diff --git a/docs/数据科学/Data100.en.md b/docs/数据科学/Data100.en.md
index 8eceee35..74ed4acd 100644
--- a/docs/数据科学/Data100.en.md
+++ b/docs/数据科学/Data100.en.md
@@ -3,7 +3,7 @@
 ## Description
 
 - Offered by: UC Berkeley
-- Prerequisites: CS61A, Linear Algebra
+- Prerequisites: Data8, CS61A, Linear Algebra
 - Programming Languages: Python
 - Difficulty: 🌟🌟🌟
 - Class Hour: 80 hours
@@ -11,7 +11,7 @@
 This is Berkeley's introductory course in data science, covering the basics of data cleaning, feature extraction, data visualization, machine learning and inference, as well as common data science tools such as Pandas, Numpy, and Matplotlib. The course is also rich in interesting programming assignments, which is one of the highlights of the course.
 
 ## Resources
-- Course Website: <https://ds100.org/fa21/>
+- Course Website: <https://ds100.org>
 - Records: refer to the course website
 - Textbook: <https://www.textbook.ds100.org/intro.html>
 - Assignments: refer to the course website
diff --git a/docs/数据科学/Data100.md b/docs/数据科学/Data100.md
index 5f23e09f..d91221fc 100644
--- a/docs/数据科学/Data100.md
+++ b/docs/数据科学/Data100.md
@@ -3,7 +3,7 @@
 ## 课程简介
 
 - 所属大学：UC Berkeley
-- 先修要求：CS61A，线性代数
+- 先修要求：Data8, CS61A，线性代数
 - 编程语言：Python
 - 课程难度：🌟🌟🌟
 - 预计学时：80 小时
@@ -12,7 +12,7 @@
 
 ## 课程资源
 
-- 课程网站：<https://ds100.org/fa21/>
+- 课程网站：<https://ds100.org/>
 - 课程视频：参见课程网站
 - 课程教材：<https://www.textbook.ds100.org/intro.html>
 - 课程作业：参见课程网站