Introduction to Statistics
Starting dates and places
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…
Frequently asked questions
There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.
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 InstructorSebastian 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 InstructorAdam 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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