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.

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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:

1. Understand fundamental data science concepts and techniques.

2. Apply statistical methods to real-world data sets.

3. Develop proficiency in using data science tools and programming languages.

4. 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

1. Assignments: 30% of final grade

2. Quizzes: 20% of final grade

3. Midterm Exam: 25% of final grade

4. Project: 15% of final grade

5. 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.

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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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