CSCI 6951 - Data Science and Machine Learning
Fall 2026 Syllabus, Section 02, CRN 42711,
Credit hours: 3
Course Meeting Times
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Instructor
Zack While
Email: zwhile@ysu.edu
Course Description
6951. Data Science and Machine Learning. Basic methodologies for the data science pipeline: data acquisition and cleaning, handling missing data, exploratory data analysis, visualization, feature engineering, modeling, interpretation, and presentation in the context of real-world datasets. Classical models and techniques for classification, clustering, anomaly detection, deep learning, and collaborative filtering. Cross-Listed: CSCI 4851. 3 s.h.
Course Readings
The course readings are subject to change in the event of extenuating circumstances, research developments, current events, and/or to ensure better learning.
Assignments/Assessments
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Tentative Course Schedule
| Week of | Reading(s) | Proposed Topic | Due/To Prepare for Class |
|---|---|---|---|
| 8/24 | Chapter 1 Chapter 2 |
Introduction to Data Science Crash Course in Python |
|
| 8/31 | Chapter 4 Chapter 5 Chapter 6 Chapter 7 |
Theoretical Background | Quiz 1 Homework 0 |
| 9/7 | Chapter 3 | Data Visualization | Quiz 2 |
| 9/14 | Chapter 9 | Getting Data | Quiz 3 Homework 1 |
| 9/21 | Chapter 10 Chapter 26 |
Working with Data Data Ethics |
Quiz 4 |
| 9/28 | Exam 1 Review | Quiz 5 Exam 1 |
|
| 10/5 | Chapter 11 | Machine Learning Concepts | Homework 2 |
| 10/12 | Chapter 12 Chapter 17 |
K-Nearest Neighbors Decision Trees |
Quiz 6 Project Proposal |
| 10/19 | Chapter 13 | Naïve Bayes | Quiz 7 Homework 3 |
| 10/26 | Chapter 20 Chapter 23 |
Clustering Recommender Systems |
Quiz 8 |
| 11/2 | Exam 2 Review | Quiz 9 | |
| 11/9 | Chapter 14 | Simple Linear Regression | Homework 4 |
| 11/16 | Chapter 15 Chapter 16 |
Multiple Regression Logistic Regression |
Project Poster Homework 5 Project Code |
| 11/23 | Chapter 8 | Gradient Descent | Quiz 11 Project Report |
| 11/30 | Chapter 18 | Neural Networks | Quiz 12 Poster Presentation Poster Session |
| 12/7 | Quiz 13 Exam 3 |
The course schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better learning.