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    • Statistics

    필터링 기준

    "statistics"에 대한 2334개의 결과

    • 무료

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      Stanford University

      Introduction to Statistics

      획득할 기술: Econometrics, Basic Descriptive Statistics, Statistical Tests, Experiment, Bayesian Statistics, Probability Distribution, Plot (Graphics), Regression, Probability & Statistics, Data Analysis, Probability, Machine Learning, Data Visualization, Statistical Analysis, General Statistics, Correlation And Dependence, Statistical Hypothesis Testing, Statistical Inference, Markov Model, Inference, Hypothesis Testing

      4.5

      (1.3k개의 검토)

      Beginner · Course · 1-3 Months

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      University of Michigan

      Statistics with Python

      획득할 기술: Analysis, Basic Descriptive Statistics, Bayesian Statistics, Business Analysis, Computer Programming, Correlation And Dependence, Data Analysis, Data Visualization, Econometrics, Experiment, General Statistics, Inference, Machine Learning, Machine Learning Algorithms, Plot (Graphics), Probability & Statistics, Programming Principles, Python Programming, Regression, Statistical Analysis, Statistical Inference, Statistical Programming, Statistical Tests, Statistical Visualization

      4.6

      (2.8k개의 검토)

      Beginner · Specialization · 1-3 Months

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      Johns Hopkins University

      Advanced Statistics for Data Science

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

      4.4

      (660개의 검토)

      Advanced · Specialization · 3-6 Months

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      Rice University

      Business Statistics and Analysis

      획득할 기술: Basic Descriptive Statistics, Business Analysis, Computer Programming, Correlation And Dependence, Data Analysis, Data Analysis Software, Data Mining, Data Visualization, Econometrics, Experiment, General Statistics, Mathematics, Microsoft Excel, Plot (Graphics), Probability & Statistics, Probability Distribution, Regression, Regression Analysis, Spreadsheet Software, Statistical Analysis, Statistical Tests, Statistical Visualization

      4.7

      (11.2k개의 검토)

      Beginner · Specialization · 3-6 Months

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      University of Amsterdam

      Basic Statistics

      획득할 기술: Probability & Statistics, Statistical Hypothesis Testing, Probability, Statistical Tests, Probability Distribution, Analysis, Experiment, Hypothesis, Regression, Data Analysis, Correlation And Dependence, General Statistics, Confidence, Hypothesis Testing, Basic Descriptive Statistics

      4.6

      (4.2k개의 검토)

      Beginner · Course · 1-3 Months

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      Duke University

      Data Analysis with R

      획득할 기술: Bayesian Statistics, Business Analysis, Data Analysis, Data Mining, Data Visualization, Econometrics, Experiment, Exploratory Data Analysis, General Statistics, Machine Learning, Machine Learning Algorithms, Mathematics, Plot (Graphics), Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Programming, Statistical Tests

      4.7

      (6.6k개의 검토)

      Beginner · Specialization · 3-6 Months

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      Johns Hopkins University

      Data Science: Statistics and Machine Learning

      획득할 기술: Algorithms, Applied Machine Learning, Business Analysis, Data Analysis, Data Visualization, Econometrics, General Statistics, Interactive Data Visualization, Interactivity, Machine Learning, Machine Learning Algorithms, Natural Language, Natural Language Processing, Plot (Graphics), Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Programming, Statistical Tests, Theoretical Computer Science

      4.4

      (7k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      Johns Hopkins University

      Biostatistics in Public Health

      획득할 기술: Analysis, Basic Descriptive Statistics, Biostatistics, Business Analysis, Data Analysis, Data Visualization, Econometrics, Experiment, Feature Engineering, General Statistics, Machine Learning, Mobile Development, Plot (Graphics), Probability & Statistics, Probability Distribution, Regression, Risk, Statistical Analysis, Statistical Tests, Statistical Visualization, iOS Development

      4.8

      (1.9k개의 검토)

      Beginner · Specialization · 3-6 Months

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      University of Amsterdam

      Methods and Statistics in Social Sciences

      획득할 기술: Basic Descriptive Statistics, Business Analysis, Correlation And Dependence, Data Analysis, Experiment, General Statistics, Interview, Probability, Probability & Statistics, Probability Distribution, Qualitative Research, Regression, Research Methods, Research and Design, Statistical Analysis, Statistical Tests

      4.6

      (6.9k개의 검토)

      Beginner · Specialization · 3-6 Months

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      University of California, Santa Cruz

      Bayesian Statistics

      획득할 기술: Analysis, Bayesian, Bayesian Statistics, Business Analysis, Data Analysis, Econometrics, Estimation, Forecasting, General Statistics, Graph Theory, Inference, Machine Learning, Markov Model, Mathematics, Probability, Probability & Statistics, Probability Distribution, R Programming, Regression, Statistical Analysis, Statistical Machine Learning, Statistical Programming

      4.6

      (3.2k개의 검토)

      Intermediate · Specialization · 3-6 Months

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      Google

      Google Data Analytics

      획득할 기술: Algorithms, Application Development, Big Data, Budget Management, Business Analysis, Business Communication, Change Management, Cloud Computing, Communication, Computational Logic, Computer Architecture, 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, Distributed Computing Architecture, Entrepreneurship, Extract, Transform, Load, Feature Engineering, Finance, Financial Analysis, Full-Stack Web Development, General Statistics, Interactive Data Visualization, Leadership and Management, Machine Learning, Mathematical Theory & Analysis, Mathematics, Network Security, Other Programming Languages, Plot (Graphics), Privacy, 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, Tableau Software, Theoretical Computer Science, Visual Design, Web Development

      4.8

      (71.3k개의 검토)

      Beginner · Professional Certificate · 3-6 Months

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      University of Colorado Boulder

      Expressway to Data Science: Essential Math

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

      4.4

      (76개의 검토)

      Intermediate · Specialization · 3-6 Months

    statistics과(와) 관련된 검색

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

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

    • Introduction to Statistics: Stanford University
    • Statistics with Python: University of Michigan
    • Advanced Statistics for Data Science: Johns Hopkins University
    • Business Statistics and Analysis: Rice University
    • Basic Statistics: University of Amsterdam
    • Data Analysis with R: Duke University
    • Data Science: Statistics and Machine Learning: Johns Hopkins University
    • Biostatistics in Public Health: Johns Hopkins University
    • Methods and Statistics in Social Sciences: University of Amsterdam
    • Bayesian Statistics: University of California, Santa Cruz

    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.
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