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    • Statistics
    Related topics:확률 분포R 통계데이터 과학을 위한 통계베이지안 통계추론적 통계데이터

    필터링 기준

    "statistics"에 대한 2391개의 결과

    • University of Colorado Boulder

      University of Colorado Boulder

      Data Science Foundations: Statistical Inference

      획득할 기술: Business Analysis, Calculus, Estimation, General Statistics, Mathematics, Probability & Statistics, Probability Distribution, Spreadsheet Software

      4.2

      (69개의 검토)

      Intermediate · Specialization · 3+ Months

    • 무료

      Eindhoven University of Technology

      Eindhoven University of Technology

      Improving your statistical inferences

      획득할 기술: Statistical Inference, Machine Learning, Experiment, Probability & Statistics, Bayesian, Bayesian Network, Bayesian Statistics, Inference, Statistical Tests, General Statistics, Interpretation

      4.9

      (715개의 검토)

      Intermediate · Course · 1-3 Months

    • University of Pennsylvania

      University of Pennsylvania

      Business Analytics

      획득할 기술: Accounting, Analysis, Analytics, Big Data, Business Analysis, Collaboration, Communication, Computational Logic, Computer Programming, Computer Programming Tools, Customer Analysis, Data Analysis, Data Analysis Software, Data Management, Data Structures, Decision Making, Decision Tree, Entrepreneurship, Financial Analysis, Forecasting, General Statistics, Human Resources, Leadership and Management, Marketing, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Operational Analysis, People Analysis, Performance Management, Probability & Statistics, Research and Design, Spreadsheet Software, Statistical Analysis, Strategy and Operations, Supply Chain and Logistics, Talent Management, Theoretical Computer Science

      4.6

      (16.4k개의 검토)

      Beginner · Specialization · 3+ Months

    • 무료

      University of Cape Town

      University of Cape Town

      Understanding Clinical Research: Behind the Statistics

      획득할 기술: Basic Descriptive Statistics, Data Analysis, Probability & Statistics, General Statistics, Analysis

      4.8

      (2.8k개의 검토)

      Mixed · Course · 1-3 Months

    • Imperial College London

      Imperial College London

      Mathematics for Machine Learning

      획득할 기술: Algebra, Algorithms, Analysis, Basic Descriptive Statistics, Calculus, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Linear Algebra, Linearity, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Python Programming, Software Engineering, Software Testing, Statistical Programming, Theoretical Computer Science

      4.6

      (12.6k개의 검토)

      Beginner · Specialization · 3+ Months

    • Duke University

      Duke University

      Inferential Statistics

      획득할 기술: Statistical Inference, Hypothesis, Statistical Hypothesis Testing, General Statistics, Experiment, R Programming, Probability & Statistics, Analysis, Statistical Programming, Inference

      4.8

      (2.4k개의 검토)

      Beginner · Course · 1-3 Months

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      University of Illinois at Urbana-Champaign

      University of Illinois at Urbana-Champaign

      Master of Computer Science

      학위 취득

      Degree

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      무료

      University of London

      University of London

      Probability and Statistics: To p or not to p?

      획득할 기술: Experiment, Risk Management, Theoretical Computer Science, General Statistics, Machine Learning, Hypothesis, Leadership and Management, Data Management, Statistical Hypothesis Testing, Decision Making, Probability & Statistics, Markov Model, Entrepreneurship, Data Structures, Hypothesis Testing, Probability, Finance, Probability Distribution

      4.6

      (1.3k개의 검토)

      Beginner · Course · 1-3 Months

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      Rice University

      Rice University

      Fundamentals of Computing

      획득할 기술: Algorithms, Applied Mathematics, Communication, Computer Programming, Data Management, Data Structures, Databases, General Statistics, Graph Theory, Mathematics, Operations Research, Patient, Probability & Statistics, Python Programming, SQL, Software Engineering, Statistical Programming, Syntax, Theoretical Computer Science

      4.8

      (3.8k개의 검토)

      Beginner · Specialization · 3+ Months

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      Johns Hopkins University

      Johns Hopkins University

      Data Science: Foundations using R

      획득할 기술: Analysis, Application Development, Business Analysis, Computer Programming, Data Analysis, Data Management, Data Visualization, Exploratory Data Analysis, Extract, Transform, Load, Knitr, Probability & Statistics, R Programming, Rstudio, Software Engineering Tools, Statistical Programming

      4.6

      (46.4k개의 검토)

      Beginner · Specialization · 3+ Months

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      SAS

      SAS

      SAS Statistical Business Analyst

      획득할 기술: Advertising, Algebra, Analysis, Business Analysis, Communication, Computer Programming, Data Analysis, Econometrics, Experiment, General Statistics, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Regression, Regression Analysis, SAS (Software), Statistical Analysis, Statistical Programming, Supply Chain, Supply Chain Systems, Supply Chain and Logistics

      4.6

      (86개의 검토)

      Intermediate · Professional Certificate · 3+ Months

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      Imperial College London

      Imperial College London

      Introduction to Statistics & Data Analysis in Public Health

      획득할 기술: Statistical Programming, Hypothesis, Statistical Hypothesis Testing, Business Analysis, Probability & Statistics, Data Analysis, Basic Descriptive Statistics, General Statistics, Probability Distribution, Statistical Analysis, Statistical Tests, Hypothesis Testing, Analysis

      4.7

      (1.2k개의 검토)

      Beginner · Course · 1-4 Weeks

    statistics과(와) 관련된 검색

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

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

    • Data Science Foundations: Statistical Inference: University of Colorado Boulder
    • Improving your statistical inferences: Eindhoven University of Technology
    • Business Analytics: University of Pennsylvania
    • Understanding Clinical Research: Behind the Statistics: University of Cape Town
    • Mathematics for Machine Learning: Imperial College London
    • Inferential Statistics: Duke University
    • Master of Computer Science: University of Illinois at Urbana-Champaign
    • Probability and Statistics: To p or not to p?: University of London
    • Fundamentals of Computing: Rice University
    • Data Science: Foundations using R: Johns Hopkins University

    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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