Statistics and probability control your life. I don’t just mean What YouTube’s algorithm recommends you to watch next, and I don’t just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.
统计和概率控制着你的生活。我指的不仅仅是YouTube的算法建议你接下来观看的内容，我也不只是指在课堂上或酒吧里遇到你未来的另一半的机会。人类行为、单细胞生物、地震、股市、12月第一周是否会下雪，以及无数其他现象都是概率和统计的。甚至宇宙最基本的深层结构的本质也受到概率和统计学的支配。

You need to understand statistics.
你需要了解统计学。

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called ‘data science’ and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.
人类文明的几乎所有领域都在结合代码和数值计算。这意味着许多工作和研究领域都是基于统计和机器学习技术在 Python 和 MATLAB 等编程语言中的应用。这通常被称为“数据科学”，是一个越来越重要的话题。统计和机器学习也是人工智能 （AI） 和商业智能的基础。

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field — ranging from data scientist to engineering to research scientist to deep learning modeler — you’ll need to know statistics and machine-learning. And you’ll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.
如果你想让自己成为任何技术领域的面向未来的员工、雇主、数据科学家或研究人员——从数据科学家到工程学，从研究科学家到深度学习建模师——你需要了解统计学和机器学习。您需要知道如何在 Python 或 MATLAB 等计算机语言中实现概率论和置信区间、k 均值聚类和 PCA、Spearman 相关和逻辑回归等概念。

There are six reasons why you should take this course:
您应该参加本课程有六个原因：

• This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.
  本课程涵盖了理解统计学、机器学习和数据科学基础知识所需的一切，从条形图到方差分析，从回归到 k 均值，从 t 检验到非参数排列检验。

• After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren’t taught here. That’s because you will learn the foundations upon which advanced methods are build.
  完成本课程后，您将能够理解广泛的统计和机器学习分析，甚至是此处未教授的特定高级方法。这是因为您将学习构建高级方法的基础。

• This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.
  本课程在数学严谨性与直观解释和代码实践探索之间取得平衡。

• Enrolling in the course gives you access to the Q&A, in which I actively participate every day.
  注册该课程可以让您访问我每天都积极参与的问答环节。

• I’ve been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool.
  我研究、开发和教授统计学已经有 20 多年了，我认为数学真的很酷。

What you need to know before taking this course:
在参加本课程之前，您需要了解的内容：

• High-school level maths. This is an applications-oriented course, so I don’t go into a lot of detail about proofs, derivations, or calculus.
  高中水平的数学。这是一门面向应用的课程，所以我不会详细介绍证明、推导或微积分。

• Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don’t have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.
  Python或MATLAB的基本编码技能。仅当您想按照代码进行操作时，才需要这样做。您无需编写任何代码即可成功完成本课程！但是参加编码练习将帮助您学习材料。MATLAB 代码依赖于“统计和机器学习”工具箱（如果您没有 MATLAB 或统计工具箱，则可以使用 Octave）。Python 代码是在 Jupyter 笔记本中编写的。

• I recommend taking my free course called “Statistics literacy for non-statisticians“. It’s 90 minutes long and will give you a bird’s-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!
  我建议参加我的免费课程，名为“非统计学家的统计素养”。它长达 90 分钟，将为您提供统计学主要主题的鸟瞰图，我在本课程中将更详细地介绍这些主题。请注意，本课程不需要免费的短期课程，但很好地补充了本课程。如果您以 1.5 倍的速度观看，您可以在不到一个小时的时间内完成整个过程！

• You do not need any previous experience with statistics, machine learning, deep learning, or data science. That’s why you’re here!
  您不需要任何统计学、机器学习、深度学习或数据科学方面的经验。这就是你来这里的原因！

Is this course up to date?
这门课程是最新的吗？

Yes, I maintain all of my courses regularly. I add new lectures to keep the course “alive,” and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).
是的，我会定期维护我所有的课程。我添加新的讲座以保持课程的“活力”，如果学生发现某个主题令人困惑，或者我在讲座中犯了错误，我会添加新的讲座（或有时重新拍摄现有的讲座）以更好地解释数学概念（很少见，但它发生了！

You can check the “Last updated” text at the top of this page to see when I last worked on improving this course!
您可以查看本页顶部的“上次更新”文本，了解我上次改进本课程的时间！

What if you have questions about the material?
如果您对材料有疑问怎么办？

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.
本课程有一个问答（问答）部分，您可以在其中发布有关课程材料（关于数学、统计学、编码或机器学习方面）的问题。我尝试在一天内回答所有问题。您还可以查看所有其他问题和答案，这确实提高了您可以学到的程度！您可以通过发布正在进行的讨论来为问答做出贡献。

And, you can also post your code for feedback or just to show off — I love it when students actually write better code than me! (Ahem, doesn’t happen so often.)
而且，你也可以发布你的代码以获得反馈，或者只是为了炫耀——我喜欢学生真正写出比我更好的代码！（咳咳，这种情况并不经常发生。

What should you do now?
你现在应该怎么做？

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you’re ready, invest in your brain by learning from this course!
首先，恭喜您读到这里;这意味着你对学习统计学和机器学习非常感兴趣。观看预览视频，查看评论，当您准备好时，通过学习本课程来投资您的大脑！