Printable Graduate Course Syllabus

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Course Title: Advanced Data Analytics
Course Code: ADA 501
Term: Fall 2050
Instructor: Dr. Robert Scott
Office Hours: Tuesdays 2:00 PM - 4:00 PM
Contact Information: robert@email.com

Introduction:

Welcome to Advanced Data Analytics. This syllabus provides an overview of the course, including its objectives, schedule, and grading criteria. Please review this document carefully to ensure you understand the course requirements and expectations. For any questions or clarifications, feel free to reach out during office hours or via email.

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Course Overview

Course Description:

This course explores advanced techniques in data analytics, including statistical methods, machine learning algorithms, and data visualization. Students will learn to analyze complex datasets and apply analytical methods to real-world problems.

Prerequisites:

Basic knowledge of statistics and data analysis is required. Completion of ADA 101 or equivalent is recommended.

Course Objectives:

• Understand and apply advanced statistical techniques.

• Develop proficiency in machine learning algorithms.

• Create effective data visualizations.

• Analyze and interpret complex datasets.

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Course Schedule

The following table outlines the schedule for the course, including key topics and important dates. Please note that this schedule is subject to change based on class progress and other considerations.

Week	Date	Topic	Readings/Assignments	Important Deadlines
1	September 4, 2050	Introduction to Data Analytics	Chapter 1 of Data Science Essentials by Aiden Parker	Assignment 1 Due Date: September 11, 2050
2	September 11, 2050	Statistical Methods	Chapter 2 of Data Science Essentials by Aiden Parker	Assignment 2 Due Date: September 18, 2050
3	September 18, 2050	Machine Learning Basics	Chapter 3 of Machine Learning Foundations by Sophia Taylor	Midterm Exam: October 2, 2050
4	September 25, 2050	Advanced Algorithms	Chapter 4 of Machine Learning Foundations by Sophia Taylor	Assignment 3 Due Date: October 9, 2050
5	October 2, 2050	Data Visualization	Chapter 5 of Visualizing Data by Robert GRasmussenreen	Project Proposal Due Date: October 16, 2050
6	October 9, 2050	Case Studies	Chapter 6 of Visualizing Data by Robert Rasmussen	Assignment 4 Due Date: October 23, 2050
7	October 16, 2050	Project Work	Case Study Materials (Provided in Class)	Presentation Date: October 30, 2050
8	October 23, 2050	Review and Exam Preparation	Review Materials (To be distributed)	Assignment 5 Due Date: October 30, 2050
9	October 30, 2050	Final Exam	Final Exam Materials (To be distributed)	Final Exam: October 30, 2050

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Grading Criteria

Component	Percentage	Description
Participation	10%	Active participation in class discussions and activities.
Assignments	30%	Weekly assignments to apply course concepts.
Midterm Exam	20%	Exam covering material from Weeks 1-3.
Final Project	20%	Comprehensive project applying all learned techniques.
Final Exam	20%	Cumulative exam covering the entire course material.

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Course Policies

• Attendance: Regular attendance is expected. Absences must be communicated in advance and documented if necessary.

• Late Assignments: Assignments submitted after the due date will incur a 10% penalty per day late unless prior arrangements are made.

• Academic Integrity: All students are expected to adhere to the university’s academic integrity policy. Plagiarism or cheating will result in disciplinary action.

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Contact Information

For any inquiries related to this course, please contact me via email at [YOUR EMAIL] or during my office hours.

Thank you for your attention to this syllabus. I look forward to a productive and engaging term.

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