확률과 통계

확률과 통계 강좌는 데이터의 패턴 분석 및 결과 예측, 이해 및 개선을 위해, 활용, 추정, 시험 및 기타 방법을 포함하여 데이터가 의미를 지니고 있는지의 여부를 확인할 수 있는 기술을 가르칩니다.

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필터링 기준:
74 결과
정렬 기준:
Understanding Clinical Research: Behind the Statistics

Understanding Clinical Research: Behind the Statistics

University of Cape Town
강좌
5점 만점에 4.8점을 받았습니다. 1969 리뷰
R Programming

R Programming

Johns Hopkins University
강좌
5점 만점에 4.5점을 받았습니다. 18789 리뷰
Introduction to Probability and Data with R

Introduction to Probability and Data with R

Duke University
강좌
5점 만점에 4.7점을 받았습니다. 4228 리뷰
Understanding and Visualizing Data with Python

Understanding and Visualizing Data with Python

University of Michigan
강좌
5점 만점에 4.6점을 받았습니다. 1336 리뷰
Bayesian Statistics: From Concept to Data Analysis

Bayesian Statistics: From Concept to Data Analysis

University of California, Santa Cruz
강좌
5점 만점에 4.6점을 받았습니다. 2371 리뷰
A Crash Course in Causality:  Inferring Causal Effects from Observational Data

A Crash Course in Causality: Inferring Causal Effects from Observational Data

University of Pennsylvania
강좌
5점 만점에 4.7점을 받았습니다. 254 리뷰
Summary Statistics in Public Health

Summary Statistics in Public Health

Johns Hopkins University
강좌
5점 만점에 4.8점을 받았습니다. 813 리뷰
Basic Statistics

Basic Statistics

University of Amsterdam
강좌
5점 만점에 4.6점을 받았습니다. 3177 리뷰
Bayesian Statistics: Techniques and Models

Bayesian Statistics: Techniques and Models

University of California, Santa Cruz
강좌
5점 만점에 4.8점을 받았습니다. 331 리뷰
Econometrics: Methods and Applications

Econometrics: Methods and Applications

Erasmus University Rotterdam
강좌
5점 만점에 4.6점을 받았습니다. 957 리뷰
Practical Time Series Analysis

Practical Time Series Analysis

The State University of New York
강좌
5점 만점에 4.6점을 받았습니다. 1068 리뷰
Introduction to Statistics & Data Analysis in Public Health

Introduction to Statistics & Data Analysis in Public Health

Imperial College London
강좌
5점 만점에 4.7점을 받았습니다. 639 리뷰
Probability and Statistics: To p or not to p?

Probability and Statistics: To p or not to p?

University of London
강좌
5점 만점에 4.6점을 받았습니다. 936 리뷰
Experimental Design Basics

Experimental Design Basics

Arizona State University
강좌
5점 만점에 4.9점을 받았습니다. 17 리뷰
Improving your statistical inferences

Improving your statistical inferences

Eindhoven University of Technology
강좌
5점 만점에 4.9점을 받았습니다. 590 리뷰
Inferential Statistics

Inferential Statistics

Duke University
강좌
5점 만점에 4.8점을 받았습니다. 1849 리뷰

    확률과 통계에 대한 자주 묻는 질문

  • Probability is the study of the likelihood an event will happen, and statistics is the analysis of large datasets, usually with the goal of either usefully describing this data or inferring conclusions about a larger dataset based on a representative sample. These two branches of mathematics can be considered two sides of a coin: statistics help you to understand the past, and probability helps you use that knowledge to predict the future!

    Statistics and probability are essential tools for data science. These skills enable you to determine whether your data collection methods are sound, derive relevant insights from massive datasets, build analytic models that produce usable results, and much more. Important concepts and skills in the data science context include sampling distributions, statistical significance, hypothesis testing, and regression analysis.