The Biological Data Sciences Concentration tackles a diverse set of biological questions–ranging from medicine, to genomics, to physiology, to pharmacology, to neuroscience, to ecology, and evolution–using recent tools and advances in mathematics and computation–specifically machine learning, statistical data sciences, and informatics. This concentration leverages new and developing courses within CaSB and across UCLA and will greatly aid students aiming to go directly into industry–biotech, pharmaceuticals, and more–as well as computational biology graduate school. This concentration has a strong focus and deep integration with life sciences.
1. COM SCI CM121: Introduction to Bioinformatics (4)
2. COM SCI 180: Introduction to Algorithms and Complexity (4)
3. COM SCI M146: Introduction to Machine Learning (4) OR STATS 161: Introduction to Pattern Recognition and Machine Learning (4) OR STATS 101C: Introduction to Statistical Models and Data Mining (4)
4 & 5. Two elective courses chosen from list below:
|C&S BIO M186/COM SCI CM186/COM SCI CM286/BIOENGR CM186/BIOENGR CM286/EE BIOL M178||Computational Systems Biology: Modeling and Simulation of Biological Systems (5)|
|COM SCI 111||Operating Systems Principles (5)|
|COM SCI 112||Modeling Uncertainty in Information Systems (4)|
|COM SCI 117||Computer Networks: Physical Layer (4)|
|COM SCI 118||Computer Network Fundamentals (4)|
|COM SCI CM122/CHEM CM160B||Algorithms in Bioinformatics (4)|
|COM SCI CM124/HUM GEN CM124||Machine Learning Applications in Genetics (4)|
|COM SCI 130||Software Engineering (4)|
|COM SCI 131||Programming Languages (4)|
|COM SCI 132||Compiler Construction (4)|
|COM SCI 133||Parallel and Distributed Computing (4)|
|COM SCI 143||Database Systems (4)|
|COM SCI 145||Introduction to Data Mining (4)|
|COM SCI M151B/EC ENGR M116C||Computer Systems Architecture (4)|
|COM SCI 152B||Digital Design Project Laboratory (4)|
|COM SCI 161||Fundamentals of Artificial Intelligence (4)|
|COM SCI 170A||Mathematical Modeling and Methods for Computer Science (4)|
|COM SCI 174A||Introduction to Computer Graphics (4)|
|COM SCI 181||Introduction to Formal Languages and Automata Theory (4)|
|MATH 151B||Applied Numerical Methods (4)|
|MATH 184||Enumerative Combinatorics (4)|
|STATS 101B||Introduction to Design and Analysis of Experiment (4)|
|STATS 101C||Introduction to Statistical Models and Data Mining (4)|
|STATS 130||Getting Up to Speed with SPSS, Strata, SAS, and R (4)|
|STATS 131||Python and Other Technologies for Data Analysis (4)|
|STATS 201A||Research Design, Sampling, and Analysis (4)|
|STATS 201B||Statistical Modeling and Learning (4)|
|STATS 201C||Advanced Modeling and Inference (4)|
Students in the Biological Data Sciences Concentration must also complete:
- Computer Science 32
Concentration Course Planning
When planning Major coursework, students must be mindful of pre-requisites. Some courses for the Biological Data Sciences Concentration have additional pre-requisites that are not part of the CaSB Major or Pre-Major curriculum. Always check the Registrar’s Course Descriptions for updated requisites. Additionally, students must be mindful of when classes are offered (i.e., which quarters). Students should check the Schedule of Classes for updated course offerings.
It is recommended that students meet with their Departmental Counselor regularly to plan out Major coursework.
Other Important Information
All Concentration courses must be taken for a letter grade.
Students must achieve a C or better in each Concentration course in order for that course to apply towards Major requirements. Students who receive a C- or below in a Major course must either repeat the course or petition to have the lower grade count for the major.
Students are subject to any requirement changes in the Pre-Major, Major, and Concentration until they are officially admitted to the Major.