Curriculum: Master's in Healthcare Data Science | Northwestern SPS - Northwestern School of Professional Studies

In this course students utilize data science software to practice R and Python programming.  Students will install and launch software and apply this industry relevant programming language to generate, manipulate, manage, and visualize health (or related) data.  In this course students complete a variety of hands-on programming exercises to develop data science programming skills while elearning about AI-assisted coding as well.

This course will provide an introduction to the current structure and emerging trends shaping the US Healthcare System. Students will learn what it means to navigate the confusing, bottom-up, and variously incentivized entities in the American health care system.  Additionally, we will cover foundational healthcare data sources for the 3Ps (Providers, Patients, and Payers) and fundamentals of Health Information Technology including electronic health records, health information exchanges, clinical decision support, consumerism, and the impact of big data and predictive analytics.  Topics center on how information technology and analytics enables patient care and fuels modern health care organizations.

Students will learn about current and future data trends, relational databases, healthcare data standards, and the basics of utilizing SQL for data and analytics including the use of GenAI. Becoming familiar with the fundamentals of relational databases and SQL, the most popular language used to query data from relational databases, is an essential part of this class.

Students will leave this course with a strong understanding of standard healthcare terminologies (i.e., RxNorm, SNOMED), relational databases, and how to retrieve, analyze, and aggregate relational data for analytics purposes using SQL. Concepts learned in this class will also introduce students to deeper data modeling concepts(OMOP, FHIR).

This course provides a working knowledge of the specific considerations for health data and the appropriate application of privacy laws to protect personal health information and maintain confidentiality.  This will include oversight of technical, administrative, and physical safeguards needed to maintain a secure environment and minimize the risk of a data breach.  Additionally, the course will address ethical concerns and dilemmas in the use and disclosure of health data, such as use in public safety and medical research. Topics include: ensuring system specifications and configurations meet regulatory requirements; AI governance; ensuring cybersecurity risks are mitigated; addressing European Union Privacy Laws for international systems; and managing organizational & technical governance.

This is an introductory course to general concepts and fundamentals in the practice of biostatistics as commonly used in health data. This will form the foundation for more advanced topics to come in later courses. The material covered will be sufficient to perform basic descriptive statistical analyses.

The goal of this course is to teach students techniques in how to perform statistical analysis of health data sets in R and Python. The techniques you’ll learn in this course are important in themselves, and will form the foundation for later courses in the MHDS program.

Prerequisite: MHDS 401 Foundation of Healthcare Programming with Python and R

This course will provide students with the skills to develop analytical features from health datasets. Students will develop an understanding of healthcare data, particularly electronic health record (EHR) data, and use R & SQL to build features for analytical modeling. In addition to working with continuous and categorical health data, students will understand and develop skills for natural language processing to extract discrete data elements from free-text clinical documentation, such as physician notes, for the development of analytical features.

Prerequisite: MSDS 405 Data Literacy and Analytics in Healthcare

The course introduces machine learning (ML) and artificial intelligence (AI) methods that are applied to a wide range of problems in health care. It surveys ML and AI techniques, including resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, ensemble methods, and artificial neural networks. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, classification, and model output post-processing. This course will address effective prompt engineering, model lifecycle, and deep learning. A reoccurring theme throughout the course is the reproducibility and trustworthiness of analytic results, and on workflow management.

Prerequisite: MHDS 409 Foundation of Health Statistics and Data Exploration

This course will build upon the analytical tools learned during the previous courses in the MSHA sequence to enable students to visually convey their findings to both technical and non-technical audiences. In this course, students will learn how to identify and explain the layers of the grammar of graphics, select effective static data visualizations, write R code to manipulate data visualizations, and construct their own compelling visualizations from scratch using health data. Course goals will be achieved using the ggplot2 package in R. In addition this course will utilize Tableau and Power BI for multistakeholder communications. By the end of the course, students should be effective visual communicators of their findings and will be proficient in producing impactful visualizations using ggplot2.

Prerequisites: MHDS 401 Foundation of Healthcare Programming with Python and R and MHDS 409 Foundation of Health Statistics and Data Exploration

This interdisciplinary course provides students with the knowledge and leadership skills to drive operational excellence and digital transformation in healthcare organizations. Integrating perspectives from both Health Analytics and Health Informatics, the course explores how healthcare systems operate—and how strategy, innovation, data, and AI can be leveraged to improve clinical, operational, and research outcomes.

Students will examine foundational aspects of healthcare operations including governance, interdisciplinary care models, financial stewardship, emergency preparedness, cybersecurity, and quality improvement. In parallel, they will explore the leadership and strategic dimensions of digital health and analytics—developing competencies in business intelligence (BI), systems/data governance, organizational readiness, and stakeholder engagement.

Additional emphasis is placed on identifying opportunities to apply analytics, informatics, and emerging technologies (such as AI, LLMs, and cloud-based tools) to solve real-world challenges. Students will learn how to lead cross-functional teams, apply project and change management tools, and translate complex data into actionable insights. Case-based projects will help students explore how to align innovation and analytics strategies with organizational goals.

By the end of this course, students will be able to:

• apply tools learned during MHDS coursework to define a problem in health care
• define the data needed to resolve the problem
• perform the analysis, and communicate the results and conclusion of the analysis in written form
• present their project to a leadership team with a convincing proposal and recommendations

Prerequisite: Capstone must be taken in the final quarter of the MHDS program.
Note: Registration for this course will close one week prior to the start of the term.

This course builds on MHDS 409 and develops the foundations of predictive modeling by: introducing the conceptual foundations of regression and multivariate analysis; developing statistical modeling as a process that includes exploratory health data analysis, model identification, and model validation; and discussing the difference between the uses of statistical models for statistical inference versus predictive modeling. The high level topics covered in the course include: exploratory data analysis, statistical graphics, linear regression, automated variable selection, principal components analysis, exploratory factor analysis, and cluster analysis.

Prerequisite: MHDS 409 Foundation of Health Statistics and Data Exploration

This course provides a practical, interdisciplinary exploration of applied artificial intelligence (AI) in healthcare, with a focus on high-impact use cases such as Natural Language Processing (NLP), computer vision, and clinical decision support systems (CDSS). Students will examine real-world applications ranging from automated clinical documentation and radiology image analysis to predictive risk modeling and AI-assisted care delivery.

Designed for healthcare and analytics professionals, this course emphasizes how to evaluate, implement, and lead AI-driven solutions, rather than build them from scratch. Through hands-on case studies and interactive scenario planning, students will learn how to assess data readiness, address model limitations, and communicate the value of AI tools to clinical and operational stakeholders.

Key topics include AI adoption frameworks, handling messy healthcare data, evaluating vendor tools, understanding ethical and regulatory considerations (e.g., HIPAA, bias mitigation, FDA guidance), and aligning AI innovation with organizational goals. By the end of the course, students will be equipped to bridge the gap between advanced AI technologies and responsible, outcome-driven implementation in healthcare settings.

As artificial intelligence (AI) becomes foundational to modern healthcare and biomedical research, scalable infrastructure and cloud computing environments are critical to support real-time analytics, machine learning pipelines, and growing healthcare datasets. This course introduces the key concepts, tools, and architectures that underpin AI-readiness at scale, with a focus on practical applications in health systems, research institutions, and AI innovation hubs.

Students will gain hands-on exposure to core components of modern data infrastructure, including cloud service models (IaaS, PaaS, SaaS), containerization (Docker, Kubernetes), data pipelines, and storage strategies optimized for protected health information (PHI). The course also explores the role of distributed computing, high-performance environments, and MLOps practices for deploying AI models into production.

Through real-world case studies and guided design exercises, students will assess trade-offs in cost, compliance, scalability, and performance, developing a systems-thinking approach to supporting AI and data science initiatives in clinical, operational, and research settings.

In this course, you will be introduced to the design and adoption of AI-enabled digital twinning, the aim of which is to curate dynamic, continuously learning service-oriented solutions that mirror a person’s physiology, behavior, and contextual care. Students will integrate multimodal sensing, machine-learning, and Agentic AI to (1) model functional states such as activity, emotion, and circadian rhythm; (2) generate holistic, outcomes-based care plans; (3) develop prescriptive “clinical sandboxes” that predict risk and compare treatment pathways (i.e., sepsis, mortality, and outcomes) inclusive of social determinants of health; and (4) address data-governance challenges of consent, provenance, and portability. By the end of the course, learners build a functioning Digital Twin Agentic AI Agent service that uses a blend of language and/or vision models based on a digital-twin dataset of their choice.