Introduction to Statistics

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Description

Making Decisions Based on Data

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and…

Class Summary

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.

What Should I Know?

This course does not require any previous knowledge of statistics. Basic familiarity with algebra such as knowing how to compute the mean, median and mode of a set of numbers will be helpful.

What Will I Learn?

This course will cover visualization, probability, regression and ot…

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Didn't find what you were looking for? See also: Visualization, Statistics, Algebra, Mathematics, and Global Environment.

Making Decisions Based on Data

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and…

Class Summary

Statistics is about extracting meaning from data. In this class, we will introduce techniques for visualizing relationships in data and systematic techniques for understanding the relationships using mathematics.

What Should I Know?

This course does not require any previous knowledge of statistics. Basic familiarity with algebra such as knowing how to compute the mean, median and mode of a set of numbers will be helpful.

What Will I Learn?

This course will cover visualization, probability, regression and other topics that will help you learn the basic methods of understanding data with statistics.

Syllabus

Part 1: Visualizing relationships in data

Seeing relationships in data and predicting based on them; Simpson's paradox

Part 2: Probability

Probability; Bayes Rule; Correlation vs. Causation

Part 3: Estimation

Maximum Likelihood Estimation; Mean, Mean, Mode; Standard Deviation, Variance

Part 4: Outliers and Normal Distribution

Outliers, Quartiles; Binomial Distribution; Central Limit Theorem; Manipulating Normal Distribution

Part 5: Inference

Confidence intervals; Hypothesis Testing

Part 6: Regression

Linear regression; correlation

Part 7: Final Exam

Course Instructors

Sebastian Thrun Instructor

Sebastian Thrun is a Research Professor of Computer Science at Stanford University, a Google Fellow, a member of the National Academy of Engineering and the German Academy of Sciences. Thrun is best known for his research in robotics and machine learning, specifically his work with self-driving cars.

Adam Sherwin Assistant Instructor

Adam spent seven years inferring and monitoring how people drive, and helping to start and buy lending businesses. Now instead of filling his days with never-ending database queries and presentations, Adam hopes to help everyone learn statistics.

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There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.