確率と統計

確率と統計のコースでは、データの有意性、最適化、推論、テストを理解するスキルや、データのパターンを分析して結果を予測、理解、改善するその他の方法について学習します。

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73 結果
並び替え:
R Programming

R Programming

Johns Hopkins University
コース
5つの星のうち 4.5 を評価18649のレビュー
Understanding Clinical Research: Behind the Statistics

Understanding Clinical Research: Behind the Statistics

University of Cape Town
コース
5つの星のうち 4.8 を評価1935のレビュー
Introduction to Probability and Data with R

Introduction to Probability and Data with R

Duke University
コース
5つの星のうち 4.7 を評価4188のレビュー
Understanding and Visualizing Data with Python

Understanding and Visualizing Data with Python

University of Michigan
コース
5つの星のうち 4.6 を評価1296のレビュー
Bayesian Statistics: From Concept to Data Analysis

Bayesian Statistics: From Concept to Data Analysis

University of California, Santa Cruz
コース
5つの星のうち 4.6 を評価2350のレビュー
Basic Statistics

Basic Statistics

University of Amsterdam
コース
5つの星のうち 4.6 を評価3134のレビュー
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
コース
5つの星のうち 4.7 を評価254のレビュー
Econometrics: Methods and Applications

Econometrics: Methods and Applications

Erasmus University Rotterdam
コース
5つの星のうち 4.6 を評価951のレビュー
Summary Statistics in Public Health

Summary Statistics in Public Health

Johns Hopkins University
コース
5つの星のうち 4.8 を評価789のレビュー
Probability and Statistics: To p or not to p?

Probability and Statistics: To p or not to p?

University of London
コース
5つの星のうち 4.6 を評価916のレビュー
Bayesian Statistics: Techniques and Models

Bayesian Statistics: Techniques and Models

University of California, Santa Cruz
コース
5つの星のうち 4.8 を評価321のレビュー
Introduction to Statistics & Data Analysis in Public Health

Introduction to Statistics & Data Analysis in Public Health

Imperial College London
コース
5つの星のうち 4.7 を評価618のレビュー
Improving your statistical inferences

Improving your statistical inferences

Eindhoven University of Technology
コース
5つの星のうち 4.9 を評価584のレビュー
Getting and Cleaning Data

Getting and Cleaning Data

Johns Hopkins University
コース
5つの星のうち 4.6 を評価7292のレビュー
Practical Time Series Analysis

Practical Time Series Analysis

The State University of New York
コース
5つの星のうち 4.6 を評価1050のレビュー
Inferential Statistics

Inferential Statistics

Duke University
コース
5つの星のうち 4.8 を評価1835のレビュー

    確率と統計に関するよくある質問

  • 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.