Associate Professor in College of Information at UNT
My philosophy of teaching is grounded in the belief that education is not merely the transmission of knowledge, but the igniting of a lifelong passion for learning. I strive to create an environment where students are not just passive recipients of information but active, engaged learners who are inspired and motivated to explore, question, and discover.
I have consistently received positive evaluations from students. Here is a summary list of the courses I taught and the review: teaching summary.
This introductory course provides a comprehensive overview of data science, designed to equip students with the fundamental concepts, tools, and skills necessary to understand and engage with the world of data analysis and modeling. Throughout the semester, students will explore the lifecycle of data science projects, including data collection, cleaning, analysis, and visualization.
Key topics include an introduction to statistical methods, machine learning, data manipulation, and the ethical implications of data use. Students will learn to use popular data science tools and programming languages, primarily R, to handle real-world datasets. By the end of the course, participants will be able to design basic data models and interpret their outputs, preparing them for more advanced studies in data science or roles that require analytical skills.
Course is facing undergraduate students majoring in Data Science.
Prerequisite: MATH 1650 and CSCE 1030.
Course Information: Syllabus
This course is designed to equip students with the skills to inetgrate statistical methods with graphic-centered, computer-based analysis of both structured and unstructured data. Students will explore both the theoretical foundations of visual design and the practical aspects of data visualization, enabling students to apply visualization techniques and tools for visual analysis of large data sets.
Well-designed Data Visualization would improve comprehension, memory, inference, and decision making. This course introduces techniques, algorithms, and tools for creating effective data visualizations based on principles and techniques from graphic design, visual art, perceptual psychology, and cognitive science. Emphasis is placed on the identification of patterns, trends, and differences among data sets.
Course is facing graduate students in Information Science, Data Science, and Library Science.
Prerequisite: None
Course Information: Syllabus
The Data Science Practicum offers an immersive, hands-on experience designed to consolidate students' knowledge and skills in data science through applied projects. This course is tailored for students who have a foundational understanding of data science concepts and are ready to apply these in real-world scenarios.
Throughout the term, students will engage directly with complex data sets and conduct end-to-end data science projects under the guidance of industry professionals and faculty advisors. These projects will involve data acquisition, cleaning, exploration, modeling, and interpretation of results with an emphasis on practical problem-solving and innovation.
Application: Students must have completed 18 credit hours before they can enroll in practicum. See the application process here.
Course Information: Syllabus
The internet offers a vast amount of data, necessitating an understanding of how to utilize this wealth of information more effectively. This knowledge will not only serve to advance library related research, e.g., digital resource management, data-driven library services, but also equip librarians the knowledge and skills to provide data literacy services that assist users in better comprehending and employing online data. This session will cover data collection, classification, and transformation methods using Python. For data collection, we will provide an overview of web communication through hyperlinks and APIs. Practical considerations for accessing APIs, such as obtaining credentials and making requests using Python. For data classification and transformation, we will introduce the use of open sourced large language models (LLM) such as Llama2 for the automatic classification of data into predetermined categories or hierarchies for further analysis. By the end of the course, participants will be equipped with the necessary knowledge and skills to collect, preprocess, and analyze data from online platforms, thereby enhancing their ability to conduct research and provide data related services.
Learning Objectives:
Here to download the slides: data collection classification.
Textual analysis has applications across various fields for interpreting and analyzing the content, structure, and context of texts. In LIS, textual analysis can be applied to digital humanity and literary analysis projects, to uncover trends, themes, and relationships within vast corpora of texts such as historical documents and digital archives. Textual analysis has wide applications in Social Sciences research in examining interview transcripts, speeches, news articles, and social media posts. This session will first introduce fundamental concepts of text mining and textual analysis tools such as SÉANCE with its indices, then introduce statistical analysis to facilitate a deeper understanding of textual analysis results. Hands-on guidance will be provided for LIS researchers and practitioners on utilizing tools such as SÉANCE for text analysis. Participants will be adept at tasks such as textual data preprocessing, linguistic indices analysis using SEANCE, and statistical analysis for insights. LIS participants will gain basic knowledge and skills that will be useful to conduct data-driven research and provide data literacy services.
Learning Objectives:
Here to download the slides: text analysis.