
AIM-AHEAD Connect Course Spotlight
VADSTI Data Science Training Program
- Course Directors:
- Toufeeq Syed, PhD
- John Kwagyan, PhD
- Level: No prerequisites for research knowledge topics. A basic undergraduate knowledge of algebra and probability is recommended for content knowledge topics.
- Format: Modular, asynchronous,
self-paced - Total Modules: 9
Overview: This training series introduces foundational data science concepts, Python programming, and core analytic methods used in clinical, biomedical, and genomic research. Participants gain hands-on experience with statistics, data exploration, visualization, and predictive analytics through recorded lectures and assignments from Howard University’s Virtual Applied Data Science Training Institute.
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AIM-AHEAD News
AIM-AHEAD DICB Awardee Launches aiMATCH Leukemia Patient Outcome Prediction Data Challenge
The aiMATCH team is preparing to launch the new Leukemia Patient Outcome Prediction Challenge in partnership with AIM-AHEAD, The University.. Continue reading
Applications Now Open for the AIM-AHEAD FHIR Collaborative Training Program
Find out more about the Call for Applications for the latest AIM-AHEAD Training Program... Continue reading
Unlock New Learning Opportunities: Explore the Enhanced Course Catalog on AIM-AHEAD Connect
Find out more about several new courses available on AIM-AHEAD Connect. .. Continue reading
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The National Institutes of Health’s AIM-AHEAD program was established to create mutually beneficial and coordinated partnerships to empower researchers and communities across the United States in the development of AI/ML models and enhance the capabilities of this emerging technology, beginning with electronic health record (EHR) data. Read More
**Please note that, consistent with NIH practice and applicable law, funded programs may not use the race, ethnicity, or sex of prospective program participants or faculty as an eligibility or selection criteria. The race, ethnicity, or sex of candidates will not be considered by AIM-AHEAD in the application review process or when making funding decisions.**
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