# B.S. Electives & Pathways

Within the B.S. in Data Science, students expand their expertise by taking three higher-level courses (>100) from the offerings in Statistics, CS, Mathematics, or the following pre-approved list. Each course must be at least 3 units, and the courses should be selected from the offerings of at least two different departments.

With advisor approval, courses other than those listed or offered by the sponsoring departments may be used to fulfill part of the elective requirement. Courses must provide skills relevant to the Data Science degree and not overlap courses in the student's program. Depending on a student’s interests, these courses may be in fields such as biology, economics, electrical engineering, industrial engineering, or medicine. To initiate this process, please fill out the Elective Approval Form and have a conversation with your program advisor.

##
CME

- Introduction to Numerical Methods for Engineering (CME 206)
- Software Development for Scientists and Engineers (CME 211)
- Numerical Linear Algebra (CME 302)

##
Computer Science

- Object Oriented Systems Design (CS 108)
- Principles of Computer Systems (CS 110)
- Operating Systems and Systems Programming (CS 140)
- Compilers (CS 143)
- Logic and Automated Reasoning (CS 157)
- Design and Analysis of Algorithms (CS 161)
- Software Project (CS 194)
- Artificial Intelligence: Principles and Techniques (CS 221)
- Introduction to Robotics (CS 223A)
- Experimental Robotics (CS 225A)
- Probabilistic Graphical Models: Principles and Techniques (CS 228)
- Machine Learning (CS 229)
- Program Analysis and Optimization (CS 243)
- Mining Massive Data Sets (CS 246)
- Interactive Computer Graphics (CS 248)

##
Economics

- Advanced Topics in Econometrics (ECON 102C)
- Introduction to Financial Economics (ECON 140)
- Game Theory and Economic Applications (ECON 160)
- Experimental Economics (ECON 179)

##
EE

- The Fourier Transform and Its Applications (EE 261)
- Introduction to Linear Dynamical Systems (EE 263)
- Introduction to Statistical Signal Processing (EE 278)
- Computer Systems Architecture (EE 282)
- Convex Optimization I (EE 364A)
- Convex Optimization II (EE 364B)

##
Management Science & Engineering

- Probabilistic Analysis (MS&E 220)
- Simulation (MS&E 223)
- Introduction to Stochastic Control with Applications (MS&E 251)
- Topics in Social Data (MS&E 334)

##
Math

- Applied Matrix Theory (Math 104)
- Functions of a Complex Variable (Math 106)
- Graph Theory (Math 107)
- Introduction to Combinatorics and its Applications (Math 108)
- Linear Algebra and Matrix Theory (Math 113)
- Introduction to Scientific Computing (Math 114)
- Functions of a Real Variable (Math 115)
- Complex Analysis (Math 116)
- Partial Differential Equations I (Math 131P)
- Partial Differential Equations II (Math 132)
- Stochastic Processes (Math 136)
- Basic Probability and Stochastic Processes with Engineering Applications (Math 158)
- Discrete Probabilistic Models (Math 159)
- Fundamental Concepts of Analysis (Math 171)
- Lebesgue Integration and Fourier Analysis (Math 172)

##
Philosophy

- Metalogic (Phil 151)

##
Statistics

- Mathematics of Sports (Stats 100)
- Data Mining and Analysis (Stats 202)
- Applied Multivariate Analysis (Stats 206)
- Introduction to Time Series Analysis (Stats 207)
- Introduction to the Bootstrap (Stats 208)
- Statistical Methods for Group Comparisons and Causal Inference (Stats 209)
- Statistical Methods in Biology (Stats 215)
- Introduction to Statistical Learning (Stats 216)
- Introduction to Stochastic Processes I (Stats 217)
- Introduction to Stochastic Processes II (Stats 218)
- Stochastic Processes (Stats 219)
- Statistical Methods for Longitudinal Research (Stats 222)
- Statistical Methods in Finance (Stats 240)
- Bayesian Statistics I (Stats 270)

##
Individualized Concentration Option

Some students may choose to pursue an individualized concentration in place of their electives. The Data Science program has designed three such paths for students who want to pursue their interests in fields where applied mathematics and statistical analysis is utilized. Declared Data Science B.S. majors are not required to choose a path.

##
Biology Path

The biology path is intended for students interested in pursuing a graduate degree in the bioinformatics and biostatistics related fields.

Take three courses from the Biology core:

- BIO 82
- BIO 83
- BIO 84
- BIO 85
- BIO 86

OR Take **two classes from the above** biology core and take **one **of the following:

- BIO 104
- BIO 118
- BIO 133
- BIO 144
- BIO 183
- BIO 187
- BIO 230

Other Notes:

- Honors students should take BIO 113, BIO 114, and STATS 155.
- Bio/Stats 141 can replace STATS 191 (Introduction to Applied Statistics) or STATS 203 (Introduction to Regression Models and Analysis of Variance).

##
Engineering Path

Take one course from the following:

- Math 106
- Math 108
- Math 116
- Math 118
- Math 132
- Math 174
- Phil 151

Take two courses from the following:

- ENGR 15
- ENGR 20
- ENGR 25B
- ENGR 30
- ENGR 40
- ENGR 50
- ENGR 105

##
Statistics Path

Take the core courses in Math, CS, MS&E, and Stats in addition to the following courses:

- Stats 217 (3 units)
- An advanced CS course, such as CS 246 (3 units)
- An advanced MS&E course, such as MS&E 220 or 223 (3 units)

Choose three statistics path electives (below), which replace the major electives (9 units):

- Stats 202
- Stats 206
- Stats 207
- Stats 208
- Stats 216
- Stats 219
- Stats 270

(85 units)