EXECUTIVE EDUCATION

Data Science in Healthcare (Online Certificate Program)

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Course Dates

STARTS ON

March 18, 2021

Course Duration

DURATION

8 weeks, online
4-6 hours per week

Course Duration

PROGRAM FEE

US$2,600

Course Information Flexible payment available
Course Information Special group enrollment pricing

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

SOURCE: FORBES

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.

SOURCE: HEALTHTECH

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

Program Leaders

Weekly Q&A Sessions

Program Topics

Module 1:

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

Module 2:

Data Wrangling | Preparing your data for analysis

Module 3:

Visualization of Healthcare Data | Presenting your data to facilitate communication

Module 4:

Linear Regression | Using analysis to estimate relationships between variables

Module 5:

Logistic Regression | Using analysis to determine probabilities of health outcomes

Module 6:

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

Module 7:

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

Module 8:

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

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
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Program Faculty

CHRISTIAN DARABOS, PhD

Lecturer, Biomedical Data Science & Assistant Director of Research Informatics

At Dartmouth, Dr. Darabos manages and coordinates research projects across departments. His research focuses on biologically inspired heuristics applied to modeling complex dynamical systems. He designs and implements intelligent machine learning-based techniques and tools to large-scale biomedical datasets. His projects have included the study of complex genetic interactions in human diseases.

Eugene Demidenko, PHD

Professor of Biomedical Data Science, Professor of Community and Family Medicine, Adjunct Professor of Mathematics & Adjunct Professor of Engineering

At Dartmouth, Dr. Demidenko teaches statistics to undergraduate students and graduate students in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. He is an expert in theoretical and applied statistics, such as epidemiology and biostatistics, statistical analysis of images, mixed models, nonlinear regression, mathematical tumor regrowth modeling, and partial differential equations with mixed boundary conditions.

Todd A. MacKenzie, PHD

Professor of Biomedical Data Science, Professor of Medicine & Professor of The Dartmouth Institute

At Dartmouth, Dr. MacKenzie teaches biostatistics in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. His research focuses on the use of data science to help clinical and health services researchers across a spectrum of disciplines and specialties. Many of his projects involve comparative effectiveness, clinical trials, association studies, and prediction modeling. He is particularly interested in survival analysis, causal inference, and the decline in American longevity.

James O’Malley, PHD

Professor of Biomedical Data Science and of The Dartmouth Institute for Health Policy and Clinical Practice & Adjunct Professor of Mathematics and Computer Science

At Dartmouth, Dr. O’Malley teaches biostatistics at The Dartmouth Institute and the Biomedical Data Sciences program at the Geisel School of Medicine. His research focuses on applying statistical methods to health policy and health services to improve patient care. He is an expert in statistical inference for social networks, comparative effectiveness, Bayesian statistics, vascular surgery, cardiology, the design and evaluation of medical device clinical trials, and the relationship between health and social networks. He collaborates with physicians, sociologists, health economists, health services researchers, and epidemiologists.

RAMESH YAPALPARVI, PhD

Course Director, Master’s Program in Healthcare Data Science

At Dartmouth, Dr. Yapalparvi is the course director for the graduate program in healthcare data science, data mining, and data visualization in the Quantitative Biomedical Sciences program at the Geisel School of Medicine. In addition to his academic work, he is senior manager of data science for Optum/UnitedHealth Group, where he manages a team of data scientists to identify and prevent fraud, waste, and abuse. He also has worked with orthopedic surgeons to develop tools for clinical interventions and predictive analytics tools for patient reported outcomes data.

CHRISTIAN DARABOS, PhD

Lecturer, Biomedical Data Science & Assistant Director of Research Informatics

Eugene Demidenko, PHD

Professor of Biomedical Data Science, Professor of Community and Family Medicine, Adjunct Professor of Mathematics & Adjunct Professor of Engineering

Todd A. MacKenzie, PHD

Professor of Biomedical Data Science, Professor of Medicine & Professor of The Dartmouth Institute

James O’Malley, PHD

Professor of Biomedical Data Science and of The Dartmouth Institute for Health Policy and Clinical Practice & Adjunct Professor of Mathematics and Computer Science

RAMESH YAPALPARVI, PhD

Course Director, Master’s Program in Healthcare Data Science

Certificate

Certificate

Upon successful completion of the program, participants will receive a verified digital certificate of participation from Dartmouth QBS. Your digital certificate will be issued in your legal name and emailed to you at no additional cost, upon completion of the program, per the stipulated requirements. All certificate images are for illustrative purposes only and may be subject to change at the discretion of Dartmouth.

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