Asya W.

Master of Science
in Data Science,
Health Analytics

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240K+ Alumni Worldwide


The Master of Science in Data Science program prepares you for a career in data science by teaching you how to apply statistical methods in solving real-world problems. Core coursework includes data modeling; data management; and mining of continuous, categorical, and multivariable data. Advanced specializations focus on artificial intelligence and optimization, business analytics, database analytics, or health analytics. The program culminates in a three-month capstone where real data from sponsoring organizations (or, alternatively, publicly available data) will be used in a team project to demonstrate your mastery in data acquisition, cleaning, analysis, modeling, visualization, and reporting.

The Health Analytics specialization is designed to provide data professionals with  practical learning experience through application of statistical methods to help solve real-world health and life science analytics problems. Instruction and coursework will explore healthcare information management systems, epidemiology, health management, clinical research, clinical trials, and health-outcomes research.

Foundation Courses

For the Master of Science in Data Science degree with a specialization in Health Analytics, you must complete seven foundation courses, five specialization courses, and three capstone courses. Completion of all foundation and specialization courses is required prior to starting the capstone course sequence.

Course Details

Foundation Course Listings

Course Name

Introduction to statistical modelling and data analysis using R programming to explore data variation, model the data, and evaluate the models. Analysis and evaluation of different types of regression models and error analysis methods.

PrerequisiteANA 600

Forms of data, gap analysis, model building, and interpretation will form the foundation for students to ethically apply data analytics to facilitate modern knowledge discovery techniques.

PrerequisiteANA 605

Application of the data management process for analytics including acquiring and auditing data, assembling data into a modeling sample, performing basic data integrity checks, cleansing data, feature engineering and data visualization.

PrerequisiteANA 610

Application of data mining methods and predictive modeling. Design of objectives, data selection and preparation, analytic method selection such as classification and decision trees, and predictive modeling will be used for a variety of case studies and practical industry applications.

PrerequisiteANA 615

Application of methods for analyzing continuous data for knowledge discovery. Analytic continuous data concepts and methods are developed with practical skills in exploratory data analysis. Descriptive statistics, goodness-of-fit tests, correlation measures, single and multiple linear regression, and analysis of variance and covariance are covered. Applying continuous data methods using case studies and real world data will leverage statistical assessment and interpretation.

PrerequisiteANA 620

Application of methods for analyzing categorical data for knowledge discovery. Analytic categorical data analysis concepts and methods are developed with practical skills in exploratory data analysis. Descriptive statistics of discrete data, contingency tables, and methods of generalized linear models are covered. Applying categorical methods using case studies and real world data will leverage statistical assessment and interpretation.

PrerequisiteANA 625

Advanced application of data analytics methods for knowledge discovery. This course will explore several of the advanced topics in data analytics such as methods for longitudinal data, factor and principal components analysis, multivariate logistic regression, and multivariate analysis of variance (ANOVA). Application using case studies and real world data will leverage statistical assessment and interpretation.

Specialization Courses

Those with no prior Python programming experience must complete ANA 500 prior to
ANA 670. Those with Python experience will complete ANA 505 after ANA 680.

Course Name

A study of effective data and information technology applications that improve the performance of healthcare organizations. Coursework will focus on information systems; databases and analytical tools for structuring, analyzing, and presenting information; and legal and ethical issues affecting healthcare information management.

An examination of determinants and distribution of diseases and disabilities in human populations. You’ll do empirical analysis on morbidity data and investigate disease outbreaks, risk factors, health outcomes, and causal relationships. As part of your analysis and assessment, you’ll learn to critically evaluate public health literature and the design of health studies.

This course applies health data analytics to improve health results in clinical care. The focus will be on data integration and analysis in support of patient care, improved decision-making, and quality control for evidence-based solutions.

This course studies the application of health-data analytics to guide decisions about the health of populations and individuals. Data integration and analysis for populations and individuals will be conducted to provide evidenced-based solutions in areas of clinical trials, recovery-time assessment, patient stays, risk of complications, morbidity, and mortality.

An introduction to and investigation of advanced topics in AI (artificial intelligence) and optimization in various state-of-the-art applications.

Capstone Courses

Course Name

PrerequisiteAll core and specialization courses in an analytics program with a minimum GPA of 3.0 or approval of Lead Faculty.

Master’s level research in analytic project design, problem framing, and technical presentation. Team building, team collaboration, and conflict resolution are implemented in the proposal of a data science project. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology are proposed and presented to project advisors and stakeholders.

PrerequisiteANA 699A

Continuation of master’s level research in analytic project implementation, technical writing, and project presentation. Strategic and technical aspects of data acquisition, data cleaning, and analytic methodology are implemented and presented to project advisors and stakeholders.

PrerequisiteANA 699B

Completion of master’s level research in analytic project implementation, technical writing, and project presentation. Strategic and technical aspects of data analysis and visualization are implemented and presented to project advisors and stakeholders in a written thesis.

Learning Outcomes

Students earning the Master of Science in Data Science with a Health Analytics specialization will learn to:

  • Analyze the planning, organization, administration, and policies of healthcare organizations using health analytic methods
  • Evaluate healthcare information system technologies through integration and interoperability of health data
  • Integrate data and analytic techniques to ensure that the financial priorities of a healthcare organization are in line with the needs and values of the community and stakeholders it serves
  • Analyze the distribution and determinants of disease and health outcomes in human populations
  • Integrate components of data science to produce knowledge-based solutions for real-world challenges using public and private data sources
  • Evaluate data management methods and technologies to improve integrated data use
  • Construct data files using statistical and data programming to solve practical problems in data analytics
  • Design and implement an analytic strategy for a potential issue relevant to the community and stakeholders
  • Develop team skills to research, develop, and evaluate analytic solutions to improve organizational performance
Program Disclosure

Successful completion and attainment of National University degrees do not lead to automatic or immediate licensure, employment, or certification in any state/country. The University cannot guarantee that any professional organization or business will accept a graduate’s application to sit for any certification, licensure, or related exam for the purpose of professional certification.

Program availability varies by state. Many disciplines, professions, and jobs require disclosure of an individual’s criminal history, and a variety of states require background checks to apply to, or be eligible for, certain certificates, registrations, and licenses. Existence of a criminal history may also subject an individual to denial of an initial application for a certificate, registration, or license and/or result in the revocation or suspension of an existing certificate, registration, or license. Requirements can vary by state, occupation, and/or licensing authority.

NU graduates will be subject to additional requirements on a program, certification/licensure, employment, and state-by-state basis that can include one or more of the following items: internships, practicum experience, additional coursework, exams, tests, drug testing, earning an additional degree, and/or other training/education requirements.

All prospective students are advised to review employment, certification, and/or licensure requirements in their state, and to contact the certification/licensing body of the state and/or country where they intend to obtain certification/licensure to verify that these courses/programs qualify in that state/country, prior to enrolling. Prospective students are also advised to regularly review the state’s/country’s policies and procedures relating to certification/licensure, as those policies are subject to change.

National University degrees do not guarantee employment or salary of any kind. Prospective students are strongly encouraged to review desired job positions to review degrees, education, and/or training required to apply for desired positions. Prospective students should monitor these positions as requirements, salary, and other relevant factors can change over time.