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
| Group | Title | Author | ISBN |
|---|---|---|---|
| Required | Data Science from Scratch: First Principles with Python (2e) | Joel Grus | 9781492041139 |
| Optional | Hands-On Machine Learning with Scikit-Learn and PyTorch | Aurélien Géron | 9798341607972 |
| Optional | Hands-On Large Language Models | Jay Alammar, Maarten Grootendorst | 9781098150952 |
All are available for free online via Maag Library and O'Reilly.
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 | Working with Data | Quiz 4 |
| 9/28 | Exam 1 Review Exam 1 |
Quiz 5 | |
| 10/5 | Chapter 11 Chapter 12 Chapter 2 (Géron) |
Machine Learning Concepts K-Nearest Neighbors Hyperparameter Tuning |
Homework 2 |
| 10/12 | Chapter 13 Chapter 17 Chapter 6 (Géron) |
Decision Trees Ensemble Models Naïve Bayes |
Quiz 6 Project Proposal |
| 10/19 | Chapter 14 Chapter 15 Chapter 16 |
Linear Regression Logistic Regression |
Quiz 7 Homework 3 |
| 10/26 | Chapter 8 Chapter 18 |
Gradient Descent Neural Networks |
Quiz 8 |
| 11/2 | Exam 2 Review Exam 2 |
Quiz 9 | |
| 11/9 | Chapter 13 (Géron) Chapter 8 |
Time Series Modeling Unsupervised Learning |
Homework 4 |
| 11/16 | Chapter 21 Chapter 2 (Alammar & Grootendorst) |
Natural Language Processing | Project Poster Project Code Quiz 10 |
| 11/23 | Chapter 23 | Recommender Systems | Quiz 11 Homework 5 |
| 11/30 | Chapter 1 (Alammar & Grootendorst) Chapter 8 (Alammar & Grootendorst) |
Foundation Models & Generative AI Poster Session |
Quiz 12 Poster Presentation Project Report Quiz 13 |
| 12/7 | 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.