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STAT 385

Elementary Statistical Techniques for Machine Learning and Big Data

Math, Statistics, & Comp Scnc

3 credit hoursUsually offered: Fall, Spring
Avg GPA
3.01
Easiness
2.70
Total regs
310

Grade Distribution

Based on all available term-level grade data for this course

Avg GPA
3.01
A
37.4%
116
B
42.9%
133
C
9.7%
30
D
3.2%
10
F
1.9%
6
W
4.8%
15
Visual total
310
Other
0
Total regs
310

Quick Insights

Avg GPA
3.01
Easiness
2.70
Pass rate
93.2%
Using A, B, C, D as passing
Withdrawal rate
4.8%
Based on visible distribution
Most common grade
B
Students counted
310
A through F plus W

STAT 385 by Professor

Ranked highest to lowest GPA — using A, B, C, D, and F outcomes only

#1
Zhong, Pingshou
3.34
RMP
1.8
Graded
119
Total regs
122
#2
Embers, Dale Gene
3.29
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Best professor lead
Zhong, Pingshou

Top GPA signal with 119 grades.

Avg GPA 3.34Open profile →
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About This Class

Sampling algorithms; nonparametric tests; data mining: classification, clustering, LASSO, cross-validation, Principle Component Regression; and big data analysis focus on R-package. Course Information: Extensive computer use required. Prerequisite(s): Grade of C or better in STAT 382 ; and consent of the instructor. Students in the BS in Data Science may satisfy the prerequisite with grade of C or better in IDS 462 instead of STAT 382 .

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RMP
4.4
Graded
51
Total regs
53
#
Professor
Avg GPA
RMP
Graded
Total regs
  • 1
    Zhong, Pingshou
    3.34
    1.8
    119
    122
  • 2
    Embers, Dale Gene
    3.29
    4.4
    51
    53
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