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

The MS in Healthcare Data Science (MHDS), previously the Master's in Health Analytics, is an online graduate program from Northwestern University's School of Professional Studies that prepares professionals to apply data, analytics, and AI to healthcare, research, and life sciences challenges.

Students build technical skills in programming, statistics, and machine learning alongside healthcare domain knowledge, governance, and leadership, so they can turn complex healthcare data into decisions that hold up in clinical, operational, and research settings.

The online MS in Healthcare Data Science at Northwestern distinguishes itself from similar programs at other insitutions in several ways:

• Healthcare-focused approach: Rather than teaching data science in a general context, the curriculum applies analytics, machine learning, AI, and data visualization directly to healthcare, research, and life sciences challenges.
• AI-integrated curriculum: Students gain practical experience with machine learning, generative AI, large language models (LLMs), and AI-enabled healthcare workflows while also exploring responsible AI, governance, and ethics.
• Interdisciplinary perspective: Coursework combines technical training with healthcare systems knowledge, digital health, leadership, communication, and strategy.
• Faculty expertise: Courses are taught by experienced healthcare, analytics, and technology professionals who bring real-world industry perspectives into the classroom.
• Practical learning: Through applied projects, case studies, and a capstone experience, students learn how to translate data into actionable insights that drive organizational impact.

The MHDS degree curriculum requires 12 courses: 10 core courses, including a capstone, and 2 electives.

Core courses cover foundational programming, the U.S. healthcare and digital health landscape, healthcare data literacy and SQL, data governance and AI ethics, health statistics, feature engineering and unstructured data, practical machine learning and AI, data visualization and storytelling, and healthcare strategy and AI leadership. Electives let students go deeper in areas such as predictive modeling, applied AI, cloud platforms, and digital twin intelligence in healthcare.

No; the Master's in Healthcare Data Science program is designed for students from a variety of professional and academic backgrounds.

Students may come from healthcare, technology, business, research, public health, or life sciences. The curriculum builds foundational healthcare knowledge while progressively developing technical skills in programming, analytics, and AI, so both healthcare professionals and technically-oriented students without healthcare experience can succeed.

No prior programming experience is required to succeed in the MS in Healthcare Data Science.

Students develop programming skills throughout the curriculum, beginning with foundational coursework in Python and R before progressing to more advanced analytics, machine learning, and AI applications. The program supports learners with varying levels of technical experience.

AI is integrated throughout the curriculum for the MS in Healthcare Data Science rather than treated as a standalone topic.

Students learn to apply machine learning, generative AI, large language models (LLMs), and natural language processing to healthcare and research challenges. Coursework also covers responsible AI implementation, model evaluation, governance, privacy, ethics, and organizational adoption, so graduates understand not just how AI works, but how to apply it effectively and responsibly in healthcare settings.

The MHDS capstone is the culminating, applied project in which students bring together the technical and healthcare skills built throughout the program.

It is a challenging but rewarding experience that prepares and enhances students' professional skills in healthcare data science, with a greater emphasis on career-relevant, real-world experience than other courses in the MHDS program. Students define a real healthcare, research, or operational problem; identify and analyze the relevant data; and present findings and recommendations to a leadership audience. Capstone projects have addressed problems such as predicting hospital readmissions, evaluating digital health or remote monitoring programs, and analyzing care access and health equity using real-world data. Depending on project complexity, students can expect to spend 5 to 10 hours per week, or 50 to 100 hours over the quarter.

Graduates of the Master's in Healthcare Data Science are prepared for a broad range of careers in analytics, AI, and data-focused across healthcare, life sciences, and public health organizations, spanning entry-level to leadership positions.

• Entry-level: Healthcare Data Analyst, Clinical Data Analyst, Population Health Analyst, Business Intelligence Analyst
• Mid-level and specialist: Healthcare Data Scientist, Research Data Scientist, Healthcare Analytics Manager, Healthcare AI Specialist, Digital Health Analytics Consultant
• Senior and leadership: Director of Healthcare Analytics, Health System Strategy and Analytics Leader, Chief Data Officer

Graduates of the MHDS program (previously the MS in Health Analytics) work at healthcare providers, payers, pharmaceutical and biotechnology companies, research institutions, public health organizations, consulting firms, and health technology companies.

Yes, each group can use the program differently to reach the same outcome: applying data and AI to healthcare with confidence.

Clinicians and physician scientists use it to strengthen data literacy and lead AI-enabled initiatives. Data scientists and analysts without healthcare experience use it to learn the clinical context, terminology, and privacy considerations specific to healthcare data. Recent graduates with a strong interest in healthcare, data, or AI can use it to build a specialized foundation for entry-level analytics roles.

Yes; Northwestern's online MHDS degree program is designed for busy working professionals. To be successful in your classes, you should plan for approximately 12 to 15 hours of work per week per course.

Courses in the MHDS program are asynchronous, meaning that there is no designated class meeting time each week.

Please note, however, that this is not a self-paced program. Students complete assignments on their own schedule but still have weekly deliverables such as readings, discussions, projects, and problem sets. Each course also includes a minimum of three live, optional Zoom sessions per quarter, hosted by the professor and recorded for students who cannot attend.

Students in the online Master's in Healthcare Data Science gain real-world experience through applied projects, healthcare datasets, case studies, and the capstone.

Coursework uses tools and technologies common in industry, including Python, R, SQL, Tableau, Power BI, and AI-enabled workflows, so students build a portfolio of practical work alongside their degree.

Students in the online MS in Healthcare Data Science build professional connections through faculty who are active practitioners, classroom discussions and group projects with peers from diverse professional backgrounds, and through Northwestern's alumni network.

Additional opportunities include the student leadership council and career resources, webinars, and professional development programming available through Northwestern.

As of the 2026-27 academic year, the online MS in Healthcare Data Science degree consists of 12 courses at $4,780 per course, for an estimated program total of $58,740, including a technology fee for each online course.

Tuition is paid per course each term—there is no quarterly or annual fee, and students do not pay tuition when not enrolled in courses. Program tuition, which is competitive with comparable programs at peer institutions, includes access to career advising, writing and math support, and Northwestern's global alumni network.

Both programs prepare students to improve healthcare through technology and data, but they emphasize different areas of expertise.

The MS in Healthcare Data Science focuses on analytics, statistical modeling, machine learning, AI, programming, and data-driven decision-making. The MS in Health Informatics focuses on the design, implementation, and optimization of healthcare information systems, including electronic health records, health information exchange, and clinical workflows.

The Healthcare Data Science Master's program is best suited for professionals interested in analytics, AI, machine learning, and data-driven innovation. Health Informatics Master's program is best suited for professionals interested in healthcare technology implementation, clinical systems, and informatics leadership.

A general MS in Data Science teaches broad technical methods applicable across industries; Northwestern's MHDS applies those same methods specifically to healthcare, research, and life sciences.

That distinction matters because healthcare data carries unique complexity: clinical context, payer and provider workflows, health data standards, privacy requirements, and regulatory constraints. MHDS is designed for students who need data science and AI skills to hold up in healthcare settings where accuracy, responsible use, and stakeholder trust all matter.