Overview and use of data science tools in R and/or Python for data retrieval, analysis, visualization, reproducible research, and automated report generation. Case studies will illustrate the practical use of these tools.

Prerequisites: PSTAT 131 or PSTAT 231 or Computer Science 165B; and Computer Science 9 or Computer Science 16. A minimum letter grade of C or better must be earned in each course.

4

Units

Optional

Grading

1, 2, 3

Passtime

None

Level Limit

Letters and science

College
T B A
No info found
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Spring 2024 . Baracaldo Lan
BUCHN1920
M W
09:30 AM - 10:45 AM
Winter 2025 . Oh Sang-Yun
SH 1430
T R
15:30 PM - 16:45 PM
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PSTAT 234 Oh Sang-Yun Winter 2025 Total: 8
PSTAT 234 Coburn K M Fall 2024 Total: 20
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PSTAT 234
24 / 20 Full
Statistical Data Science
Katie Coburn 3.3
T R
15:30 PM - 16:45 PM
84.6% A
PSTAT 210
16 / 20 Enrolled
Measure Theory for Probability
Alexander Shkolnik 3.7
T R
11:00 AM - 12:15 PM
80.3% A
PSTAT 213A
31 / 30 Full
Introduction To Probability Theory And Stochastic Processes
Tomoyuki Ichiba 3.8
M W
12:30 PM - 13:45 PM
63.8% A
PSTAT 220A
35 / 40 Enrolled
Advanced Statistical Methods
Alexander Franks 4.8
M W
14:00 PM - 15:15 PM
73.0% A
PSTAT 223A
13 / 20 Enrolled
STOCHASTIC CALCULUS AND APPLICATIONS
Jean-Pierre Fouque 4.0
M W
11:00 AM - 12:15 PM
88.3% A
PSTAT 227
14 / 30 Enrolled
Bootstrap and Resampling Methodology
Andrew Carter 3.1
T R
09:30 AM - 10:45 AM
100.0% A
PSTAT 231
15 / 20 Enrolled
Introduction to Statistical Machine Learning
Laura Baracaldo 1.8
T R
17:00 PM - 18:15 PM
77.4% A