ALY 1990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions. May be repeated without limit.
ALY 2010. Probability Theory and Introductory Statistics. (3 Hours)
Introduces statistics for data analytics from an analysis-of-data viewpoint. Topics include frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; categorical data; regression and correlation; and analysis of variance. Explores the use of statistical software in data analysis. Emphasizes hands-on application of probability and statistics in SPSS.
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-
ALY 2100. Introduction to Programming for Data Analytics. (3 Hours)
Offers a hands-on first programming course for those with no prior programming experience. Covers basic programming logic and syntax with Python. Students apply Python packages mostly used on data analytics. Offers students an opportunity to learn how to code on the most used language in the job market.
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-
ALY 2550. Generative AI. (3 Hours)
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.
ALY 2983. Topics. (1-4 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.
ALY 2990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions.
ALY 3015. Intermediate Statistics for Data Analytics. (3 Hours)
Expands upon the earlier introduced statistical approaches. Emphasizes more advanced analysis and multivariate methods. The goal is to provide students with the fundamental data management, review, reengineering, and exploration skills as necessary data analytical competencies.
Prerequisite(s): ALY 2010 with a minimum grade of D-
ALY 3040. Data Mining. (3 Hours)
Introduces the theories and tools for data mining techniques such as rule-based learning, decision trees, clustering, and association-rule mining. Also covers interpretation of the mined patterns using visualization techniques. Offers students an opportunity to gain the knowledge and experience to apply modern data-mining techniques for effective large-scale data pattern recognition and insight discovery. Introduces data analysis software—student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D-
ALY 3070. Communication and Visualization for Data Analytics. (3 Hours)
Offers an interdisciplinary examination of design concepts and cognitive and communication theories that support effective practices for data visualization and communication. Considers the relationship between information and audience and studies effective techniques in the written, spoken, and visual communication of complex quantitative information. Project-based activities offer students opportunities to apply these techniques in a manner that makes data understandable, compelling, and actionable. Introduces R and Python visualization packages.
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D-
ALY 3110. Big Data and Web Mining. (3 Hours)
Offers students an opportunity to work with very large data sets and to learn how to write code to search the World Wide Web for publicly available data in a methodical and automated manner.
Prerequisite(s): ALY 2100 with a minimum grade of D- ; ALY 3015 with a minimum grade of D-
Attribute(s): NUpath Analyzing/Using Data
ALY 3510. AI Foundations: An Interdisciplinary Approach. (3 Hours)
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.
ALY 3983. Topics. (1-4 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.
ALY 3990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions.
ALY 4000. Analytics Using R. (3 Hours)
Offers an overview of analytics concepts and practices across a diverse range of organizational contexts. Introduces data structure and management using R and SQL R packages. Leverages big data using Hadoop, Spark, and R scripting. Engages students in discussions on analytics careers and their ethical considerations. Introduces the basics of business strategies for big data analytics through a final project.
Prerequisite(s): ALY 2010 with a minimum grade of D- ; ITC 2300 with a minimum grade of D-
ALY 4020. Predictive Analytics Using R and Python. (3 Hours)
Introduces the end-to-end data-driven predictive modeling approach in R, Python, KNIME and WEKA with applications and case studies. Includes all the data and modeling steps in a full modeling cycle (training, validation and testing), exploratory data analysis and data cleansing, commonly applied modeling techniques such as SVM, random forest and ensemble models; introduces neural networks using TensorFlow.
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-
ALY 4520. MLOps: Operationalizing AI. (3 Hours)
Examines the machine learning operations, or MLOps, life cycle for designing, deploying, and managing scalable machine learning systems. Surveys emerging trends, such as serverless computing and edge deployments, to illuminate future directions in MLOps. Highlights foundational methods for data ingestion, pipeline orchestration, and performance monitoring while addressing ethical and security considerations. Emphasizes cross-functional collaboration between data science and engineering for production-ready solutions that prioritize reliability and reproducibility. Demonstrates how automation strategies and best practices accelerate feedback loops and streamline model updates. Encourages a practice-based approach that integrates experimental results with continuous delivery to align with organizational goals.
Prerequisite(s): ALY 4020 with a minimum grade of B
ALY 4570. Social Impacts and Issues of AI. (3 Hours)
Presents a comprehensive overview of AI's fundamental concepts, techniques, and applications. Addresses core components—such as problem-solving and search algorithms, knowledge representation and reasoning, machine learning principles and algorithms, computer vision, and natural language processing—while highlighting practical considerations, ethical issues, and the societal implications of AI. Reinforces understanding and aims to develop practical skills through hands-on programming assignments and projects in Python. Offers students an opportunity to establish a solid foundation for mastering AI principles and techniques, preparing them for further study or careers in the field.
Prerequisite(s): ALY 2100 with a minimum grade of D-
ALY 4850. Analytics Capstone. (3 Hours)
Offers an advanced practicum in the development and delivery of data analysis for strategic decision making in organizations. Students apply the principles and tools of analytics to a comprehensive real-world problem or project within a sponsoring organization. Expects students to present analytical insights and recommendations for successful implementation of their capstone project.
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
ALY 4955. Project. (1-4 Hours)
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.
ALY 4983. Topics. (1-4 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.
ALY 4990. Elective. (1-4 Hours)
Offers elective credit for courses taken at other academic institutions.
ALY 6105. Introduction to Applied Statistics. (3 Hours)
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.
ALY 6115. Foundations of Data Analytics. (3 Hours)
Introduces statistics for business analytics from an analysis-of-data viewpoint. Topics include frequency distributions; measures of location (mean, median, mode); measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; categorical data; regression; correlation; and analysis of variance. Explores the use of statistical and data analysis software. Lab sessions emphasize hands-on application of probability and statistics and data problem solving using advanced analytic tools.
Prerequisite(s): ALY 6105 (may be taken concurrently) with a minimum grade of C- or ALY 6000 with a minimum grade of C-
ALY 6125. Intermediate Analytics. (3 Hours)
Explores an end-to-end, data-driven, statistical, and predictive modeling approach with applications and case studies for predictive analytics. Applies all the data and modeling steps in a full modeling cycle, including exploratory data analysis and data cleansing for outlier imputation, data normalization, and commonly applied modeling techniques such as regression models, regularization techniques and model training, validation, testing, and selection.
Prerequisite(s): ALY 6115 with a minimum grade of C- or (ALY 6015 with a minimum grade of C- ; ALY 6070 with a minimum grade of C- )
ALY 6135. Introduction to Enterprise Analytics. (3 Hours)
Presents an overview of analytics concepts and practices across a diverse range of industries and organizational contexts. Case studies of successful analytics initiatives from fields including retail, government, education, and the arts examine how the collection and analysis of data impacts decision making within a variety of contexts. Offers students an opportunity to engage with the current theories, practices, and debates in the field of analytics to critically examine its practice. Distinguishes between specific analytical techniques and tools for fundamental data analysis methods, providing context essential to preparing students to engage more deeply with the individual courses that follow.
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 6145. Communication and Visualization with Data. (3 Hours)
Presents a hands-on overview of principles needed to effectively design and create data visualizations, transforming raw data into actionable insights. Explores key informational design concepts, emphasizing the relationship between information and audience in the context of communicating complex quantitative information. Offers students an opportunity to use a variety of tools and techniques to conduct exploratory data visualization, design dashboards and scorecards, and present findings to hypothetical stakeholders. Evaluates ethical questions and biases related to the communication and visualization of data. Investigates techniques used to represent geospatial data as well as network and graph techniques.
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 6400. Integrated Experiential Learning. (3 Hours)
Offers students an opportunity to implement the development and delivery of data analysis for strategic decision making in organizations. Students apply the principles and tools of analytics to a comprehensive real-world problem or project within a sponsoring organization. Designed to prepare students for the successful implementation of their capstone project.
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-
ALY 6410. Healthcare Analytics: From Data to Decisions. (2 Hours)
Offers a comprehensive introduction to healthcare analytics, including understanding the healthcare system and its data sources to master different types of analytics—descriptive, predictive, and prescriptive. Analytics is essential for advancing healthcare systems, involving quantitative methods to generate insights, support decision making, and enhance the quality of care. Essential topics include clinical analytics, population health management, quality improvement, research and development, and operational analytics. Applies theoretical concepts to real-world challenges through a case study approach, tackling problems like predicting hospital readmissions, optimizing staffing levels, and improving surgery scheduling. Investigates various data sources and the roles of governance entities in generating and safeguarding these data.
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- )
ALY 6420. Introduction to Queries. (2 Hours)
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.
ALY 6430. Machine Learning Operations. (2 Hours)
Examines machine learning operations—the collection of methods, strategies, organizational culture, and thought processes that guarantee ML systems' dependable and scalable implementation. Navigates the complex process of deploying models, highlighting the crucial differences between software engineering and ML. Explores the interconnected data collection, storage, transformation, and feature engineering processes that define the ML workflow. ML operates on statistical methods utilizing often messy real-world data, leading to changeable behavior in ML systems and calling for continuous observation and analysis. Addresses challenges in reproducibility and version control, emphasizing the need for a robust approach to managing different versions of models and datasets. Comprehensively analyzes the tools, practices, strategies, and mindset essential to ensuring the reliable and scalable implementation of machine learning systems.
Prerequisite(s): ALY 6125 with a minimum grade of C-
ALY 6440. Text Analytics and Language Processing. (2 Hours)
Examines how to analyze, interpret, and generate human language using cutting-edge models and techniques. Studies text analytics and language processing in-depth. Explores foundational concepts, such as tokenization and bag-of-words representation; delves into modern language models and techniques, such as LLMs and BERT; and covers application building. Explores corporate contextual implementations and ethical considerations in natural language processing. Emphasizes analytical skills, model development, application development, and ethical understanding.Offers students an opportunity to engage in hands-on labs and assignments.
Prerequisite(s): ALY 6125 with a minimum grade of C-
ALY 6600. Data Warehousing and Querying. (3 Hours)
Introduces principles, tools, and methods in data analysis and data management focusing on storing and retrieving data. Explores how a querying tool can solve data analysis problems and increase efficiency of data manipulation using large databases within a data warehouse to create business intelligence. With the explosion of data, computing power, and cloud data warehouses, business intelligence professionals and data scientists need to be equipped with effective tools to deal with the increasing complexity of data storage and querying.
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- )
ALY 6610. Data Mining Applications. (3 Hours)
Introduces the theories, concepts, and methods for intensive data analysis methods and data mining techniques, such as supervised and unsupervised learning, classification methods, decision trees, clustering, association rule mining, text mining, and natural language processing. Investigates Exploratory Data Analysis, the interpretation of hidden patterns in data using statistical and visualization techniques. Offers students an opportunity to apply modern data mining techniques for effective large-scale data pattern recognition and insight discovery. Student teams evaluate, analyze, and report data for the methods used and insights discovered during case studies.
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- )
ALY 6620. Big Data and Cloud Computing. (3 Hours)
Explores advanced tools for interacting with large-scale data and the challenges and benefits Big Data provides. Introduces tools and methods of analytics for extracting, transforming, and loading to facilitate analysis. Examines questions of ethics, governance, privacy, and security of data stored in elastic clouds and analyzed on distributed clusters. Offers students an opportunity to apply theoretical concepts to create data solutions supported by large-scale data collection and management. Introduces concepts of distributed file systems, large-scale data streaming, and techniques of analysis in large-scale data systems.
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- )
ALY 6630. Programming for Data Science. (3 Hours)
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.
ALY 6700. Decision Support and Business Intelligence. (3 Hours)
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.
ALY 6710. Leadership in Analytics. (3 Hours)
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.
ALY 6980. Capstone. (3 Hours)
Presents an advanced practicum in the development and delivery of data analysis for strategic decision making in organizations. Offers students an opportunity to apply the principles and tools of analytics to a comprehensive real-world problem or project within a sponsoring organization. Guides students to present analytical insights and recommendations in preparation for implementation of their capstone project.
Prerequisite(s): (ALY 6125 with a minimum grade of C- ; ALY 6135 with a minimum grade of C- ; ALY 6145 with a minimum grade of C- )
ALY 6983. Topics. (1-4 Hours)
Discusses contemporary topics in analytics for a rotating variety of industries (nonprofit and for-profit). May be repeated three times for a maximum of 16 semester hours.
Prerequisite(s): ALY 6115 with a minimum grade of C-
ALY 6995. Project. (1-4 Hours)
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.
ALY 7100. Strategic Analytics Leadership and Organizational Transformation. (3 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.
ALY 7110. Enterprise Decision Intelligence and Strategic Business Systems. (3 Hours)
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.
ALY 7120. Enterprise Risk Analytics and Governance Strategy. (3 Hours)
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.
ALY 7130. Technology Strategy and Digital Innovation Leadership. (3 Hours)
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. Explores research on technology adoption patterns, innovation management, and digital transformation effectiveness.