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* Update Data100.md Prerequisites While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites: Foundations of Data Science: Data 8 covers much of the material in Data 100 but at an introductory level. Data8 provides basic exposure to python programming and working with tabular data as well as visualization, statistics, and machine learning. Computing: The Structure and Interpretation of Computer Programs CS 61A or Computational Structures in Data Science CS 88. These courses provide additional background in python programming (e.g., for loops, lambdas, debugging, and complexity) that will enable Data 100 to focus more on the concepts in Data Science and less on the details of programming in python. Math: Linear Algebra (Math 54, EE 16A, or Stat 89A): We will need some basic concepts like linear operators and derivatives to enable statistical inference and derive new prediction algorithms. This may be satisfied concurrently to Data 100. * Update Data100.en.md Prerequisites While we are working to make this class widely accessible we currently require the following (or equivalent) prerequisites: Foundations of Data Science: Data 8 covers much of the material in Data 100 but at an introductory level. Data8 provides basic exposure to python programming and working with tabular data as well as visualization, statistics, and machine learning. Computing: The Structure and Interpretation of Computer Programs CS 61A or Computational Structures in Data Science CS 88. These courses provide additional background in python programming (e.g., for loops, lambdas, debugging, and complexity) that will enable Data 100 to focus more on the concepts in Data Science and less on the details of programming in python. Math: Linear Algebra (Math 54, EE 16A, or Stat 89A): We will need some basic concepts like linear operators and derivatives to enable statistical inference and derive new prediction algorithms. This may be satisfied concurrently to Data 100. * Update Data100.en.md
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712 B
Markdown
18 lines
712 B
Markdown
# UCB Data100: Principles and Techniques of Data Science
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## 课程简介
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- 所属大学：UC Berkeley
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- 先修要求：Data8, CS61A，线性代数
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- 编程语言：Python
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- 课程难度：🌟🌟🌟
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- 预计学时：80 小时
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伯克利的数据科学入门课程，内容相对基础，覆盖了数据清洗、特征提取、数据可视化以及机器学习和推理的基础内容，也会讲授 Pandas, Numpy, Matplotlib 等数据科学常用工具。其丰富有趣的编程作业也是这门课的一大亮点。
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## 课程资源
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- 课程网站：<https://ds100.org/>
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- 课程视频：参见课程网站
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- 课程教材：<https://www.textbook.ds100.org/intro.html>
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- 课程作业：参见课程网站
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