Data Science
Chair: James Geller
The Department of Data Science is the newest addition to the Ying Wu College of Computing. Founded in 2021, it offers a B.S. Degree in Data Science. This is a new degree program that responds to a strong demand from employers for trained Data Scientists. Data is revolutionizing most industries and B.S. graduates in Data Science command high starting salaries.
Data Science combines powerful methods from Computer Science, Statistics, Artificial Intelligence and Machine Learning into a unique new blend of techniques for deriving valuable insights from Big Data. Data Science is an ideal choice for students who are interested in applying data processing methods to ever larger and more varied real-world data sets, including image, video, natural language and speech data that go substantially beyond traditional text and table data to solve real-world problems. The Department of Data Science closely collaborates with the Department of Mathematical Sciences and the Department of Computer Science and students can take advantage of many computer science and mathematical sciences offerings. The Department of Data Science offers its own two-semester capstone projects that are executed with industrial sponsors. Students also can get involved in state-of-the-art research projects at the NJIT Institute for Data Science, where top notch scientists work with users to develop data-driven technologies to innovate the way the world works and lives.
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Bader, David, Distinguished Professor
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Dasgupta, Aritra, Assistant Professor
Du, Mengnan, Assistant Professor
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Gaikwad, Nikita, Lecturer
Geller, James, Professor
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Islam, Akm, University Lecturer
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Li, Daming, Senior University Lecturer
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Monogioudis, Pantelis, Professor of Practice
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Pethkar, Kaustubh Lecturer
Phan, Hai, Assistant Professor
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Renda, Michael, Professor of Practice
Roshan, Usman, Associate Professor
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Wang, Lijing, Assistant Professor
Wu, Chase, Professor
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Xu, Mengjia
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Yusuf, Fatima, University Lecturer
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Zhang, Shuai, Assistant Professor
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 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.
