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.
This course will cover visualization, probability, regression and other topics that will help you learn the basic methods of understanding data with statistics.
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.
Seeing relationships in data and predicting based on them; Simpson's paradox
Probability; Bayes Rule; Correlation vs. Causation
Maximum Likelihood Estimation; Mean, Median, Mode; Standard Deviation, Variance
Outliers, Quartiles; Binomial Distribution; Central Limit Theorem; Manipulating Normal Distribution
Confidence intervals; Hypothesis Testing
Linear regression; correlation
This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course.
This class will always be available!
Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. If you want to know more, just enroll in the course and start exploring.
Yes! The point is for you to learn what YOU need (or want) to learn. If you already know something, feel free to skip ahead. If you ever find that you’re confused, you can always go back and watch something that you skipped.
It’s completely free! If you’re feeling generous, we would love to have you contribute your thoughts, questions, and answers to the course discussion forum.
Collaboration is a great way to learn. You should do it! The key is to use collaboration as a way to enhance learning, not as a way of sharing answers without understanding them.
Udacity classes are a little different from traditional courses. We intersperse our video segments with interactive questions. There are many reasons for including these questions: to get you thinking, to check your understanding, for fun, etc... But really, they are there to help you learn. They are NOT there to evaluate your intelligence, so try not to let them stress you out.
Learn actively! You will retain more of what you learn if you take notes, draw diagrams, make notecards, and actively try to make sense of the material.
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.