CS 470: Data Mining

(Fall 2026)

This course will introduce fundamental methods and principles for working with, learning from, and automatically extracting non-trivial patterns and knowledge from big data

Logistics

Instructor:
- Prof. Temiloluwa Prioleau
Teaching Assistant
- - Jason Wu
Class Time & Location:
- TTH 11:30am - 12:45pm
- White Hall 103

Instructor Office Hours (Prof. Prioleau):
- Thursday 1 - 2pm or right after class on TTH
- Math & Science Center W302J
TA Office Hours:
- Thursday & Friday 1:30 - 2:30pm
- Math & Science Center N426

Course Overview

This course offers an introduction to data mining concepts and techniques. The goal is for the students to have a solid foundation in data mining that allows them to apply data mining techniques to real-world problems and to conduct research and development of new data mining methods. Topics include data processing, data mining algorithms and methods such as association analysis, classification, cluster analysis, as well as emerging topics in mining complex data.

This course is for students who...

Have completed prerequisites CS 224 (Foundations of Computer Science) and CS 253 (Data Structures and Algorithms) or equivalent transfer credit as prerequisites
Are comfortable programming with Python

Reference Textbooks:

Data Mining: Concepts and Techniques. 3rd Edition. Jiawei Han, Micheline Kamber, Jian Pei
Introduction to Data Mining. 2nd Edition. Pang-Ning Tan, Anuj Karpatne, Michael Steinbach, Vipin Kumar
Mining of Massive Datasets. J. Leskovec, A. Rajaraman, J. Ullman

WhaT You Will Learn?

Students will learn foundational skills for:

Working with, learning from, and extracting non-trivial patterns from data
Analyzing different types of real-world datasets, including tabular, time-series, text, and image datasets
Conducting automated analysis of big datasets to support hypothesis generation and data-driven decision making
Reading and writing data-centric research papers on emerging topics that benefit from mining complex datasets.

What is Data Mining?

Data mining is a domain for automatically extracting knowledge from big data, where knowledge refers to interesting patterns that are non-trivial, implicit, previously unknown, and potentially useful.

Acknowledgements

This course is informed by the work of many outstanding educators including Emory Profs. Kai Shu, Davide Fossati, and non-Emory Profs. Pang-Ning Tan, Anuj Karpatne, Michael Steinbach, Vipin Kumar, Jiawei Han, Micheline Kamber, Jian Pei, J. Leskovec, A. Rajaraman, J. Ullman and more.

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