Data Science
DATA 155 Survey of Data Science
Fall/Spring, 3 credit hours
This course provides the fundamentals of data science. It helps students understand and learn some concepts necessary to start and work as data scientists. It covers the definitions, main concepts, in data science.
DATA 230 Applied Data Science - R Programming
Fall/Spring, 3 credit hours
This course provides the fundamentals of applied data science – R morning. It helps students understand and learn some concepts necessary to start and work as data scientists. It covers the definitions, and main concepts, of data science.
DATA 240 AI Fundamentals
Fall/Spring, 3 credit hours
This course introduces students to the foundational concepts of artificial intelligence (AI). It covers the history, basic principles, methodologies, and applications of AI. By the end of the course, students will have a solid understanding of AI's core concepts and its significance in today's technological landscape.
DATA 315 Data Mining and Machine Learning
Fall/Spring, 3 credit hours
This course provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This course focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this course explains the methods of knowing, preprocessing, processing, and warehousing data. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The course details the methods for data classification and introduces the concepts and methods for data clustering. Finally, it discusses the outlier detection and the trends, applications, and research frontiers in data mining.
DATA 320 Data Security and Privacy
Fall/Spring, 3 credit hours
The goal of the course is to familiarize the students with basic concepts of data security and privacy, their definitions, applications and current advances in research community and industry.
DATA 321 Big Data Fundamentals
Fall/Spring, 3 credit hours
Review the Big Data concepts, methods, and approaches and provide some examples of Big Data applications in Data science.
DATA 415 EThics in Data Science
Fall/Spring, 3 credit hours
This course discusses the ethical considerations on the collection, storage, use and analysis of data. This course helps students to examine the ethical and privacy aspects of collecting and managing data. Discovering the effect of the data science in the 21st century. The students are presented with discussions on the complications of data collection in the modern society and the principles of transparency, accountability and fairness as they understand the crucial aspect of having a shared set of ethical values. Students learn about best practices for responsible data management, using basic methods to preserve anonymity of the users when dealing with personal identifiable information.
Prerequisites: 45 completed credit hours
DATA 420 Advanced Data Mining and Machine Learning
Fall/Spring, 3 credit hours
This Course provides advanced topics in machine learning and data mining, including prediction, generating, and classification algorithms.
Prerequisites: DATA 315
DATA 421 Deep Learning Fundamentals
Fall/Spring, 3 credit hours
This course reviews the deep learning concepts, methods, and approaches and provides some examples of deep learning applications in prediction and classification.
Prerequisites: DATA 315
DATA 422 Advanced AI and ChatGPT
Fall/Spring, 3 credit hours
The Advanced AI and ChatGPT course offers a specialized exploration into the realm of advanced topics in artificial intelligence, with a keen focus on the ChatGPT models. Building upon foundational AI knowledge, this course dives deep into the architecture, training, and deployment of state-of-the-art chatbots. Participants will gain hands-on experience in fine-tuning conversational models, addressing challenges in chatbot design, and understanding the ethical implications of deploying such models. Upon completion, participants will be equipped with the skills to develop, refine, and implement advanced conversational AI solutions in various domains.
Prerequisites: DATA 240