Wahrscheinlichkeitsrechnung und Statistik

Kurse zu Wahrscheinlichkeitsrechnung und Statistik vermitteln Kenntnisse zum Verständnis, ob Daten aussagekräftig sind, einschließlich Optimierung, Inferenz, Tests und anderen Methoden zur Analyse von Mustern von Daten und deren Verwendung zur Prognose, zum Verständnis und zur Verbesserung von Ergebnissen.

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R Programming
Johns Hopkins University
R Programming
Kurs
Understanding Clinical Research: Behind the Statistics
University of Cape Town
Understanding Clinical Research: Behind the Statistics
Kurs
Introduction to Probability and Data with R
Duke University
Introduction to Probability and Data with R
Kurs
Bayesian Statistics: From Concept to Data Analysis
University of California, Santa Cruz
Bayesian Statistics: From Concept to Data Analysis
Kurs
Understanding and Visualizing Data with Python
University of Michigan
Understanding and Visualizing Data with Python
Kurs
Basic Statistics
University of Amsterdam
Basic Statistics
Kurs
Econometrics: Methods and Applications
Erasmus University Rotterdam
Econometrics: Methods and Applications
Kurs
Summary Statistics in Public Health
Johns Hopkins University
Summary Statistics in Public Health
Kurs
A Crash Course in Causality:  Inferring Causal Effects from Observational Data
University of Pennsylvania
A Crash Course in Causality: Inferring Causal Effects from Observational Data
Kurs
Probability and Statistics: To p or not to p?
University of London
Probability and Statistics: To p or not to p?
Kurs
Bayesian Statistics: Techniques and Models
University of California, Santa Cruz
Bayesian Statistics: Techniques and Models
Kurs
Introduction to Statistics & Data Analysis in Public Health
Imperial College London
Introduction to Statistics & Data Analysis in Public Health
Kurs
Improving your statistical inferences
Eindhoven University of Technology
Improving your statistical inferences
Kurs
Getting and Cleaning Data
Johns Hopkins University
Getting and Cleaning Data
Kurs
Practical Time Series Analysis
The State University of New York
Practical Time Series Analysis
Kurs
Inferential Statistics
Duke University
Inferential Statistics
Kurs
Statistical Inference
Johns Hopkins University
Statistical Inference
Kurs
Experimental Design Basics
Arizona State University
Experimental Design Basics
Kurs
Data Science Ethics
University of Michigan
Data Science Ethics
Kurs

    Häufig gestellte Fragen zum Thema Wahrscheinlichkeitsrechnung und Statistik

  • 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.