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. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
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Introduction to Statistics
斯坦福大学課程信息
- Basic familiarity with computers and productivity software
- No calculus required
- Basic familiarity with computers and productivity software
- No calculus required
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斯坦福大学
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
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Introduction and Descriptive Statistics for Exploring Data
This module provides an overview of the course and a review of the main tools used in descriptive statistics to visualize information.
Producing Data and Sampling
In this module, you will look at the main concepts for sampling and designing experiments. You will learn about curious pitfalls and how to evaluate the effectiveness of such experiments.
Probability
In this module, you will learn about the definition of probability and the essential rules of probability that you will need for solving both simple and complex challenges. You will also learn about examples of how simple rules of probability are used to create solutions for real-life complex situations.
Normal Approximation and Binomial Distribution
This module covers the empirical rule and normal approximation for data, a technique that is used in many statistical procedures. You will also learn about the binomial distribution and the basics of random variables.
Sampling Distributions and the Central Limit Theorem
In this module, you will learn about the Law of Large Numbers and the Central Limit Theorem. You will also learn how to differentiate between the different types of histograms present in statistical analysis.
Regression
This module covers regression, arguably the most important statistical technique based on its versatility to solve different types of statistical problems. You will learn about inference, regression, and how to do regression diagnostics.
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- 5 stars70.18%
- 4 stars19.87%
- 3 stars6.13%
- 2 stars2.07%
- 1 star1.72%
來自INTRODUCTION TO STATISTICS的熱門評論
A very good course. Definitely a course to take for an introduction into Statistics. Also probably going to be very useful as I'm planning on taking Machine Learning.
The course was very concisely presented, emphasizing key statistical techniques and several helpful examples.
Excellent course... Only improvement I can suggest is to add more problems in the lectures as well as quizzes.
The course was very interesting. A lot of concepts were touched up on, but the explanation part was a little less. I can label it as a very good introductory course to Statistics.
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