Undergraduate Course Syllabus
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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: francisco@email.com
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.
Apply statistical methods to real-world data sets.
Develop proficiency in using data science tools and programming languages.
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 francisco@email.com 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!
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