Master of Science in Healthcare Analytics (MSHA) Program

Program Overview

The main objective of this program is the training of recent graduates to help them land their first job as entry-level data scientists or analysts in health-related industries and organizations. The program will also support healthcare professionals already in managerial or oversight roles who are seeking to create data-driven solutions for complex problems in their areas and organizations. 

The program adopts a Problem-Based Learning (PBL) approach to design and deliver the modules of the program. PBL is increasingly considered an effective instructional approach to facilitate students’ attainment of the high-level competences and transferable skills increasingly being demanded by industry and the public sector.

The program will be structured in 3 stages as follows:

Stage 1 will ensure that students acquire advanced programming skills and statistical analysis and data visualization techniques.

Stage 2 will introduce students to machine learning techniques and generative AI and allow them to apply their skills in healthcare contexts to solve problems and drive decision-making. 

Stage 3 will provide students with the latest knowledge of healthcare delivery models and services and understand how to apply their analytical, technical, and communication competences to drive improvement in quality, utilization, operations, and other areas of healthcare management.

Program Outcomes

The student learning outcomes are grouped into the categories of analytical, technical, communication, and healthcare, and there is a substantial intersection between categories. In completing the program, students will:

Analytical Competences

• Build statistical models and understand their power and limitations.
• Design an experiment.
• Apply problem-solving strategies to open-ended questions.
• Determine when to use generative AI in healthcare contexts.
• Translate business requirements into technical specifications.
• Design and adapt secure generative AI systems in healthcare contexts.

Technical Competences

• Acquire, clean, and manage health data.
• Handle and analyze massive health datasets.
• Use machine learning and optimization to make decisions. 
• Assemble computational processes to develop data science using widely available market tools.

Communication Competences

• Visualize data for exploration, analysis, and communication in healthcare contexts.
• Collaborate with other functional teams.
• Perform reproducible data analysis.
• Conduct data science activities mindful of policies, privacy, security, and ethical considerations.
• Utilize generative AI-based systems consistent with privacy best practices, ethical principles, and security.

Healthcare Competences

• Demonstrate a practical understanding of care model concepts, funding and reimbursement models, basic organizational decisions, and patient population selection and coverage.
• Demonstrate a practical understanding of quality, utilization, and operations in a healthcare context and the ability to apply analytical, technical, and communication Competences to improve outcomes for patients and providers. 

Program Summary

The program consists of a total of 36 credits and encompasses 11 core courses along with a Capstone Project. It is structured to be completed in a specific sequence, as indicated by the prerequisites mentioned in the Course Descriptions section. The Capstone Project serves as a platform to integrate and apply the knowledge acquired throughout the degree program. Consequently, this course is pursued at the end of the program on its own or simultaneously with one other course if desired.

Program Requirements

In order to be eligible for the award of degree, students must:

• successfully complete and pass all core courses (33 credits);
• successfully complete and pass the Capstone Project (3 credits);
• maintain acceptable program performance and remain in good academic standing, which includes maintaining a cumulative GPA of 3.00 or above and no more than 2 “C” grades.
• have no outstanding student disciplinary sanctions or investigations; and
• be in good financial standing.

Course Descriptions

All courses will be offered either 100% online or in hybrid format, depending on the program format that students have selected.

Python for Data Science 1 (MTI-521)
3 credits

In this course you will learn fundamental programming skills that enable you to search and sort data. You will be introduced to programming in Python and learn how to develop and run programs in Jupyter Notebooks. You will learn key programming principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data

Python for Data Science 2 (MTI-522)
3 credits

In this course you will learn fundamental programming skills that enable you to process textual and time-series information. You will further learn programming concepts in Python. You will learn advanced programming principles and will practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data.
Prerequisite: Python for Data Science 1

Math for Data Science 1 (MDS-511)
3 credits

In this course you will learn fundamental statistical skills that enable you to analyze data. You will be introduced to basic descriptive and exploratory statistics and will learn how to implement them using software code. You will learn key statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data.

Math for Data Science 2 (MDS-512)
3 credits

In this course you will learn fundamental statistical skills that enable you to analyze data. You will be introduced to basic statistical analysis with a focus on regression models and learn how to implement them using software code. You will learn key statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using data.
Prerequisite: Math for Data Science 1

Python for Data Science 3 (MTI-621)
3 credits

In this course you will learn advanced programming skills that enable you to process large datasets and access external databases. You will further learn programming concepts in Python. You will learn advanced programming principles and practice applying them to real business or industry problems. These skills will further enhance your ability to address business or industry problems using data.
Prerequisite: Python for Data Science 2

Math for Data Science 3 (MDS-641)
3 credits

In this course you will learn advanced statistical skills that enable you to analyze panel data. You will be introduced to advanced statistical analysis and learn how to apply it using software code. You will learn key statistical principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using complex data structures.
Prerequisite: Math for Data Science 2

Analytics for Decision Making (MDS-741)
3 credits

In this course you will apply the concepts, methodologies, and techniques from previous courses to tackle realistic analytical challenges. You will develop analytical skills and Competences required to inform decision-making processes in business and industry contexts. This course will allow you to consolidate your analytical skills and Competences.
Prerequisites: Python for Data Science 3, Math for Data Science 3

Machine Learning for Data Science 1 (MDS-731)
3 credits

In this course you will learn advanced statistical skills that enable you to classify, forecast and find patterns in data. You will be introduced to clustering, forecasting and classification techniques and learn how to apply them using software code. You will learn basic machine learning principles and practice applying them to real business or industry problems. These skills will form the basis of your ability to address business or industry problems using machine learning techniques.
Prerequisites: Python for Data Science 3, Math for Data Science 3

Generative AI for Data Science 1 (MTI-771)
3 credits

In this module you will learn how to apply generative artificial intelligence to process and transform complex, unstructured datasets. You will be introduced to prompt engineering and learn how to apply it for analytical purposes. These skills will complement your ability to process textual information and extract relevant insights from it.
Prerequisites: Python for Data Science 3, Math for Data Science 3

The Business of Medicine (MBU-511)
3 credits

This course is designed to provide a comprehensive understanding of the healthcare business landscape, including the various components that drive success in the industry. From crafting a clear vision and mission to navigating the intricacies of the revenue cycle, this course offers valuable insights and strategies for anyone looking to start or run a healthcare-based business. With a focus on putting the patient at the center and understanding the business behind medicine, it equips you with the knowledge and skills to navigate the healthcare industry successfully.

Healthcare Quality and Service Optimization (MHC-536)
3 credits

This course delves into the fundamental principles and methodologies essential for enhancing healthcare quality, optimizing service delivery, and improving operational efficiency within healthcare organizations. You will explore topics including quality measurement, utilization management, and healthcare operations optimization and develop the knowledge and insights necessary to leverage healthcare analytics to drive meaningful improvements in healthcare quality and operational effectiveness.

Capstone Project (MAF-731)
3 credits

In this course you will apply the concepts, methodologies, and techniques from previous courses to provide a solution to a real business or industry challenge. This course will allow you to consolidate your analytical skills and Competences and gain real experience as an aspiring data scientist or analyst.