Core Coursework
The core coursework of the Master of Science degree in Health Data Science builds a firm foundation in programming, biostatistics, and classification algorithms including machine learning, and builds the skills to apply these methods with real world data.
The Health Data Science curriculum also includes an optional 3-unit capstone that challenges students to refine their data science skills while working on a real-world applied data project. Beyond showcasing technical knowledge, the capstone experience also provides the training in critical skills needed for professional success, such as scientific writing, presentation skills, and the ability to translate data science findings to non-data science stakeholders.
- QBS 108 Machine Learning
- QBS 119 Applied Biostatistics OR QBS 120 Foundations of Biostatistics I: Statistical Theory
- QBS 121 Foundations of Biostatistics II: Statistical Modeling
- QBS 177 Methods of Statistical Learning for Big Data
- QBS 194 Biostatistics Journal Club (0.5 unit) OR QBS 270 Biostatistics Journal Club (0.5 unit)
One of the following*:
- QBS 185.5 QBS MS Capstone Preparation Course (0.5 unit course taken in winter term)
- QBS 185 QBS MS Capstone (3 units)
- QBS 700 Responsible and Ethical Conduct of Research does not count toward the units of coursework required for the MS degree.
- Students who matriculate Fall 2021 and beyond may not take more than 4 units of coursework per quarter unless approved by QBS administration and leadership.
Elective Courses
Through the Health Data Science elective course options, students can dive deeper into advanced concepts such as applied epidemiology, biostatistics, machine learning, molecular biology, and healthcare systems.
- QBS 110 Integrative Biomedical Sciences Seminar (0.5 unit)
- QBS 110.5 Integrative Biomedical Sciences Seminar Project (0.5 unit)
- QBS 119 Biostatistics I: Applied Biostatistics or QBS 120 Foundations of Biostatistics I: Statistical Theory for the Quantitative Biomedical Sciences
- QBS 122 Biostatistics III: Modeling Complex Data
- QBS 123 Biostatistics Consulting Lab (0.5 unit)
- QBS 124 Advanced Biomedical Data Analysis
- QBS 125 Biomedical Informatics
- QBS 126 Analysis of Densely Collected Longitudinal Data
- QBS 131 Foundations of Epidemiology II: Theory and Methods
- QBS 132 Molecular Biologic Markers in Human Health Studies
- QBS 132.5 Molecular Biologic Markers in Human Health Studies Lab (0.5 unit)
- QBS 133 Clinical Epidemiology
- QBS 136 Applied Epidemiological Methods
- QBS 139 Advanced Methods in Health Services Research
- QBS 140 Decision & Cost Effectiveness Analysis
- QBS 146 Foundations of Bioinformatics I
- QBS 147 Genomics: From Data to Analysis
- QBS 192 Health Informatics
- QBS 193 Independent Journal Club (0.5 unit)
- QBS 194 Biostatistics Journal Club (0.5 unit)
- QBS 195 Independent Study
- QBS 270 QBS Journal Club: Biostatistics (0.5 unit)
- QBS 270 QBS Journal Club: Bioinformatics (0.5 unit)
- QBS 270 QBS Journal Club: Epidemiology (0.5 unit)
- QBS 271 Advanced Epidemiology Journal Club (0.5 unit) (not offered every year)
- COSC 122 3D Digital Modeling
- COSC 124 Computer Animation: The State of the Art
- COSC 169 Topics in Computer Systems
- COSC 189 Topics in Applied Computer Science
- COSC 258 Operating Systems
- COSC 267 Introduction to Human Computer Interaction
- COSC 274 Machine Learning and Statistical Data Analysis
- COSC 276 Artificial Intelligence
- COSC 278 Deep Learning
- COSC 281 Principles of Robot Design and Programming
- PH 114 Contemporary Issues in Biotechnology: The Practitioner's Perspective (0.5 unit)
- PH 115/QBS 144 Value and Resource Allocation
- PH 122/QBS 129 Survey Methods
- PH 125/QBS 13 Introduction to Qualitative Methods for Public Health & Healthcare Studies (0.5 unit)
- PH 128: Health Systems (0.5 unit)
- PH 131/QBS 138 Patient Centered Health Communication(0.5 unit)
- PH 140 Applying Health Statistics (1.5 units)
- PH 151/QBS 145 Environmental Health Sciences and Policy(0.5 unit)
- PH 154/QBS 141 Determinants of Health Inequities (0.5 unit)
- ENGM 179.1 Strategy (0.5 unit)
- ENGM 181 Marketing
- ENGM 182 Data Analytics
- ENGM 183 Operations Management
- ENGM 184 Introduction to Optimization Methods
- ENGM 188 Law for Technology and Entrepreneurship
- ENGM 189.1 Medical Device Commercialization (0.5 unit)
- ENGM 189.2 Medical Device Development (0.5 unit)
- ENGM 190 Platform Design, Management, and Strategy
- ENGM 191 Product Design and Development
- ENGM 204 Data Analytics Project Lab
- ENGS 102 Game-theoretic Design, Learning and Engineering
- ENGS 162 Basic Biological Circuit Engineering
- ENGS 262 Advanced Biological Circuit Engineering
- No more than 3 journal clubs or 1.5 units of journal club courses may count towards elective credit.
- No more than 1 independent study may count towards elective credit.
- No more than 1 independent journal club may count towards elective credit.
- Seek administrative approval for non-listed electives. Email Monica Espinoza for additional information.
Learning Objectives
- Demonstrate core competency in bioinformatics, biostatistics, and epidemiology
- Demonstrate skills required to pursue careers in data science as applied in biomedical settings, including:
- Ability to apply statistical methodologies on large datasets (“big data”)
- Correct use of statistical models that consider data design, encoding, and organization
- Ability to develop and apply algorithms for processing and analysis of biomedical data
- Ability to apply machine learning algorithms
- Ability to produce effective data visualizations which narrate central points for presentation and reporting.
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