Master of Science in Pharmaceutical Sciences (MSPS) with a Focus in Data Science & Health Informatics Program

What is Data Science?

Data science uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data science involves a range of techniques. These include data mining, machine learning, statistical analysis, and visualization.

These techniques are used to uncover patterns, trends, and correlations in large datasets. As a data scientist these techniques are used to make data-driven decisions, develop predictive models, and derive actionable insights from complex data.

Healthcare utilizes data science to analyze and interpret vast amounts of health-related data within the healthcare system. Data science helps in areas such as clinical research, research and development of new medicines and supplements, disease prediction, patient monitoring, and personalized medicine.

Data scientists in healthcare have many responsibilities. They identify patterns in patient and EHR data, optimize treatment plans and predict disease outbreaks. Their work contributes to improving overall healthcare outcomes.

What is Health Informatics?

Health informatics focuses specifically on the management and application of healthcare information. It involves using information technology to collect, store, retrieve, and transmit health-related data securely and efficiently. Health informatics encompasses various aspects of healthcare data, including electronic health records (EHRs), medical imaging, clinical decision support systems, health information exchange, and telemedicine.

Health informatics professionals work on designing, implementing, and maintaining information systems that facilitate the storage, retrieval, and analysis of healthcare data. They ensure the interoperability and security of health information technology and systems, develop standards and policies for data exchange, and collaborate with healthcare providers to improve the use of technology in healthcare delivery. Health informatics aims to enhance patient care, streamline administrative processes, reduce medical errors, and support evidence-based decision-making

Our MSPS with a focus in Data Science & Health Informatics, prepares students for meaningful, in-demand careers such as:

• Data Scientist
• Data Analyst
• Business Intelligence Analyst
• Health Informaticist
• Bioinformatics Specialist

Students working towards the MSPS with a focus in Data Science & Health Informatics are required to take three specific courses to fulfill their focus area: Introduction to Data Science, Applied Machine Learning and Health Informatics. The thesis research that is required at the end of the program will focus on data science and health informatics.

MS660 – Introduction to Data Science (3 credits)

This course is designed to introduce students to the field of data science, which combines elements of statistics, computer science, and domain-specific knowledge to analyze and extract insights from data. Students will learn about the data science workflow (from data collection and cleaning to analysis and visualization) and will gain experience with popular data science tools and programming languages such as Python and R.

MS661 – Applied Machine Learning (3 credits)

This course introduces students to the fundamental principles and techniques of supervised and unsupervised machine learning. Students will learn how to preprocess and analyze data, design, and train various machine learning models, and evaluate the performance of those models. Through hands-on exercises and projects, students will gain experience with popular machine learning tools, frameworks, and solutions implemented in on-premises and cloud environments.

MS662 – Health Informatics (3 credits)

This course covers the fundamental principles and concepts of health informatics, including the design, development, implementation, and evaluation of health information systems. Students will learn how to use information technology to improve outcomes pertaining to healthcare delivery, patient care, and population health. Course topics include health information standards and interoperability, electronic health records, health data analytics, clinical decision support systems, and health information privacy and security.

MS730 – Thesis Research (Graduate Internship in Data Science & Health Informatics) (1-3 credits)

The Graduate Internship in Data Science & Health Informatics is designed to provide students with practical skills in applying data science and informatics principles to real-world problems in the health sciences. Students will work with industry partners or academic researchers to gain hands-on experience in designing, developing, and evaluating data-driven healthcare solutions.

Admissions Materials

An applicant must submit the following materials:

1. completed application
2. $75 non-refundable application fee
3. an official transcript from each college or university attended, listing all courses taken, grades and degrees earned, and dates of graduation
4. three letters of recommendation from persons acquainted with the applicant’s academic program, scholastic ability, or professional performance
5. a brief autobiographical statement describing the applicant’s educational and professional goals and objectives
6. a curriculum vitae

Additional material or standards may be required by the academic unit’s Graduate Studies Committee for the purpose of evaluating prospective students and making admission decisions. These requirements may include:

• minimum requirements for the GRE or other standardized tests, as may be relevant to the field of study
• specific pre-requisite courses
• other training/preparatory activities, as applicable

Admissions Criteria

• Bachelor’s degree from an accredited institution in the Life Sciences (biology, biochemistry, biotech, chemistry, pharmacy, pharmaceutical sciences or related field) with a preferred GPA of 3.0.
• Three letters of recommendation emphasizing academic performance.
• A personal statement, stating future goals and research interests and interest in the MSPS program

Jamie Fairclough, PhD, MPH, MS

Professor & Associate Dean (Roseman College of Medicine)
Adjunct Professor of Data Science (Noorda College of Osteopathic Medicine)

Atif Mohammad, PhD

Chief AI & Data Officer (Omni AGI – Global Technology Solutions)
Adjunct Professor (UNC Charlotte)

Donald Williams, PharmD

Clinical Pharmacist (Cleveland Clinic)

Hari Mudipalli, MSDS

Data Scientist (Roseman College of Medicine)

Mahtab (Hamid) Milburn, MBS

Data Scientist (Roseman College of Medicine)