Spring 2020
STAT 102 104L - LAB 104L
Formerly DATA C102
Data, Inference, and Decisions
Current Enrollment
Total Open Seats:
1
Enrolled: 26
Waitlisted: 0
Capacity: 27
Waitlist Max: 7
No Reserved Seats
Hours & Workload
3 hours of instructor presentation of course materials per week, 7 hours of outside work hours per week, 1 hours of instructional experiences requiring special laboratory equipment and facilities per week, and 1 hours of the exchange of opinions or questions on course material per week.
Other classes by Ashley Chien
Course Catalog Description
This course develops the probabilistic foundations of inference in data science, and builds a comprehensive view of the modeling and decision-making life cycle in data science including its human, social, and ethical implications. Topics include: frequentist and Bayesian decision-making, permutation testing, false discovery rate, probabilistic interpretations of models, Bayesian hierarchical models, basics of experimental design, confidence intervals, causal inference, Thompson sampling, optimal control, Q-learning, differential privacy, clustering algorithms, recommendation systems and an introduction to machine learning tools including decision trees, neural networks and ensemble methods.
Rules & Requirements
Requisites
- Mathematics 54 or Mathematics 110 or Statistics 89A or Physics 89 or both of Electrical Engineering and Computer Science 16A and Electrical Engineering and Computer Science 16B; Statistics/Computer Science C100; and any of Electrical Engineering and Computer Science 126, Statistics 140, Statistics 134, Industrial Engineering and Operations Research 172. Statistics 140 or Electrical Engineering and Computer Science 126 are preferred.
Credit Restrictions
Students will receive no credit for STAT 102 after completing STAT 102. A deficient grade in STAT 102 may be removed by taking STAT 102.
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats