Data Train Starter Track: Statistical thinking
Data science approaches are based on statistical/mathematical methods as well as computer science competences. In this context, it is crucial to understand the basic principles of statistical methods. This will help to adequately apply statistical methods and to produce reliable statistical results.
This course provides an introduction into statistical basics and concepts relevant for data science applications. After a brief presentation of the categories of statistics (descriptive, predictive, confirmatory) and their general ideas, selected basic methods will be explained and illustrated by practical examples: concept of probability, parameter estimation, confidence intervals and testing of hypotheses.
A basic understanding of the major statistical principles.
Speaker: Iris Pigeot, Scientific director of the Leibniz Institute for Prevention Research and Epidemiology – BIPS and head of the Department of Biometry and Data Management, Professor for Statistics with a Focus on Biometry and Methods in Epidemiology at the University of Bremen
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Universität Bremen Data Train
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