Data Science, Analytics, AI and Computational Methods
teaches knowledge and skills of data analytics and computational approaches in healthcare. Students must demonstrate competency in understanding and applying
-
high-level mathematical analysis of large data sets,
-
predictive and prescriptive analytic methods and tools,
-
Natural Language Processing (NLP),
-
optimal clinical knowledge management principles at healthcare organizations, and
-
information visualization.
Students with interest in extended training in Big Data analytics may concurrently take courses in the School of Computing’s Big Data Certificate program. While students are not expected to take every course, they are expected to gain specialized knowledge in key computational methodologies. For example, trainees may specialize in NLP and predictive analytics, where the courses blend skills in algorithm development with practical knowledge of applications of NLP in biomedicine and healthcare, biomedical knowledge resources, and characteristics of biomedical text. This enables trainees to implement NLP solutions.
Suggested Courses
BMI 6015 - Applied Machine Learning |
CS 6300 - Introduction to Data Science, Artificial Intelligence |
CS 6390 - Information Extraction from Text |
BMI 6114 - Deep Learning in Biomedicine |
CS 6630 - Visualization |
CS 6936 - Learning Semantics for NLP |
BMI 6203 - Clinical Database Design |
IS 6910 - Data Mining in Healthcare |
CS 5300 - Artificial Intelligence |
BMI 6115 - Biomedical Text Processing |
IS 6483 - Advanced Data Mining |
MATH 5080 - Statistical Inference |
BMI 6016 - Biomedical Data Wrangling |
Practicum
Students have the opportunity to apply for practicums to gain hands-on experience by working a semester with the ReImagineEHR team or a Sociotechnical expertise on an existing project. Must be coordinated with the team/expertise director before registering:
-
ReImagine EHR - Ken Kawamoto
