数据科学基础:重抽样推理思维

Foundations of Data Science: Inferential Thinking by Resampling

Learn how to use inferential thinking to make conclusions about unknowns based on data in random samples.

571 次查看
加州大学伯克利分校
edX
  • 完成时间大约为 5
  • 初级
  • 英语
注:因开课平台的各种因素变化,以上开课日期仅供参考

你将学到什么

The logical and conceptual frameworks of statistical inference

How to conduct hypothesis testing, permutation testing, and A/B testing

The purpose and power of resampling methods

Relations between sample size and accuracy

P-values, quantifying uncertainty, and generating confidence intervals using the bootstrap method

How to interpret the results from hypothesis testing

课程概况

This course will teach you the power of statistical inference: given a random sample, how do we predict some quantity that we cannot observe directly?

Using real-world examples from a wide array of domains including law, medicine and football, you’ll learn how data scientists make conclusions about unknowns based on the data available. Often, the data we have is not complete, yet we’d still like to draw inferences about the world and quantify the uncertainty in our conclusions. This is called statistical inference. In this course, you will learn the framework for statistical inference and apply them to real-world data sets.

Notably, you will develop the practice of hypothesis testing—comparing theoretical predictions to actual data, and choosing whether to accept those predictions. This method allows us to evaluate theories or hypotheses about how the world works.

You will also learn how to quantify the uncertainty in the conclusions you draw from hypothesis testing. This helps assess whether patterns that appear to be present in the data actually represent a true relationship in the world, or whether they might merely reflect random fluctuations due to noise. Throughout this course, we will go over multiple methods for estimation and hypothesis testing, based on simulations and the bootstrap method. Finally, you will learn about randomized controlled experiments and how to draw conclusions about causality.

The course emphasizes the conceptual basis of inference, the logic of the decision-making process, and the sound interpretation of results.

预备知识

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