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Non-metric / qualitative average examples: • faculty id • grammatical gender • religion • ethnicity • earth science location • home of birth • tax file Numbers • telephone Numbers • post codes this sort of data should ne'er be used to calculate the average, for example.
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Where can I learn statistics for data science?
Through online courses, you can learn the fundamentals of statistics for data science, including the theories and techniques statisticians use in their work. Some courses explore fundamental concepts like Bayes’ Theorem and probability theory. Others present methods for calculating and evaluating data sets.
What can I learn about statistics at Harvard?
Learn to use R programming to apply linear models to analyze data in life sciences. Increase your quantitative reasoning skills through a deeper understanding of probability and statistics. Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008.
Do you need to be trained in statistics?
As much as we enjoy this superconductivity of data, it invites abuse as well. Data professionals need to be trained to use statistical methods not only to interpret numbers but to uncover such abuse and protect us from being misled. Not many data scientists are formally trained in statistics.
What do you need to know about statistics?
Getting Started— Understanding types of data (rectangular and non-rectangular), estimate of location, estimate of variability, data distributions, binary and categorical data, correlation, relationship between different types of variables.
Last Update: Oct 2021
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