Probability and Statistics

Probability and statistics courses teach skills in understanding whether data is meaningful, including optimization, inference, testing, and other methods for analyzing patterns in data and using them to predict, understand, and improve results.

...
Learn SQL Basics for Data Science
University of California, Davis
Learn SQL Basics for Data Science
SPECIALIZATION
Methods and Statistics in Social Sciences
University of Amsterdam
Methods and Statistics in Social Sciences
SPECIALIZATION
Business Statistics and Analysis
Rice University
Business Statistics and Analysis
SPECIALIZATION
Statistics with R
Duke University
Statistics with R
SPECIALIZATION
Statistics with Python
University of Michigan
Statistics with Python
SPECIALIZATION
Biostatistics in Public Health
Johns Hopkins University
Biostatistics in Public Health
SPECIALIZATION
Statistical Analysis with R for Public Health
Imperial College London
Statistical Analysis with R for Public Health
SPECIALIZATION
Анализ данных
Novosibirsk State University
Анализ данных
SPECIALIZATION
Просто о статистике (с использованием R)
Saint Petersburg State University
Просто о статистике (с использованием R)
SPECIALIZATION

    Frequently Asked Questions about Probability and Statistics

  • 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.