概率论与数理统计

概率论与数理统计课程介绍理解数据意义的技能,具体内容包括优化、推断、测验、数据模式分析方法,以及使用分析方法来预测、理解和改进结果。

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Learn SQL Basics for Data Science

Learn SQL Basics for Data Science

University of California, Davis
专项课程
评分为 4.1(满分 5 星)。
Statistics with R

Statistics with R

Duke University
专项课程
评分为 4.5(满分 5 星)。
社会科学的方法和统计

社会科学的方法和统计

University of Amsterdam
专项课程
评分为 4.5(满分 5 星)。
Business Statistics and Analysis

Business Statistics and Analysis

Rice University
专项课程
评分为 4.8(满分 5 星)。
Statistics with Python

Statistics with Python

University of Michigan
专项课程
评分为 4.5(满分 5 星)。
Biostatistics in Public Health

Biostatistics in Public Health

Johns Hopkins University
专项课程
评分为 4.7(满分 5 星)。
Statistical Analysis with R for Public Health

Statistical Analysis with R for Public Health

Imperial College London
专项课程
评分为 4.7(满分 5 星)。
Design of Experiments

Design of Experiments

Arizona State University
专项课程
评分为 4.7(满分 5 星)。
Advanced Statistics for Data Science

Advanced Statistics for Data Science

Johns Hopkins University
专项课程
评分为 4.5(满分 5 星)。
TensorFlow 2 for Deep Learning

TensorFlow 2 for Deep Learning

Imperial College London
专项课程
评分为 4.9(满分 5 星)。
Анализ данных

Анализ данных

Novosibirsk State University
专项课程
评分为 4.6(满分 5 星)。
Просто о статистике (с использованием R)

Просто о статистике (с использованием R)

Saint Petersburg State University
专项课程
评分为 4.5(满分 5 星)。

    关于 概率论与数理统计 的常见问题

  • 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.