DS 100. Basic Foundations of Data Science. 3 credits, 3 contact hours (3;0;0).

Prerequisites: No prerequisites are required to enroll. Data Science (DS) and artificial intelligence (AI) systems are increasingly being deployed in real-world applications across domains, significantly impacting our daily and social lives. It is critical to ensure that students have a good understanding of the new era of the DS/AI-human-centric world. That will lead to a broader adoption of DS/AI in real-world applications in practice. This course will provide an insightful understanding of recent data science (DS) and artificial intelligence (AI) developments. Students will learn basic program skills in Python and enable a venture through basic building blocks of data structures, data collection, processing, and generating. Using data visualization tools, students will learn to analyze real-world datasets across domain applications. Students will explore the ethics of AI abstractly and comprehensively, ranging from societal risks, regulations, and responsible technologies in AI. Hands-on labs are developed to align basic knowledge and practical skills in DS/AI. As a result, the course is designed to offer an appropriate entry point into the vibrant world of DS/AI for students with no background in computing so that students can diversify and strengthen their career paths by using DS/AI appropriately and optimally.

DS 240. General Introduction to Data Science. 3 credits, 3 contact hours (3;0;0).

Prerequisites: CS 100 or CS 101 or CS 103 or CS 104 or CS 106 or CS 113 or CS 115 or BME 210 or BNFO 135 with a grade C or better. Restrictions: This course is not for DS and CS majors, DS or CS students need to take DS 340 or CS 301 instead. This course provides a basic, yet comprehensive coverage of the fundamental principles and practical applications of data science and artificial intelligence (AI). This course, intended for all majors at NJIT, provides an introduction to Data Science with reduced coding. The course progresses to help students build a solid foundation for data processing, computing, and analysis. Topics include data manipulation, visualization, big data ecosystem, machine learning, deep learning, trustworthy AI, AI ethics, and cutting-edge advancements such as large language models and AI for sciences. Hands-on work involves Python with popular libraries including Pandas, NumPy, and PyTorch. This course is not for DS and CS majors.

DS 340. Fundamentals and Principles of Data Science. 3 credits, 3 contact hours (3;0;0).

Prerequisites: CS 114 and (MATH 333 or MATH 341) with a grade C or better. Fundamentals and principles of data science familiarize students with the theories and techniques for data representation, manipulation, analysis, visualization, and interpretation. Topics include introduction to data preparation and preprocessing, data mining, anomaly detection, machine learning, statistical learning, data analysis and visualization, large language models, ethics, and popular data science tools and systems. Hands-on work will include Python with Pandas coding.

DS 400. Scientific Foundation of Machine Learning. 3 credits, 3 contact hours (3;0;0).

Prerequisites: (CS 100 or DS 100) and (CS 375 or CS 370). This course provides an advanced exploration of machine learning, emphasizing its mathematical foundations and the interplay between statistical and computational aspects. Designed for students seeking to deepen their understanding or advance the theory and development of learning algorithms, the course primarily focuses on conceptual insights at the undergraduate level. Key topics include classical results in statistical and computational learning theory, recent advances in deep learning, unsupervised learning, and large language models (LLMs). Students will acquire tools to analyze and prove performance guarantees for learning methods, fostering a strong theoretical foundation and practical expertise in machine learning.

DS 410. Federated Machine Learning and Applications. 3 credits, 3 contact hours (3;0;0).

Prerequisites: CS 375. The increasing availability of data has greatly boosted the power of machine learning (ML). However, modern data generation (e.g., from personal devices, within hospitals) fundamentally changes ML pipelines. Unlike traditional pipelines that use centralized datasets collected from the web to train ML models, these new data modes result in heterogeneous siloed data residing in the devices or organizations that generated it. To make use of this decentralized data, we focus on collaborative and federated ML, which enables secure and trustworthy learning across multiple parties and diverse data sources.

DS 480. Fundamentals and Applications of Graph Neural Networks. 3 credits, 3 contact hours (3;0;0).

Prerequisites: (CS 100 or DS 100) and CS 375. Graphs provide a natural framework for representing complex relationships between various objects. Graph Neural Networks (GNNs) have gained significant importance in both academic research and industrial applications. This course introduces GNNs and explores foundational concepts, algorithms, and diverse applications. Students will learn the fundamentals of graph theory, and key models, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), advanced graph diffusion models, and integrations of GNNs with sequential models for temporal graph modeling. The course will cover practical applications across fields like social networks, biological networks, brain networks, and finance, focusing on hands-on implementation and problem-solving. By the end of the semester, students will be skilled in designing and applying GNN models to real-world datasets.

DS 485. Selected Topics in DS. 3 credits, 3 contact hours (3;0;0).

Restrictions: Junior standing and/or department approval. The study of new and/or advanced topics in an area of data science not regularly covered in any other DS course. The precise topics to be covered in the course, along with prerequisites, will be announced in the semester prior to the offering of the course. A student may register for no more than two semesters of Special Topics.

DS 488. Independent Study in Data Science. 3 credits, 3 contact hours (3;0;0).

Restrictions: Open only to Data Science majors who have the prior approval of the department and the DS faculty member who will guide the independent study. Independent studies, investigations, research, and reports on advanced topics in data science. Students must prepare, in collaboration with their faculty mentor and in the semester prior to enrolling in this course, a detailed plan of topics and expected accomplishments for their independent study. This must have the approval of both the department and the faculty mentor. A student may register for no more than one semester of Independent Study.

DS 492. Data Science Capstone I. 3 credits, 3 contact hours (3;0;0).

Restrictions: Senior standing. The Data Science (DS) Capstone Project spans two semesters and is intended to provide a real-world project-based learning experience for seniors in the BS DS program. The overall objectives of this course are to investigate the nature and techniques of a data-oriented computing development project. Projects are provided by faculty members or industry partners, or proposed by students who wish to become entrepreneurs. In DS Capstone I, teams of project participants will carry out market research, identify appropriate data science problems, collect and preprocess the needed data, define performance metrics, perform risk analysis, and finish an overall design of their solution that integrates various data analytics techniques. The course instructor will mentor and evaluate all projects in conjunction with an entrepreneurship board of industry, faculty, and alumni advisors.

DS 493. Data Science Capstone II. 3 credits, 3 contact hours (3;0;0).

Prerequisites: DS 492 with a grade C or better. The Data Science (DS) Capstone Project spans two semesters and is intended to provide a real-world project-based learning experience for seniors in the BS DS program. The overall objectives of this course are to investigate the nature and techniques of a data-oriented computing development project. Projects are provided by faculty members or industry partners, or proposed by students who wish to become entrepreneurs. In DS Capstone II, teams of project participants will refine their design, implement and integrate component techniques into a complete software solution, present data analysis results, evaluate the system performance, and validate the proposed solution. The course instructor will mentor and evaluate all projects in conjunction with an entrepreneurship board of industry, faculty, and alumni advisors.