Chemistry and chemical biology majors who choose a concentration in computational chemistry must complete the following curriculum in lieu of the Allied Subjects requirement. Students must declare the concentration by the end of their junior year. Complete the Chemistry Concentration Declaration Form and submit it to your adviser for approval.
Chem 121 (Introduction to Computational Chemistry)
One course from each of the following three areas:
1. Programming
- Engineering 7 (Introduction to Computer Programming for Scientists and Engineers)
- Computer Science 61A (The Structure and Interpretation of Computer Programs)
- Computer Science 88 (Computational Structures in Data Science)
- Math 124 (Programming for Mathematical Applications)
2. Mathematical, computational, and statistical methods
- Data 8 (Foundations of Data Science)
- Computer Science 61B (Data Structures)
- Computer Science 61C (Machine Structures)
- Computer Science 70 (Discrete Mathematics and Probability Theory)
- Computer Science 170 (Efficient Algorithms and Intractable Problems)
- Computer Science 189 (Introduction to Machine Learning)
- Math 55 (Discrete Mathematics)
- Math 110 (Linear Algebra)
- Math 121A (Mathematical Tools for the Physical Sciences)
- Math 128A (Numerical Analysis)
- Physics 89 (Introduction to Mathematical Physics)
- Statistics 134 (Concepts of Probability)
- Statistics 140 (Probability for Data Science)
- Statistics 150 (Stochastic Processes)
3. Advanced methods and applications
- Bioengineering 143 (Computational Methods in Biology)
- Bioeng/CMPBio C131 (Introduction to Computational Molecular and Cell Biology)
- Chem C142 (Machine Learning, Statistical Models, & Optimization for Molecular Problems)
- Chem 191 (Quantum Information Science and Technology)
- Computer Science 176 (Algorithms for Computational Biology)
- Computer Science 189 (Introduction to Machine Learning)
- Data 100 (Principles and Techniques of Data Science)
- Materials Science and Engineering 215 (Computational Materials Science)
- Math 121B (Mathematical Tools for the Physical Sciences)
- Physics 188 (Bayesian Data Analysis and Machine Learning for Physical Sciences)