Course Overview课程概述

This Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including – data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. 
这门数据科学统计课程旨在介绍用于数据分析的统计方法和程序的基本原理。完成本课程后，您将掌握统计中关键主题的实用知识，包括——数据收集、使用描述性统计总结数据、数据的展示和可视化、检验变量之间的关系、概率分布、期望值、假设检验、方差分析（ANOVA）入门、回归和相关分析。

You will take a hands-on approach to statistical analysis using Python programming language and Jupyter Notebooks – the tools of choice for modern Data Scientists and Data Analysts. At the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. 
您将采用实践方法，使用 Python 编程语言和 Jupyter Notebook 进行统计分析——这是现代数据科学家和数据分析师的首选工具。课程结束时，您将完成一个项目，将课程中的各种概念应用于一个涉及现实生活启发场景的数据科学问题，并展示对基础统计思维和推理的理解。重点在于清晰地理解不同数据类型的不同方法，培养直观理解，对所提出的方法进行适当评估，使用 Python 分析我们的数据，并准确解释输出结果。

This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. Optional refreshers for using Jupyter notebook and popular Python libraries are also provided. They are intended to help review some of the usages of Jupyter and Python in this course and are not a replacement for becoming proficient in Python.
本课程适合各种专业人员和希望开始数据与统计驱动型职业旅程的学生，例如数据科学家、数据分析师、商业分析师、统计学家和研究人员。我们强烈建议在开始本课程之前先学习《Python 数据科学》课程，以熟悉 Python 编程语言、Jupyter 笔记本和库。同时，也提供了使用 Jupyter 笔记本和流行 Python 库的选修复习内容。这些内容旨在帮助学生复习本课程中 Jupyter 和 Python 的部分用法，并非替代熟练掌握 Python。

Important  重要

Every module of the course has:
本课程的每个模块都包含：

• A hands-on assignment in Python,
  一个 Python 实践作业，
• A practice quiz to test your understanding of the material, and
  一个用于测试您对材料理解的练习测验，以及
• A graded quiz that will contribute to 17% of the total grade for the course.
  一个计分的测验，将占课程总成绩的 17%。

In the final week, you will be given a project that you will need to complete and submit the notebook for AI evaluation. This project will be worth 15% of the final grade for the course.
在最后一周，您将获得一个项目，需要完成并提交笔记本进行 AI 评估。该项目将占课程最终成绩的 15%。