George Mason University

Department of Computer Science

CS 484: Data Mining (ONLINE)

Spring 2021

Professor Jessica Lin

Course Description

Basic principles and methods for data analysis and knowledge discovery. Emphasizes developing basic skills for modeling and prediction and performance evaluation. Topics include system design; data quality, preprocessing, and association; event classification; clustering; biometrics; business intelligence; and mining complex types of data.

Class Time and Location

Monday/Wednesday 12:00-1:15pm
Online via Zoom (TBA)

Instructor

Dr. Jessica Lin
Email: jessica [AT] gmu [DOT] edu
Office Hours: Monday 2-4pm

Teaching Assistant

Manpriya Dua
Office Hours: TBA
Office: Online

Prerequisites

• Formally: Grade of C or better in CS 310 (Data Structures) and STAT 344 (Probability and Statistics) or equivalent.
  


• More specifically: Programming experience in Python preferred. Java or C will work as well, but the assignments will use the Python framework. Students should be familiar with basic probability and statistics concepts, and linear algebra. Please expect lots of programming in all the assignments and class project.

• The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
• A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
• The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
• The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
• The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

Grading

Assignments: 40%

Project: 30%

Exams: 30%

          Extra credit possible (participation, competition winners for Homework)

Assignments

There will be four competition-style programming assignments. The preferred programming language is Python. Competition winners will get 1% extra credit added to the final grade.

Exam

There will be quizzes throughout the semester covering lectures and readings (in class), and one final exam. The exams must be taken at the scheduled time and place, unless prior arrangement has been made with the instructor. Missed exam cannot be made up.

Topics

• Ch.1: Introduction
• Ch.2: Data
• Ch.3: Classification
• Ch.4: Classification: Alternative Techniques
• Ch.5: Association Analysis: Basic Concepts and Algorithms
• Ch.6: Association Analysis: Advanced Concepts
• Ch.7: Cluster Analysis: Basic Concepts and Algorithms
• Ch.8: Cluster Analysis: Additional Issues and Algorithms
• Ch.9: Anomaly Detection

Technology Requirement

This class will be 100% online this semester. Technology requirements to successfully complete this class include:

• Computer (desktop/laptop)
• Stable high speed internet connection to attend synchronous class virtually through Zoom
• Functional microphone and webcam

Honor Code Statement

The GMU Honor Code is in effect at all times. In addition, the CS Department has further honor code policies regarding programming projects, which are detailed here. Any deviation from the GMU or the CS department Honor Code is considered an Honor Code violation. All assignments for this class are individual unless otherwise specified.

Learning Disability Accommodation

If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and then discuss with the professor about accommodations.

Course Website