Data Science in Biomedicine: Exploring the UCLA Curriculum
Recent technological advancements are poised to revolutionize biomedicine. The convergence of affordable genomic sequencing and powerful deep neural networks creates unprecedented opportunities for data scientists to impact patient care directly. The availability of large datasets within healthcare systems further amplifies this potential. In response to this growing need, UCLA offers a Data Science in Biomedicine M.S. program designed to train future leaders in this interdisciplinary field. This article delves into the curriculum and unique aspects of this program.
Program Overview
The Data Science in Biomedicine M.S. program at UCLA distinguishes itself by combining foundational data science training with cutting-edge biomedical applications. Students acquire the technical expertise to understand the inner workings of novel biomedical data science methods and develop the skills to critically analyze the latest biomedical research. This comprehensive training equips students to create innovative methods that address pressing healthcare challenges.
The program is delivered online, offering flexibility for students to balance their studies with existing work and personal commitments. The faculty director oversees student advising and the program's academic management, providing support for any academic or personal concerns.
Curriculum Structure
To earn the Data Science in Biomedicine M.S. degree, students must complete a minimum of 36 units, equivalent to nine courses, at the 200-level. The curriculum comprises five core courses (20-24 units), a capstone course (4-8 units), and four elective courses at the 200-level.
Core Courses
The core curriculum provides a solid foundation in essential data science principles and their application to biomedicine. The core consists of the following courses: Foundations of Data Science, Machine Learning Applications in Biomedicine, Advanced Machine Learning Applications in Biomedicine, Data Science for Medical Imaging, and Applied Data Science in Genomics and Biomedicine. DSB 200 (Foundations of Data Science) is typically taken as the first course in the program during the fall quarter. However, students with demonstrable equivalent knowledge may be eligible for a waiver.
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Foundations of Data Science: (4 units) This course lays the groundwork for understanding data science principles.
Machine Learning Applications in Biomedicine: (4 units) An introduction to machine learning analysis of biomedical data, with a focus on formulating interdisciplinary problems as computational problems and then solving those problems using machine learning techniques.
Advanced Machine Learning Applications in Biomedicine: (4 units) Statistical models for analysis of Biomedical data that captures the structure of the data and accounts for the constraints. Topics include Bayesian models, Probabilistic Graphical models, Deep Learning, Time series, Dynamical systems, Stochastic processes, Scalable inference (gradient descent, SGD, EM, MCMC, Variational inference), Privacy-preserving inference (differential privacy, inference over encrypted data), Interpretable ML, and Fairness and Bias.
Data Science for Medical Imaging: (4 units) This course explores the application of data science techniques to medical imaging.
Applied Data Science in Genomics and Biomedicine: (4 units) Introduction to computational approaches in bioinformatics, genomics, computational genetics, electronic health records, medical images and other analysis of biomedical data. Topics include emerging methods and their applications to genomics, epigenomics, population genetics, analysis of health records within medical systems, medical imaging, and genomic technologies. Computational techniques include those from statistics and computer science.
Elective Courses
Students choose four elective courses to further specialize in areas of interest within data science and biomedicine. These electives provide opportunities to delve deeper into specific applications and methodologies.
Capstone Course
The capstone requirement is fulfilled by successfully completing one of the designated capstone courses (DSB 218, 219, or 220) with a grade of "B" or better. These courses involve a major project that provides in-depth exposure to real-world tasks expected in the field.
- Data Science Algorithms in Biomedicine: (4 units) Development and application of algorithmic approaches to problems in biomedicine, with focus on formulating interdisciplinary problems as computational problems and then solving these problems using algorithmic techniques. Design, analysis, optimization, and implementation of algorithms. Topics include string algorithms in genomics and scalable machine learning algorithms applied to medical data.
The project requires the development and application of data science methods and techniques to address a specific problem in medicine. A faculty member supervises the project to ensure adherence to the program's rigorous academic standards.
Program Structure and Completion
The program is designed for part-time students, with most taking one course per quarter and completing the program in nine quarters. Approximately half of the students are expected to take courses during the summer quarter, while the other half will focus on the fall, winter, and spring quarters.
Academic Standing and Disqualification
Students must maintain a minimum cumulative grade point average of 3.00 to remain in good standing. Failure to meet this requirement or other academic standards may result in academic disqualification from the program. Other reasons for disqualification include failure of examinations, lack of timely progress toward the degree, and poor performance in core courses. Students on academic probation are subject to immediate dismissal upon the department's recommendation.
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Skills and Knowledge Gained
The Data Science in Biomedicine M.S. program equips students with foundational training in data science areas, including machine learning, statistics, data mining, and analytics. This training is combined with expertise in analyzing biomedical data sources such as electronic health records, medical images, and genomic data.
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