59 courses with the subject ALY, 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.
Prerequisite(s): MTH 1100 with a minimum grade of D- or MTH 1200 with a minimum grade of D- or MTH 2100 with a minimum grade of D-
Prerequisite(s): (MTH 2400 with a minimum grade of D- or PHL 2310 with a minimum grade of D- ); ITC 2300 with a minimum grade of D-
Presents the evolving landscape of generative AI, exploring foundational concepts, key technologies, and the mysteries inside AI's black box. Provides insights into cutting-edge models, explores platforms like ChatGPT and Claude, and studies the craft of prompt engineering to refine AI-generated results. Explores the ethical challenges of AI, addressing bias, transparency, and limitations. Offers professionals across analytics, information technology, engineering, biology, healthcare, management, business, and the humanities an opportunity to study powerful tools to make informed decisions and create impactful deliverables in an AI-driven future.
Discusses contemporary topics in analytics for a rotating variety of industries (nonprofit and for profit). Employs a mix of lectures, cases, and projects. Instructor determines the topics. May be repeated three times for a maximum of 16 semester hours.
Offers elective credit for courses taken at other academic institutions.
Prerequisite(s): ALY 2010 with a minimum grade of D-
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D-
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D-
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D- Attribute(s): NUpath Analyzing/Using Data
Introduces artificial intelligence, serving as an excellent starting point for exploring various aspects of AI or understanding its broader societal implications. Designed to help students from all academic backgrounds understand AI's principles and applications. Examines core concepts while exploring the field's true potential and limitations. Covers various AI paradigms, including machine learning, natural language processing, computer vision, and robotics, without requiring prior programming experience. Emphasizes AI's societal impact, discussing its ethical implications, potential benefits, and challenges across different sectors. Employs hands-on projects, interactive sessions, and real-world case studies to offer a critical perspective on AI's evolving role in shaping our future.
Discusses contemporary topics in analytics for a rotating variety of industries (nonprofit and for profit). Employs a mix of lectures, cases, and projects. Instructor determines the topics. May be repeated three times for a maximum of 16 semester hours.
Offers elective credit for courses taken at other academic institutions.
Prerequisite(s): ALY 2010 with a minimum grade of D- ; ITC 2300 with a minimum grade of D-
Prerequisite(s): (ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D- ) or ALY 3040 with a minimum grade of D-
Prerequisite(s): ALY 4020 with a minimum grade of B
Prerequisite(s): ALY 2100 with a minimum grade of D-
Prerequisite(s): ALY 3040 with a minimum grade of D- ; ALY 3070 with a minimum grade of D- ; ALY 4000 with a minimum grade of D- Attribute(s): NUpath Capstone Experience, NUpath Writing Intensive
Focuses on an in-depth project where the student conducts research or creates a product related to their major field. May be repeated twice for a maximum of 12 semester hours.
Discusses contemporary topics in analytics for a rotating variety of industries (nonprofit and for profit). Employs a mix of lectures, cases, and projects. Instructor determines the topics. May be repeated three times for a maximum of 16 semester hours.
Offers elective credit for courses taken at other academic institutions.
or ALY 6070 Communication and Visualization for Data Analytics
Complete one of the following: 3
Offers an overview of analytics concepts and practices across a diverse range of industries and organizational contexts. Analyzes real-world datasets using the advanced scripting, programming languages, and supporting libraries. Utilizes one of several predominant data analysis languages to explore datasets and analyze the relationships between their features. Provides a foundation in applied statistics, the mathematical language of data analysis, and begins to build the requisite mathematical acumen that serves as the foundational pillar of future study.
APPLIED MACHINE INTELLIGENCE CONCENTRATION Code Title Hours
Prerequisite(s): ALY 6105 (may be taken concurrently) with a minimum grade of C- or ALY 6000 with a minimum grade of C-
Prerequisite(s): ALY 6115 with a minimum grade of C- or ALY 6010 with a minimum grade of C-
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6010 with a minimum grade of C- )
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6010 with a minimum grade of C- )
ALY 6160
Prerequisite(s): ALY 6125 with a minimum grade of C- or ALY 6015 with a minimum grade of C- ; ALY 6050 with a minimum grade of C- ; ALY 6070 with a minimum grade of C-
Prerequisite(s): ALY 6125 with a minimum grade of C- or (ALY 6000 (may be taken concurrently) with a minimum grade of C- ; ALY 6010 with a minimum grade of C- ) or (EAI 6000 with a minimum grade of C- ; EAI 6010 with a minimum grade of C- )
Introduces the fundamentals of relational databases, different types of data, and how to query them. Offers instruction in writing complex queries that pull, join, filter, and summarize complex data from multiple sources. Analyzes cleaning, transforming, and aggregating data, and touches on advanced topics such as window functions and complex data types. Assesses the efficiency of queries and emphasizes the importance of minimizing runtime. Demonstrates how to integrate database access with common analytical programming languages. Applies these techniques to real-world case studies similar to those offered in case interview scenarios.
Prerequisite(s): ALY 6125 with a minimum grade of C-
Prerequisite(s): ALY 6125 with a minimum grade of C-
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6015 with a minimum grade of C- )
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6010 with a minimum grade of C- ) or (EAI 6000 with a minimum grade of C- ; EAI 6010 with a minimum grade of C- )
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6010 with a minimum grade of C- ) or (EAI 6000 with a minimum grade of C- ; EAI 6010 with a minimum grade of C- )
Studies statistical programming. Explores a selection of analytics systems technologies used for different purposes to describe data numerically and graphically, including data visualization, file systems for a large data mart, applications of structured query language, and filtering and transforming data through scripting languages. Some tools are taught in greater detail (e.g., programming languages, libraries for statistical programming, machine learning), whereas others are introduced more broadly.
Discusses the operationalization of analytics as well as the business partner concept. Introduces a variety of business analytical concepts and information technologies, focusing on the application of data, information, and insight to support decision making. Explores business intelligence technology and its applications, in addition to discussing project management and ethical questions related to the application of data analytics.
Covers analytical leadership principles for the structure and dynamics of organizations. Explores the process of leading an analytics project to drive effective change in business operations and guide decision making. Discusses topics such as the creation of an analysis-driven culture, data integration, investment/budgets and technology, and how to achieve stakeholder alignment and collaboration from a leadership perspective. Applies the analytics project life cycle as an effective tool to plan, implement, and evaluate the results of analytics projects. Investigates the philosophy that analytics leaders need to preserve a "healthy distance" from the data to be able to keep the broader picture and business question in mind, as well as the idea that data cannot explain everything and is just one element in decision making.
Prerequisite(s): (ALY 6015 with a minimum grade of C- or ALY 6125 with a minimum grade of C- ); (ALY 6050 with a minimum grade of C- or ALY 6135 with a minimum grade of C- ); (ALY 6070 with a minimum grade of C- or ALY 6145 with a minimum grade of C- )
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6000 with a minimum grade of C- ; ALY 6010 with a minimum grade of C- )
Focuses on an in-depth project in which a student conducts research or produces a product related to the student’s major field. May be repeated four times for a maximum of 20 semester hours.
Examines advanced leadership frameworks for driving enterprisewide analytical transformation and innovation. Explores competencies in strategic visioning, organizational change management, and the creation of analytics-driven cultures at scale. Offers students an opportunity to design and evaluate comprehensive data governance ecosystems, lead cross-functional transformation initiatives, and develop original frameworks for sustainable competitive advantage through analytics. Emphasizes the executive leader's role in balancing data-driven insights with strategic intuition, stakeholder dynamics, and ethical considerations in complex organizational environments.
Explores the strategic design and leadership of enterprisewide decision intelligence systems that integrate artificial intelligence, business intelligence, and human judgment for competitive advantage. Offers students an opportunity to develop competencies in architecting comprehensive decision support ecosystems, leading digital transformation initiatives, and creating innovative approaches to automated and augmented decision making. Emphasizes the executive perspective on balancing technological capabilities with organizational readiness, regulatory compliance, and stakeholder trust.
Explores advanced competencies in enterprisewide risk analytics strategy, governance frameworks, and board-level risk communication. Examines the executive leadership challenges of managing algorithmic risk, regulatory compliance, and organizational risk culture in data-driven environments. Emphasizes the strategic design of comprehensive risk management ecosystems that balance innovation with protection including AI governance, cybersecurity strategy, and operational risk management. Offers students an opportunity to conduct original research that contributes to risk management theory and to develop innovative frameworks for risk analytics in complex organizational contexts.
Offers nontechnical executives leading data-driven organizations an opportunity to advance their competencies in technology strategy, digital innovation management, and technical team leadership. Students develop frameworks for evaluating emerging technologies, managing technology portfolios, and leading digital transformation initiatives. Emphasizes strategic decision making around technology architecture, vendor selection, and innovation investment while building organizational technical capabilities. 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Source: Northeastern University's catalog, linked per course · table learning_unit · CourseShelf publish 61
