diff --git a/docs/数据科学/Data100.en.md b/docs/数据科学/Data100.en.md
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@@ -0,0 +1,17 @@
+# UCB Data100: Principles and Techniques of Data Science
+
+## Description
+
+- Offered by: UC Berkeley
+- Prerequisites: CS61A，Linear Algebra
+- Programming Languages: Python
+- Difficulty: 🌟🌟🌟
+- Class Hour: 80 hours
+
+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/>
+- Records: refer to course website
+- Textbook: <https://www.textbook.ds100.org/intro.html>
+- Assignments: refer to course website
diff --git a/docs/编程入门/MIT-Missing-Semester.en.md b/docs/编程入门/MIT-Missing-Semester.en.md
index 5889b489..4f564737 100644
--- a/docs/编程入门/MIT-Missing-Semester.en.md
+++ b/docs/编程入门/MIT-Missing-Semester.en.md
@@ -4,7 +4,7 @@
 
 - Offered by: MIT
 - Prerequisites: None
-- Programming lanuages: Shell
+- Programming Languages: Shell
 - Difficulty: 🌟🌟
 - Class Hour: 10 hours
 
@@ -14,4 +14,4 @@ Just as the course name indicated, this course will teach the missing things in
 
 - Homepage: <https://missing.csail.mit.edu/>
 - Records: <https://www.youtube.com/playlist?list=PLyzOVJj3bHQuloKGG59rS43e29ro7I57J>
-- Assignments: Some exercises behind each lecture.
\ No newline at end of file
+- Assignments: Some exercises after each lecture.