Printable Graduate Course Syllabus
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.
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.
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Develop proficiency in machine learning algorithms.
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Create effective data visualizations.
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Analyze and interpret complex datasets.
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 |
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. |
Course Policies
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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.
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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