도 자료
    • 투자자
    • 조건
    • 개인정보 보호
    • 도움말
    • 접근성
    • 문의하기
    • 문서
    • 디렉토리
    • 계열사
    • 현대 노예 선언문
    어디에서나 학습
    Placeholder
    Placeholder
    Placeholder
    © 2023 Coursera Inc. All rights reserved.
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder
    • Placeholder