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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R Programming
Johns Hopkins University
R Programming
COURSE
Basic Statistics
University of Amsterdam
Basic Statistics
COURSE
Probability and Statistics: To p or not to p?
University of London
Probability and Statistics: To p or not to p?
COURSE
Introduction to Probability and Data
Duke University
Introduction to Probability and Data
COURSE
Understanding Clinical Research: Behind the Statistics
University of Cape Town
Understanding Clinical Research: Behind the Statistics
COURSE
Understanding and Visualizing Data with Python
University of Michigan
Understanding and Visualizing Data with Python
COURSE
Getting and Cleaning Data
Johns Hopkins University
Getting and Cleaning Data
COURSE
Bayesian Statistics: From Concept to Data Analysis
University of California, Santa Cruz
Bayesian Statistics: From Concept to Data Analysis
COURSE
Machine Learning: Regression
University of Washington
Machine Learning: Regression
COURSE
Econometrics: Methods and Applications
Erasmus University Rotterdam
Econometrics: Methods and Applications
COURSE
Statistical Inference
Johns Hopkins University
Statistical Inference
COURSE
Exploratory Data Analysis
Johns Hopkins University
Exploratory Data Analysis
COURSE
Practical Time Series Analysis
The State University of New York
Practical Time Series Analysis
COURSE
Inferential Statistics
Duke University
Inferential Statistics
COURSE
Summary Statistics in Public Health
Johns Hopkins University
Summary Statistics in Public Health
COURSE
Improving your statistical inferences
Eindhoven University of Technology
Improving your statistical inferences
COURSE
Linear Regression and Modeling
Duke University
Linear Regression and Modeling
COURSE
Data Science Ethics
University of Michigan
Data Science Ethics
COURSE
Regression Models
Johns Hopkins University
Regression Models
COURSE

    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.