30 courses with the subject DAMG, 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. May be repeated without limit.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
Introduces the fundamentals of machine learning and data science with Python for individuals who want to learn and apply state-of-the-art ML algorithms to real-world datasets using the Python programming language. Two unique and foundational pillars of this course are using real-world situational analogies to intuitively understand the algorithmic aspects of data science and ML and using real-world datasets and powerful machine learning libraries in Python to put these algorithms into practice. No prior programming experience required.
Presents insights from a data perspective for engineering and business applications; data modeling, relational algebra, including entity-relationship and object approaches; user-centric information requirements and data sharing; fundamental concepts of database management systems and their applications; alternative data models, emphasizing relational design; SQL; data normalization; data-driven application design for personal computer, server-based, enterprisewide, and internet databases; and SQL injection and distributed data applications.
Offers topics of current interest in data architecture and management. May be repeated up to three times for a maximum of 16 semester hours.
Offers theoretical or experimental work under the direction of members of the department on a chosen topic. Course content depends on instructor. May be repeated up to seven times for a maximum of 8 semester hours.
Studies the Python programming language with data science as the application domain. Offers students an opportunity to learn how to perform complex numerical calculations, fixed data types, space efficiency, and vector manipulations. Covers tools and techniques for manipulating tables, spreadsheets, and group and pivot tables involving extremely large data sets. Covers large multidimensional arrays and matrices and the high-level mathematical functions to operate on these arrays. Studies how to use Python to manipulate the classic math and science algorithms. Analyzes helper functions such as linear and nonlinear regression, integration, Fourier transformations, numerical optimization, etc. Includes higher-level classes for manipulating and visualizing data. Applies tools and techniques to classical data science using cases such as time series forecasting, social network analysis, text analytics, and big data processing.
Studies design of information systems from a data perspective for engineering and business applications; data modeling, including entity-relationship (E-R) and object approaches; user-centric information requirements and data sharing; fundamental concepts of database management systems (DBMS) and their applications; alternative data models, with emphasis on relational design; SQL; data normalization; data-driven application design for personal computer, server-based, enterprise-wide, and Internet databases; and distributed data applications.
Explores the evolving relationship between data and artificial intelligence, explicitly emphasizing generative AI with large language models in the evolving data platform architecture landscape. Examines the data life cycle implementation from business problem formulation to solution delivery by studying real-world case studies to grasp practical trade-offs, challenges, and limitations. Offers students an opportunity for dynamic discussions and critical analyses of data challenges in LLM application development with a specific impact on the enterprise data platform and the ever-evolving role of data engineers.
Examines the intersection of large language models and graph databases within the framework of knowledge graphs. Emphasizes their synergistic potential in modern data engineering and AI. Presents a foundational understanding of LLMs, graph theory, and graph database technologies, focusing on natural language processing, semantic search, and intelligent data systems. Covers graph modeling, graph analytics, and approaches to embedding unstructured and structured data in graph-based architectures. Incorporates current research, applied case studies, and project-based learning to support the design and implementation of advanced, graph-enabled AI solutions.
Offers topics of current interest in data architecture and management. May be repeated up to three times for a maximum of 16 semester hours.
Offers topics of current interest in AI metamodel systems. May be repeated up to three times for a maximum of 16 semester hours.
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
Prerequisite(s): INFO 5002 with a minimum grade of B or INFO 6105 with a minimum grade of B or DAMG 6105 with a minimum grade of B
Prerequisite(s): DAMG 6105 with a minimum grade of B or INFO 6105 with a minimum grade of B
Prerequisite(s): CSYE 6200 with a minimum grade of B or DAMG 6105 with a minimum grade of B or DAMG 6210 with a minimum grade of B or INFO 5100 with a minimum grade of B or INFO 6210 with a minimum grade of B
Prerequisite(s): DAMG 6210 with a minimum grade of B or INFO 6210 with a minimum grade of B
Prerequisite(s): DAMG 6210 with a minimum grade of B or DAMG 7275 with a minimum grade of B or INFO 6205 with a minimum grade of B or INFO 6210 with a minimum grade of B
Prerequisite(s): DAMG 6105 with a minimum grade of B ; (DAMG 6210 with a minimum grade of B or INFO 6210 with a minimum grade of B )
Introduces the fundamentals of IT audit and cybersecurity compliance by exploring the evolving role of cybersecurity in organizational compliance strategies. Emphasizes the audit principles, regulatory frameworks, and the integration of compliance into organizational operations. Uses real-world scenarios and case studies to navigate the complexities of audit preparation, manage compliance within regulatory frameworks, and respond to incidents from a compliance perspective. Offers a strong foundation for those looking to advance their careers in IT auditing, cybersecurity, or compliance management, including practical skills and insights for real-world application.
Presents the principles of data and technology that define systems and cybersecurity from a socio-technical perspective. Offers students an opportunity to gain insight into the importance of systems security within cybersecurity and the integral role that information systems analysts play in developing cybersecure systems that people use. Through hands-on dynamic learning, students explore foundational cybersecurity principles, security architecture, risk management, attacks and mitigation strategies using Kali Linux, cyber incident response, and emerging IT/IS technologies.
Prerequisite(s): DAMG 6210 with a minimum grade of B or INFO 6210 with a minimum grade of B
Offers topics of current interest in data architecture and management. May be repeated without limit.
Prerequisite(s): DAMG 7370 with a minimum grade of B-
Delves deeply into advanced concepts and methodologies within data architecture and management. Emphasizes the development of critical thinking skills and the practical application of theoretical knowledge to real-world problems in the field. Through individual efforts, including laboratory work and/or literature review, students thoroughly explore and analyze various aspects of data architecture and management, focusing on a project selected in consultation with their faculty advisor that allows for tailored exploration and research. Students compile a comprehensive report detailing their findings, methodologies, and conclusions. Offers students an opportunity to present their discoveries to peers and faculty, fostering scholarly discussion and collaboration.
Offers theoretical or experimental work under the direction of members of the department on a chosen topic. Course content depends on instructor. May be repeated up to seven times for a maximum of 8 semester hours.
Offers students an opportunity to conduct full-time research under faculty supervision.
Prerequisite(s): DAMG 7945 with a minimum grade of C-
Source: Northeastern University's catalog, linked per course · table learning_unit · CourseShelf publish 61
