Data Science in Healthcare (Online Certificate Program)


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    In collaboration with:

    Capture the Potential of Healthcare Data

    The volume of data generated by hospitals, health systems, medical staff, and patients is driving an urgent need for expertly trained analysts who can get the data right. Analytical skills are as essential in an epidemic as they are in everyday wellness care, as vital for patients as they are for providers. These data skills are critical for analysts anywhere on the healthcare spectrum – on the clinical, pharmaceutical, risk, or management side. You must know how to operationalize systems to collect, measure, aggregate, interpret, and share the data your healthcare company and its partners need. Optimizing your data science skills can lead to better therapeutic options, enhanced business results, and – most important – improved patient outcomes.

    Consider the impact of big data on both business health and patient health:

    As much as 25% of U.S. per capita spending on healthcare is wasteful. Much of this waste can be mitigated by sharing and analyzing data


    Predictive analytics greatly improves oncologists’ diagnoses. Google’s AI algorithm may detect as many as 99% of metastatic breast cancers. Not surprising, 89% of healthcare executives plan to use predictive analytics in the next five years.


    Key Takeaways

    In this program, you will learn to:

    • Identify, understand, and critique the source of a result
    • Choose the most appropriate tool from a set of analytical tools for your healthcare application
    • Understand R coding and Python and modify that code for a specific task
    • Appropriately format, analyze, and present healthcare data to optimize its use

    Who Is This Program For?

    Data Science in Healthcare is designed for technical professionals who have at least a moderate level of comfort with some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics. In this program, you will learn to:

    • Use RStudio and Python analytics tools to address specific healthcare applications
    • Use predictive analytics for public health issues
    • Use data science to increase efficiency on the operations side
    • Understand how to design precision solutions for patient care using AI
    • Use predictive analytics to prevent fraud and other undesired outcomes

    Although these topics could be applied to a range of businesses, this program will be particularly useful for entry to mid-career professionals in roles similar to the following:

    Analysts – Ideal for professionals working in analytics roles in healthcare or industries adjacent to healthcare, such as insurance, pharmaceuticals, or biotech.

    Mid-Level Managers – Ideal for professionals on the executive track who have quantitative responsibilities and relevant experience in a healthcare field.

    Entry-Level Professionals – Ideal for professionals just beginning their careers who are looking to develop a data foundation with applications in the healthcare industry.

    Representative roles well suited to this program include:

    • Data Analyst
    • MIS Analyst
    • Healthcare Analyst
    • Clinical Analyst
    • Business Analyst
    • Healthcare Operations Analyst
    • Hospital Research Analyst
    • Fraud Analyst
    • Healthcare Fraud Investigator
    • Financial Analyst
    • Risk Analyst

    Program Highlights

    Medical Use Case Examples

    50+ Video Lectures

    Knowledge Checks

    Learning Facilitators

    Weekly Q&A Sessions

    Program Topics

    Module 1:

    Statistical Programming Tools for Health Data Science | Accessing your data with R or Python

    Module 5:

    Logistic Regression | Using analysis to determine probabilities of health outcomes

    Module 2:

    Data Wrangling | Preparing your data for analysis

    Module 6:

    Elements of Machine Learning | Using data to create artificial intelligence models for predictions and decision-making

    Module 3:

    Visualization of Healthcare Data | Presenting your data to facilitate communication

    Module 7:

    Bayesian Analysis | Using analysis to determine the probabilities of beliefs and hypotheses

    Module 4:

    Linear Regression | Using analysis to estimate relationships between variables

    Module 8:

    Network Analysis | Using analysis to reveal interdependencies and interrelationships between activities and events


    Program Faculty


    Upon successful completion of the program, participants will be awarded a verified digital diploma by Emeritus Institute of Management, in collaboration with MIT Sloan, Columbia Business School Executive Education & Tuck School of Business at Dartmouth.