Spring 2019
STAT C100 109 - DIS 109
Principles & Techniques of Data Science
Current Enrollment
Total Open Seats:
0
Enrolled: 0
Waitlisted: 0
Capacity: 0
Waitlist Max: 0
No Reserved Seats
Hours & Workload
1 hours of instructional experiences requiring special laboratory equipment and facilities per week, 3 hours of instructor presentation of course materials per week, 7 hours of outside work hours per week, and 1 hours of the exchange of opinions or questions on course material per week.
Other classes by Ananth Agarwal
Course Catalog Description
In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.
Rules & Requirements
Repeat Rules
Course is not repeatable for credit.
Reserved Seats
Current Enrollment
No Reserved Seats