15 courses with the subject DADS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
Offers elective credit for courses taken at other academic institutions.
Offers elective credit for courses taken at other academic institutions.
Offers elective credit for courses taken at other academic institutions.
Offers elective credit for courses taken at other academic institutions.
Prerequisite(s): (IE 3412 with a minimum grade of C- or IE 4515 with a minimum grade of C- or MATH 3081 with a minimum grade of C- ) or graduate program admission
Examines how generative AI and autonomous agentic AI systems leverage large language models to extract insights; generate solutions; and tackle complex, multifaceted problems. Emphasizes hands-on exploration of AI-driven approaches and application development across diverse domains including healthcare, supply chain optimization, financial decision making, and strategic planning. Offers students opportunities to design, deploy, and apply gen AI-powered analytical tools and applications alongside goal-oriented agentic AI systems. Addresses prompt engineering, model fine-tuning, retrieval-augmented generation, gen AI ethics, and emerging industry trends.
Introduces the theory, computation, and application of deterministic models to represent industrial operations. Includes linear programming formulation and solution using spreadsheet and algebraic languages software; simplex, big-M, two-phase, revised simplex, and dual simplex algorithms for solving linear programs; introduction to the theory of simplex, fundamental insight, duality, and sensitivity analysis; transportation, assignment, and transshipment problems; shortest path, minimum spanning tree, maximum flow, minimum cost network flow problems and project networks; and discrete-state and continuous-state dynamic programming models and applications. Requires knowledge of linear algebra.
Introduces fundamental concepts and methods in data analytics engineering, with a focus on probability, eigenvalues and eigenvectors, cluster analysis, text mining, and time series analysis. Covers modern data structures and computational techniques for data cleaning and wrangling. Prepares students for advanced coursework in data analytics.
Explores visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, and manufacturing. Covers Python and R for data mining and visualization. Examines static and interactive visualization methods that reveal patterns, interactions, and comparisons, with a focus on color encoding, shape selection, spatial layout, and annotation.
Covers the theory and applications of database management to support data analytics, data mining, machine learning, and artificial intelligence. Discusses the fundamental concepts and emerging technologies in database design and modeling, database systems, data storage, and the evolving world of data warehousing and data governance. Presents a balanced theory-practice focus. Covers relational databases, NoSQL databases, data integration, data quality, data governance, Big Data, and data processing for analytics.
Offers elective credit for courses taken at other academic institutions.
Prerequisite(s): DADS 5200 with a minimum grade of C or DADS 6400 with a minimum grade of C or DS 5020 with a minimum grade of C or IE 5374 with a minimum grade of D or IE 5374 with a minimum grade of C (Graduate) or IE 6200 with a minimum grade of C or IE 6400 with a minimum grade of C or MATH 7241 with a minimum grade of C
Prerequisite(s): CS 5100 with a minimum grade of C or CS 6120 with a minimum grade of C or CS 6140 with a minimum grade of C or CS 6180 with a minimum grade of C or CS 6200 with a minimum grade of C or CS 6220 with a minimum grade of C or CS 7140 with a minimum grade of C or CS 7150 with a minimum grade of C or DADS 7275 with a minimum grade of C or DADS 7650 with a minimum grade of C or IE 7275 with a minimum grade of C or IE 7300 with a minimum grade of C or IE 7615 with a minimum grade of C
Offers an in-depth exploration of natural language processing techniques, their underlying principles, and their interdisciplinary applications across domains such as engineering, business, healthcare, and the social sciences. Emphasizes the practical implementation of NLP methods—including word embeddings, neural networks, attention mechanisms, and advanced deep learning models—to analyze and solve real-world problems involving unstructured text data.
Prerequisite(s): DADS 7275 with a minimum grade of C+
Source: Northeastern University's catalog, linked per course · table learning_unit · CourseShelf publish 61
