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Stanford's 'Introduction to Statistics' on Coursera covers statistical thinking, exploratory data analysis, sampling, significance tests, and more. Gain foundational skills for advanced statistical topics and machine learning. Topics: Descriptive Stats, Probability, Regression, and more.
- Probability and Statistics
The course may not offer an audit option. You can try a Free...
- Basic Statistics
Learn the basics of statistics to understand social and...
- Probability and Statistics
This course develops statistical and critical thinking using probability and descriptive statistics. Key topics include sampling, distributions, the binomial distribution, interval estimation, hypothesis testing, regression, and significance tests.
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts.
28 paź 2024 · 7. Udacity (Free Courses) While Udacity is known for its paid Nanodegree programs, the platform also offers several free courses in statistics and related fields. The free courses are self-paced and provide a solid introduction to various statistical concepts, including descriptive and inferential statistics.
Stanford's "Introduction to Statistics" teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts.
Learn the basics of statistics to understand social and behavioral research. This University of Amsterdam course covers descriptive statistics, probability, and inferential statistics, including confidence intervals and significance tests, using statistical software.
This course provides an elementary introduction to probability and statistics with applications. Topics include basic combinatorics, random variables, probability distributions, Bayesian inference, hypothesis testing, confidence intervals, and linear regression.
