Course Description
Guides students in studying the process of discovering new knowledge from “Big Data” with applications of data retrieval, data processing, data analysis, modeling, and visualization. Students are expected to have previous programming experience and will design new programs which will be utilized in the data mining approaches.
Athena Title
Adv Programming Data Mining
Prerequisite
Permission of department
Semester Course Offered
Offered every year.
Grading System
A - F (Traditional)
Course Objectives
By the end of this course, students will have: • Understood the background knowledge of data science related to statistics, data mining, machine learning, and big data. • Explored concepts of how to implement data analysis, including statistical tests (hypothesis tests such as student’s t-test, Chi-squared test, ANOVA, and so on), regression and classification, extract pattern, and knowledge or insights from data. • Employed their programming skills on a variety of topics in data science including data processing, pattern recognition, feature selection, statistical tests, model construction, simulation, classification, and prediction. • Created a simulation model or a prediction model in support of their existing thesis/dissertation research. • Enhanced their skills in conducting research by data mining, leading a small research project, and coordinating research activities. • Performed a literature study on recent advances and research in feature selection or machine learning as appropriate with the student’s research focus.
Topical Outline
• Introduction of data science • Advanced applications of programming • Automatic data retrieval • Processing large amounts of data • Programming in data mining • Statistical models and their applications • Programming in simulations • Programming in feature selection • Prediction model and decision making • Unsupervised and supervised learning • Data visualization • Recent advances and research in machine learning
Syllabus