The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events Specialist
Transformative data analytical skills for the healthcare industry
February 24, 2022
8 weeks, online
4-6 hours per week
US$1,950 or get US$195 off with a referral
Our participants tell us that taking this program together with their colleagues helps to share common language and accelerate impact.
We hope you find the same. Special pricing is available for groups.
The benefit of learning together with your friend is that you keep each other accountable and have meaningful discussions about what you're learning.
Courtlyn
Promotion and Events SpecialistBased on the information you provided, your team is eligible for a special discount, for Data Science in Healthcare starting on February 24, 2022 .
We’ve sent you an email with enrollment next steps. If you’re ready to enroll now, click the button below.
Have questions? Email us at group-enrollments@emeritus.org.Note: This course requires mandatory moderate knowledge of some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics. Take a self-assessment test to verify your readiness for the math and Python content in this program.
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.
In this program, you will learn to:
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:
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:
Prerequisites: This course requires mandatory moderate knowledge of some type of analysis coding tools (such as SaS, SPSS, or R), college-level mathematics, and statistics. Students can take the self-assessment test to verify their math skills prior to commencement of the course. You can view sample questions and answers by clicking here.
Medical Use Case Examples
50+ Video Lectures
Knowledge Checks
Learning Facilitators
Weekly Q&A Sessions
CHRISTIAN DARABOS, PhD
Eugene Demidenko, PHD
Todd A. MacKenzie, PHD
James O’Malley, PHD
RAMESH YAPALPARVI, PhD
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.
Upon successful completion of the program, participants will receive a verified digital certificate of participation from Dartmouth QBS.
Download BrochureYour 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.
Flexible payment options available.
