• Python — This module provides an introduction to Python programming with a focus on building a strong foundation for statistical computing. Students will review core concepts including data types, control structures, functions and basic data manipulation. The module serves as the preparatory base for the Statistics module by developing essential programming skills for data analysis.
• R — This module introduces programming in R for data analysis, with an emphasis on core functionality in base R and the use of the dplyr package for data manipulation. Students will learn to import, clean, transform and summarize data. The module highlights writing clear code and understanding how different R tools support analytical tasks. It provides a foundation for subsequent modules in statistics, data mining, and machine learning.
• SQL — This module introduces the fundamentals of the SQL SELECT statement through hands-on practice using Python to query data. Emphasis is placed on understanding how SQL supports data exploration and analysis in real-world environments. The module serves as a quick introduction and preparation for the full SQL module in the fall semester.
• Technical Writing — This module is part 1 of 3 of the integrated ethical data storytelling curriculum (technical writing, technical presenting, and ethics) that provides students with a foundational understanding of ethical communication and professional presentation, including the responsible and effective use of generative AI, focusing on the creation of audience-centered documents and presentations that prioritize critical and creative thinking. The Technical Writing module emphasizes the creation of audience-centered documents that are clear, concise and persuasive for professional audiences. Instruction focuses on writing, critiquing and analyzing documents to develop effective feedback, editing and proofreading strategies.
• Technical Presentation — This module is part 1 of 3 of the integrated ethical data storytelling curriculum. By the end of the module, students will apply these ethical storytelling practices directly to their initial presentation and their summer practicum final presentation. The Technical Presentation module guides students to apply principles of critical and creative thinking to structure persuasive content that engages, motivates and connects with audiences. The curriculum incorporates strategies and practice for managing speaking anxiety, to help students project confidence and master verbal and nonverbal skills. Students learn to utilize visuals effectively and ethically, respond professionally to audience questions and collaborate with peers to deliver team presentations tailored to the needs of their practicum sponsors.
• Project and Team Dynamics — Students are given an open-ended analytics focused project with a very general supporting lecture content to support with a shared experiential base which in-turn will support a more structured lecture set in the fall. Lecture content includes subject matter and communications frameworks relating to team and project dynamics supporting project execution and critical, professional reflection upon team and project experiences. A post-project debrief is conducted to support application of these frameworks.

Statistics — This module provides a comprehensive foundation in statistical analysis and regression modeling. Students begin with core statistical concepts, including probability distributions, confidence intervals, hypothesis testing and both descriptive and inferential statistics, before progressing to two-sample t-tests and one- and two-way ANOVA with interactions and post-hoc comparisons. The module develops linear regression from simple correlation and scatterplots to multiple regression, emphasizing assumptions, diagnostics, multicollinearity and influential observations. Extensions address common regression challenges such as heteroscedasticity, polynomial models, weighted least squares, correlated errors and model misspecification, along with modern model-building approaches including automated selection and penalized regression. The module concludes with categorical data analysis, including chi-squared tests of association and an introduction to logistic regression.

• Linear Algebra – This module introduces linear algebra and some of the analytics methods most reliant on ideas from linear algebra. Students will learn how to work with vectors and matrices of data with emphasis on the use of software to do so. The role of linear algebra in analytics contexts will be emphasized throughout. This module will explore principal components analysis, factor analysis, principal components regression, variable clustering, singular value decomposition and dimension reduction.
• Logistic Regression – This module introduces logistic regression with a focus on understanding how it differs from linear regression and when each approach is appropriate. The course emphasizes building and interpreting binary logistic regression models using statistical software, including understanding odds ratios and making predictions. Participants will also evaluate model performance using measures such as concordance, ROC curves, sensitivity, and specificity. Finally, the course explores strategies for developing candidate models, highlighting the role of subject-matter expertise and the advantages and limitations of automated variable selection methods.
• Time Series 1 – This module provides a comprehensive introduction to time series analysis and forecasting. Students will learn how to decompose a time series into its core components — trend, seasonality, and remainder — and understand the distinction between stationary and non-stationary processes. The module emphasizes interpretation of autocorrelation (ACF) and partial autocorrelation (PACF) functions to guide model identification. Participants will build and compare major classes of forecasting models, including Exponential Smoothing and ARIMA, using statistical software. The module also covers both manual model identification and automatic ARIMA selection techniques for practical forecasting applications.
• Time Series 2 – This module extends time series modeling to more advanced forecasting techniques and seasonal data structures. Students will distinguish between seasonal and non-seasonal data and build both deterministic and stochastic seasonal ARIMA models. The module introduces methods for incorporating external regressors into ARIMA models and provides a detailed understanding of the components of Facebook’s Prophet model. Participants will also explore neural network time series models, including the addition of autoregressive terms. Emphasis is placed on model diagnostics, evaluating forecast accuracy and combining multiple forecasting approaches through model ensembling for improved performance.
• Machine Learning – This module introduces machine learning methods for predictive modeling in complex regression and classification problems beyond traditional linear regression and logistic regression. Methods will include general additive models, splines, decision trees, random forests, boosting, neural networks, penalized linear regression, naive Bayes, linear discriminant analysis, quadratic discriminant analysis and support vector machines. Use of software to apply these models to data will be emphasized with special attention to model evaluation and interpretation.
• NLP/Text Analytics – This module introduces natural language processing and text analytics as the collection of methods useful for working with text data. Students will learn the foundations of wrangling, exploring and visualizing text-based features in a dataset. Beyond exploration, students will be able to classify text, analyze sentiment, model topics and cluster in the context of text data.
• Data Mining – This module introduces the fundamental concepts and techniques of data mining for discovering patterns and knowledge from datasets. Students will explore association analysis, clustering methods, decision trees, variable clustering and k-nearest neighbors (k-NN). Emphasis is placed on practical implementation, interpretation of results and selecting appropriate methods for different data scenarios. This module serves as the foundation for the subsequent Machine Learning module.
• GitLab/GitHub – This short module introduces students to version control and collaborative workflows using GitHub and GitLab. Students will learn how to manage repositories, track changes with Git, work with branches and merge requests and follow best practices for team-based development. The module provides essential tools and collaboration skills that directly support the practicum project.
• Introduction to AWS – This module introduces students to the fundamentals of cloud computing through hands-on experience in the AWS Sandbox environment. Students will learn core cloud concepts while working with essential AWS services, including Amazon S3 for cloud storage and Amazon EC2 for provisioning and managing virtual machines. Through practical exercises, students will gain experience creating, configuring and managing cloud resources, providing a strong foundation for understanding cloud infrastructure and preparing for more advanced AWS and big data applications.
• SQL – This module provides a comprehensive introduction to SQL for querying, managing and analyzing relational databases. Students will learn core topics including SELECT statement fundamentals, SQL joins, subqueries, set operators, views and window functions using both SQLite and PostgreSQL.
• Technical Interview Prep – This module prepares students for technical data-focused interviews through structured practice in SQL and Python problem solving. Students will work through common interview-style questions that emphasize data manipulation and clear communication of solutions. By the end of the module, students will have strengthened their problem-solving confidence and built a toolkit for success in technical interviews.
• Visualization – This module introduces students to the principles and best practices of data visualization using Tableau. Students will learn how to select the most effective charts and visualizations for different types of data, apply visualization design principles and build interactive dashboards that clearly communicate insights. Through a team-based, hands-on project using real-world data, students will clean and prepare datasets, develop meaningful visualizations and design a decision-support dashboard that is presented to stakeholders to inform data-driven decision making.

• Communication Week – Communication Week is five days of intensive full-day workshops designed to support presentation skills including giving and receiving feedback. Students develop their own message map and set realistic goals for improving their presentation skills. Two individual presentations, one individual meeting, one team meeting and one team presentation are completed during Communication Week along with exercises designed to support individual students’ needs. Communication Week is a safe space to practice and take risks with communication skills in order to prepare for high-stakes presentations in the fall. 
• Ethics – This module is part 2 of 3 of the integrated ethical data storytelling curriculum (technical writing, technical presenting, and ethics) that provides students with a foundational understanding of ethical communication, including the responsible and effective use of generative AI, focusing on the creation of audience-centered documents and presentations that prioritize critical and creative thinking. By the end of the module, students will apply these ethical storytelling practices to create an ethical framework customized for their practicum project. Students will analyze case studies to identify dilemmas, evaluate potential sources of bias in data, and develop strategies for mitigation. A significant focus is placed on exploring the ethical implications of emerging technologies, such as AI, to identify strategies for responsible use in professional environments. Finally, students will learn to interact ethically with a range of stakeholder, including faculty and sponsors.
• Technical Presentation – This module is part 2 of 3 of the integrated ethical data storytelling curriculum. By the end of the module, students will apply these ethical storytelling practices directly to their classroom projects and their midpoint practicum presentation. The Technical Presentation module guides students to apply principles of critical and creative thinking to structure persuasive content that engages, motivates and connects with audiences. The curriculum incorporates strategies and practice for managing speaking anxiety, to help students project confidence and master verbal and nonverbal skills. Students learn to utilize visuals effectively and ethically, respond professionally to audience questions, and collaborate with peers to deliver team presentations tailored to the needs of their practicum sponsors.
• Technical Writing – This module is part 2 of 3 of the integrated ethical data storytelling curriculum. By the end of the module, students will apply these ethical storytelling practices directly to their homework reports, blog drafts and documentation for practicum. The Technical Writing module emphasizes the creation of audience-centered documents that are clear, concise and persuasive for professional audiences. Instruction focuses on writing, critiquing and analyzing documents to develop effective editing and proofreading strategies. 
• Analytics in Practice (AIP) Guest Lectures – Invited corporate, governmental and non-profit professional data professionals review how their functions work in their organizations so that students develop an appreciation and understanding of the breadth of the career field.
• Practicum – In this first of a two-module real-world experiential learning sequence, students develop the teaming, communication, project management and technical skills needed to execute analytics-focused project work using sponsor-provided large/complex data in pursuit of sponsor-specified business objectives, develop formal technical presentations communicating results and actionable recommendations in detail and present professional presentations back to the corporate, governmental and non-profit sponsors oriented to bottom line mission requirements. Projects vary but generally descriptive understanding of the relevant datasets and a complementary understanding of the business problem is developed with an endpoint of an early inferential product to build upon for the next semester.
• Team and Project Management – Lectures cover leadership and followership frameworks and project management tools — including Scrum and CRISP-DM — to accomplish analytics-focused team projects.

• Agentic AI – This module explores the design and implementation of intelligent systems that use large language models to reason, plan, use external tools, retrieve information, maintain memory and autonomously complete complex tasks. Building on prior coursework in machine learning, students will develop AI agents capable of supporting analytical workflows through retrieval-augmented generation (RAG), tool use, multi-step reasoning and multi-agent collaboration. The course emphasizes hands-on development, evaluation and responsible deployment of agentic systems for real-world analytics applications.
• Deep Learning – This module provides a comprehensive understanding of deep learning (DL) concepts and deep neural networks (DNN), distinguishing them from traditional artificial neural networks. Students will begin with a history of neural networks and their position within supervised learning. Next, we will discuss the presentation of a solution for implementing deep neural networks and deep learning (DNNs and DL) in the context of object recognition. We will present practical examples of fully connected networks (FCNs) and convolutional neural networks (CNNs) for classification and MNIST digit recognition, a problem often used as a testbed for basic DNN implementations. We will conclude with the use of FCNs and CNNs. Emphasis is placed on practical implementation.
• Financial Analytics – This module focuses on the development and evaluation of score cards often used by financial institutions to make informed decisions based on probability of outcomes. Students will learn how to build scorecards to rank credit applicants and determine how to set appropriate cutoff points to maximize customer approvals for a given default rate. In addition, students will analyze the time-varying risk of credit portfolios through the application of ARCH and GARCH models, including both symmetric and asymmetric specifications, to better understand and manage volatility over time.
• Marketing Analytics – This module explores the use of statistical modeling, machine learning and artificial intelligence to support data-driven marketing decisions. Building on prior coursework in analytics and machine learning, students will analyze customer behavior, evaluate marketing performance, develop predictive models for customer acquisition and retention, measure marketing effectiveness and optimize marketing strategies across digital and traditional channels. The course emphasizes practical applications using real-world marketing data while exploring the growing role of AI in customer insights, personalization and marketing decision support.
• Simulation and Risk – This module introduces computational methods for modeling uncertainty and supporting decision-making in complex systems. Building on prior coursework in statistics, machine learning and artificial intelligence, students will develop simulation models to evaluate risk, quantify uncertainty, analyze alternative scenarios and optimize decisions under uncertainty. Topics include Monte Carlo simulation, sensitivity analysis, probabilistic forecasting and simulation-based optimization. The course emphasizes practical applications in business, healthcare, engineering, finance and public policy.
• Bayesian Statistics – This module introduces Bayesian statistical methods and contrasts them with the frequentist framework. Students will develop an understanding of prior, sampling and posterior distributions, and will learn to construct posterior distributions using modern probabilistic programming tools such as Stan or PyMC. The module provides tools in diagnosing and resolving convergence issues, constructing credible intervals and transforming parameters into meaningful quantities of interest. Applications include Bayesian linear and logistic regression, with a focus on interpretation and real-world inference.
• Fraud/Anomaly Detection – This module introduces methods for fraud and anomaly detection, with a focus on the unique challenges posed by rare and highly imbalanced events. Students will examine the defining characteristics of fraud and how they influence model selection, evaluation and interpretation. The module covers both non-statistical and statistical approaches to anomaly detection, including robust adjustments for outliers and the application of machine learning models. Students will learn techniques for balancing rare-event data, scoring new observations using supervised fraud and non-fraud models and interpreting fraud risk based on observed patterns and probabilities.
• Optimization – This module introduces optimization techniques for solving complex business and analytical decision-making problems. Students will learn to formulate optimization models by identifying decision variables, objective functions and constraints, and will apply linear, integer, mixed-integer and nonlinear optimization methods. Applications include portfolio optimization to determine asset allocations that minimize risk while achieving a target return. The module emphasizes hands-on implementation using SAS, R and Python, including the use of Gurobi in R and Python to solve real-world optimization problems.
• Survival Analysis – This module introduces survival analysis methods for modeling time-to-event data and highlights how these techniques differ from traditional regression approaches. Students will learn to construct and interpret survival curves using the Kaplan-Meier estimator, as well as to analyze survival and hazard functions. The module covers key modeling frameworks, including Accelerated Failure Time models and Cox proportional hazards regression, with an emphasis on appropriate model selection and diagnostics such as assessing distributional assumptions. Advanced topics include the analysis of repeated events and competing risks commonly encountered in real-world applications.
• Advanced SQL – This module builds upon foundational SQL knowledge by introducing advanced techniques for writing efficient, scalable and production-ready queries. Students will explore advanced window functions, recursive common table expressions (CTEs), query optimization, execution plans, indexing strategies, transactions, stored procedures, views and working with semi-structured data such as JSON.
• Big Data / Cloud Computing – This module introduces the foundations of Big Data with a focus on cloud computing technologies, specifically Amazon Web Services (AWS). Students explore the history and evolution of Big Data systems and distributed computing, including Hadoop and Spark. Using AWS Elastic MapReduce (EMR), the module provides hands-on experience deploying, managing and analyzing large-scale data processing frameworks. Through practical labs, students develop the skills needed to work with modern Big Data tools in the cloud.
• Advanced Big Data – This course provides a hands-on roadmap for leveraging cloud services to solve data science and AI challenges. Rather than focusing on theory, we explore how scalable infrastructure, from serverless SQL and distributed Spark processing to generative AI frameworks, can empower you to be a more efficient and impactful practitioner. A primary goal of this module is to provide broad exposure to a diverse toolkit of services. By navigating various architectures and frameworks, the students will develop the intuition to know which tools they prefer, which ones to avoid for specific tasks and exactly where to apply them in a professional pipeline. Drawing on real-world industry experience, the curriculum bridges the gap between a local notebook and production-ready AI and ML solutions.
• SAS Viya for ML – This module focuses on applying machine learning techniques using SAS Viya as an enterprise analytics platform. Building on prior knowledge of machine learning fundamentals, students learn how to implement, compare and deploy models within the SAS Viya environment. Emphasis is placed on hands-on use of Viya tools for data preparation, model training, evaluation and automation. 
• Technical Interview Prep – This hands-on module prepares students for technical and programming interviews through practice and real-world problem solving. Students work through common interview-style questions. Through the practice labs, students build confidence and readiness for technical interview environments.

• Ethics – This module is part 3 of 3 of the integrated ethical data storytelling curriculum (technical writing, technical presenting, and ethics) that provides students with a foundational understanding of ethical communication and professional presentation,including the responsible and effective use of generative AI, focusing on the creation of audience-centered documents and presentations that prioritize critical and creative thinking. By the end of the module, students will apply these ethical storytelling practices directly to their final practicum project deliverables. The Ethics module provides a foundational understanding of ethical data storytelling and the application of various ethical frameworks to data analytics. Students will analyze case studies to identify dilemmas, evaluate potential sources of bias in data and develop strategies for mitigation. A significant focus is placed on exploring the ethical implications of emerging technologies, such as AI, to identify strategies for responsible use in professional environments. Finally, students will learn to interact ethically with a range of stakeholders — including faculty and sponsors — and refine their practicum team’s ethical framework created in the fall.
• Technical Presentation – This module is part 3 of 3 of the integrated ethical data storytelling curriculum. By the end of the module, students will apply these ethical storytelling practices directly to their final practicum project deliverables. The Technical Presentation module guides students to apply principles of critical and creative thinking to structure persuasive content that engages, motivates and connects with audiences. The curriculum incorporates strategies and practice for managing speaking anxiety, to help students project confidence and master verbal and nonverbal skills. Students learn to utilize visuals effectively and ethically, respond professionally to audience questions and collaborate with peers to deliver team presentations tailored to the needs of their practicum sponsors.
• Technical Writing – This module is part 3 of 3 of the integrated ethical data storytelling curriculum. By the end of the module, students will apply these ethical storytelling practices directly to their final practicum project deliverables. The Technical Writing module emphasizes the creation of audience-centered documents that are clear, concise and persuasive for professional audiences. Instruction focuses on writing, critiquing and analyzing documents to develop effective editing and proofreading strategies. Students will progress through a structured writing process — starting with an initial report framework and content drafts to produce an audience-oriented final practicum report — while reflecting on feedback to plan for ongoing improvement of their writing skills.
• Practicum – In this second of a two-module real-world experiential learning sequence, students develop the teaming, communication, project management and technical skills needed to execute analytics-focused project work using sponsor-provided large/complex data in pursuit of sponsor-specified business objectives, develop formal technical presentations and reports communicating results and actionable recommendations in detail, and present professional presentations back to the corporate, governmental and non-profit sponsors oriented to bottom line mission requirements.

The overarching goal of this module is to expose students to cutting-edge AI use within the context of data science. This will help students see the direct applications of AI across a wide range of industries, allowing them to better understand how the concepts they learn in the classroom translate into real-world solutions. Lectures and workshops in AA 591 will contribute to a student’s ability to Learn AI, Learn with AI, Build AI and Build with AI.