Overview of data science key concepts and the use of tools for data retrieval, analysis, visualization, and reproducible research. Topics include an introduction to inference and prediction, principles of measurement, missing data, and notions of causality, statistical traps, and concepts in data ethics and privacy. Case studies illustrate the importance of domain knowledge.

Prerequisites: PSTAT 120A; CS 9 or CS 16; and Math 4A, all with letter grade C or better.

4

Units

Optional

Grading

1, 2, 3

Passtime

None

Level Limit

Letters and science

College
T B A
No info found
PHELP1517
T R
14:00 PM - 14:50 PM
0 / 25

PHELP1517
T R
15:00 PM - 15:50 PM
0 / 25

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Spring 2024 . T B A
BUCHN1940
T R
09:30 AM - 10:45 AM
Fall 2024 . Abuzaid A H
NH 1006
T R
17:00 PM - 18:15 PM
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PSTAT 100 Marzban E P Spring 2024 Total: 95
PSTAT 100 Baracaldo Lan Fall 2023 Total: 104
PSTAT 120B
0 / 100 Enrolled
Probability and Statistics
T B A
M T W R
14:00 PM - 15:05 PM
40.0% A
PSTAT 120C
0 / 100 Enrolled
Probability and Statistics
Katie Coburn 3.3
M T W R
14:00 PM - 15:05 PM
45.8% A
PSTAT 120B
0 / 100 Enrolled
Probability and Statistics
T B A
M T W R
14:00 PM - 15:05 PM
40.0% A
PSTATW 120A
0 / 150 Enrolled
Probability and Statistics
T B A
M T W R
09:30 AM - 10:35 AM
32.0% A
PSTAT 120A
0 / 100 Enrolled
Probability and Statistics
Puja Pandey 4.3
M T W R
14:00 PM - 15:05 PM
30.9% A
PSTAT 126
0 / 50 Enrolled
Regression Analysis
Laura Baracaldo 1.8
M T W R
11:00 AM - 12:05 PM
40.0% A