Data Mining Minor

The Data Mining Minor provides students with a foundational understanding of statistical and computational techniques essential for analyzing large datasets. This minor focuses on both theoretical and practical aspects of data mining, enabling students to design and apply data-driven methods across diverse fields.

Minor Course in Data Mining

STK211 – Statistical Methods : 3(2-2)

This course explains the fundamental principles of statistical methods and introduces basic analytical methods that can be applied across various fields, such as Agriculture, Biology, Social Sciences, Business, and others. This course also serves as a foundation for advanced statistical courses, such as Categorical Data Analysis, Regression Analysis, Experimental Design, Quality Control Statistics, and Time Series Analysis. Topics covered in this course include statistical description, probability, principles of estimation and hypothesis testing, estimation and hypothesis testing for proportions, estimation and hypothesis testing for means, correlation, simple linear regression, and contingency tables.

KOM202 – Algorithms and Programming  : 3(2-2)

This course explains the concept of algorithms, algorithm construction, and an introduction to programming, covering programming concepts, fundamentals of algorithms, program structure (keywords, operators, data types), program control structures (conditionals and loops), functions, arrays and strings, structures, and file I/O. Upon completing this course, students will be able to write algorithms to solve simple computational problems and implement them as computer programs using a high-level programming language.

KOM205  –  Database  :  3(2-2)

This course covers the comparison between data storage using file systems and databases, an overview of database systems, database models, the entity-relationship (ER) model, the relational model, relational algebra, normalization, structured query language (SQL), database programming using stored procedures and triggers, as well as database design and implementation in various cases. After completing this course, students are expected to explain fundamental database concepts with an emphasis on the relational model and to design and develop database systems, query optimization, stored procedures, and triggers.

KOM322 – Quantitative Methods : 3(2-2)

Prerequisite: STK202-Introduction to Probability Calculus

This course covers the basics and analytical techniques in experimental design, data collection methods, linear modeling techniques, dimensionality reduction, clustering, an introduction to artificial neural networks, fuzzy logic, and kernel functions for parameter estimation. Upon completing this course, students are expected to understand and apply quantitative methods to problems in the field of computing, including principles and analysis in experimental design, data collection principles, techniques in linear modeling, dimensionality reduction, concepts of distance and clustering, distribution-free density function estimation, as well as principles of neural networks and fuzzy logic.

KOM332 – Data Mining : 3(2-2)

Prerequisites: STK211-Statistical Methods, KOM205-Database

This course covers the concepts of data mining, data and data exploration, data preprocessing, fundamental techniques in clustering and outlier detection, basic classification techniques, foundational methods in association rule mining, the concept of data warehouses and online analytical processing (OLAP), and an introduction to data mining techniques for various data types, including spatial, spatio-temporal, sequential, web, and text data. Upon completing this course, students are expected to apply data preprocessing techniques as an initial stage in data mining and implement basic algorithms in clustering, outlier detection, classification, and association rules for given data mining problems.