Undergraduate Course Syllabus Layout
Undergraduate Course Syllabus
Prepared by: [YOUR NAME]
Email: [YOUR EMAIL]
Company: [YOUR COMPANY NAME]
Company Number: [YOUR COMPANY NUMBER]
Company Address: [YOUR COMPANY ADDRESS]
Company Website: [YOUR COMPANY WEBSITE]
Company Social Media: [YOUR COMPANY SOCIAL MEDIA]
Introduction
Welcome to the Undergraduate Course Syllabus for the course titled Introduction to Data Science. This syllabus provides a comprehensive overview for planning and structuring the course. It includes course objectives, weekly topics, assessment methods, and important dates. This syllabus is prepared by [YOUR NAME] and serves as a foundational tool to ensure that both students and instructors are aligned with the course expectations and requirements.
Course Details
Course Title: Introduction to Data Science
Course Code: DS101
Instructor: Dr. Francisco Baxter
Office Hours: Mondays 2:00 PM - 4:00 PM
Contact Information: [email protected]
Class Location: Room 204, Science Building
Class Time: Tuesdays and Thursdays, 10:00 AM - 11:30 AM
Course Objectives
The objectives of this course are to:
-
Understand fundamental data science concepts and techniques.
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Apply statistical methods to real-world data sets.
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Develop proficiency in using data science tools and programming languages.
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Interpret and communicate data-driven insights effectively.
Weekly Schedule
|
Week |
Date |
Topic |
Reading Assignment |
Important Dates |
|---|---|---|---|---|
|
1 |
January 15, 2050 |
Introduction to Data Science |
Data Science Handbook, Chapter 1 |
- |
|
2 |
January 22, 2050 |
Data Collection and Cleaning |
Data Science Handbook, Chapter 2 |
- |
|
3 |
January 29, 2050 |
Exploratory Data Analysis |
Data Science Handbook, Chapter 3 |
Quiz 1 on January 31, 2050 |
|
4 |
February 5, 2050 |
Statistical Analysis Techniques |
Data Science Handbook, Chapter 4 |
Assignment 1 Due on February 7, 2050 |
|
5 |
February 12, 2050 |
Machine Learning Basics |
Data Science Handbook, Chapter 5 |
- |
|
6 |
February 19, 2050 |
Midterm Review |
Data Science Handbook, Chapter 6 |
Midterm Exam on February 21, 2050 |
|
7 |
February 26, 2050 |
Data Visualization |
Data Science Handbook, Chapter 7 |
Project Proposal Due on February 28, 2050 |
|
8 |
March 4, 2050 |
Advanced Topics in Machine Learning |
Data Science Handbook, Chapter 8 |
- |
|
9 |
March 11, 2050 |
Final Project Presentations |
Data Science Handbook, Chapter 9 |
Final Exam on March 14, 2050 |
Assessment Methods
-
Assignments: 30% of final grade
-
Quizzes: 20% of final grade
-
Midterm Exam: 25% of final grade
-
Project: 15% of final grade
-
Final Exam: 10% of final grade
Course Policies
-
Attendance: Regular attendance is required. Missing more than 3 classes may result in a reduction in grade.
-
Late Assignments: Late assignments will be penalized by 10% per day unless prior arrangements are made.
-
Academic Integrity: All students are expected to adhere to the academic integrity policy outlined by [YOUR COMPANY NAME].
Contact Information
For any questions or concerns regarding this course, please contact the instructor at [email protected] or visit during office hours.
This syllabus is subject to change. Any updates will be communicated promptly through class announcements or email.
Prepared by: [YOUR NAME]
Email: [YOUR EMAIL]
Company: [YOUR COMPANY NAME]
Company Number: [YOUR COMPANY NUMBER]
Company Address: [YOUR COMPANY ADDRESS]
Company Website: [YOUR COMPANY WEBSITE]
Company Social Media: [YOUR COMPANY SOCIAL MEDIA]
Feel free to reach out if you have any questions or need further clarification about the course. We look forward to a productive and engaging semester!
