Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory957.0 KiB
Average record size in memory98.0 B

Variable types

Numeric6
Categorical4

Dataset

Description한국기술교육대학교 온라인평생교육원 스마트 직업훈련 플랫폼 (STEP)에 대한 과목 점수 정책과 관련된 내용을 제공합니다.
Author한국기술교육대학교
URLhttps://www.data.go.kr/data/15091100/fileData.do

Alerts

라이브 세미나 패스 점수 has constant value ""Constant
출석 콘텐츠 시간 비율 has constant value ""Constant
과목 아이디 is highly overall correlated with 누적 패스 점수High correlation
누적 패스 점수 is highly overall correlated with 과목 아이디High correlation
퀴즈 패스 점수 is highly imbalanced (99.9%)Imbalance
포럼 패스 점수 is highly imbalanced (99.9%)Imbalance
과제 패스 점수 is highly skewed (γ1 = 50.99496851)Skewed
출석 만점 점수 is highly skewed (γ1 = -29.19419508)Skewed
과목 아이디 has unique valuesUnique
누적 패스 점수 has 1406 (14.1%) zerosZeros
시험 패스 점수 has 9791 (97.9%) zerosZeros
과제 패스 점수 has 9988 (99.9%) zerosZeros
출석 패스 점수 has 9613 (96.1%) zerosZeros

Reproduction

Analysis started2023-12-12 06:24:22.197574
Analysis finished2023-12-12 06:24:28.892620
Duration6.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

과목 아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56056.195
Minimum3
Maximum407968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:28.978647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile873.95
Q14461.75
median7608.5
Q330962.25
95-th percentile319309.15
Maximum407968
Range407965
Interquartile range (IQR)26500.5

Descriptive statistics

Standard deviation101556.35
Coefficient of variation (CV)1.8116882
Kurtosis2.7011688
Mean56056.195
Median Absolute Deviation (MAD)3503.5
Skewness1.9788855
Sum5.6056195 × 108
Variance1.0313692 × 1010
MonotonicityNot monotonic
2023-12-12T15:24:29.143673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181661 1
 
< 0.1%
8507 1
 
< 0.1%
117055 1
 
< 0.1%
3125 1
 
< 0.1%
3567 1
 
< 0.1%
259279 1
 
< 0.1%
73462 1
 
< 0.1%
6271 1
 
< 0.1%
181883 1
 
< 0.1%
2922 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
3 1
< 0.1%
8 1
< 0.1%
31 1
< 0.1%
32 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
36 1
< 0.1%
37 1
< 0.1%
38 1
< 0.1%
39 1
< 0.1%
ValueCountFrequency (%)
407968 1
< 0.1%
407962 1
< 0.1%
407809 1
< 0.1%
407104 1
< 0.1%
407101 1
< 0.1%
407098 1
< 0.1%
407095 1
< 0.1%
407089 1
< 0.1%
407086 1
< 0.1%
407083 1
< 0.1%

누적 패스 점수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.1993
Minimum0
Maximum100
Zeros1406
Zeros (%)14.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:29.310848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160
median60
Q370
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation28.712256
Coefficient of variation (CV)0.47695331
Kurtosis0.40619768
Mean60.1993
Median Absolute Deviation (MAD)0
Skewness-0.79987285
Sum601993
Variance824.39362
MonotonicityNot monotonic
2023-12-12T15:24:29.498959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
60 5961
59.6%
100 1792
 
17.9%
0 1406
 
14.1%
80 647
 
6.5%
70 164
 
1.6%
90 16
 
0.2%
10 6
 
0.1%
75 2
 
< 0.1%
99 1
 
< 0.1%
5 1
 
< 0.1%
Other values (4) 4
 
< 0.1%
ValueCountFrequency (%)
0 1406
 
14.1%
2 1
 
< 0.1%
5 1
 
< 0.1%
10 6
 
0.1%
20 1
 
< 0.1%
50 1
 
< 0.1%
60 5961
59.6%
67 1
 
< 0.1%
70 164
 
1.6%
75 2
 
< 0.1%
ValueCountFrequency (%)
100 1792
 
17.9%
99 1
 
< 0.1%
90 16
 
0.2%
80 647
 
6.5%
75 2
 
< 0.1%
70 164
 
1.6%
67 1
 
< 0.1%
60 5961
59.6%
50 1
 
< 0.1%
20 1
 
< 0.1%

시험 패스 점수
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6995
Minimum0
Maximum80
Zeros9791
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:29.664562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.6552098
Coefficient of variation (CV)8.0846459
Kurtosis91.537149
Mean0.6995
Median Absolute Deviation (MAD)0
Skewness9.3413781
Sum6995
Variance31.981398
MonotonicityNot monotonic
2023-12-12T15:24:29.805316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 9791
97.9%
25 74
 
0.7%
60 71
 
0.7%
10 57
 
0.6%
50 3
 
< 0.1%
40 2
 
< 0.1%
5 1
 
< 0.1%
80 1
 
< 0.1%
ValueCountFrequency (%)
0 9791
97.9%
5 1
 
< 0.1%
10 57
 
0.6%
25 74
 
0.7%
40 2
 
< 0.1%
50 3
 
< 0.1%
60 71
 
0.7%
80 1
 
< 0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
60 71
 
0.7%
50 3
 
< 0.1%
40 2
 
< 0.1%
25 74
 
0.7%
10 57
 
0.6%
5 1
 
< 0.1%
0 9791
97.9%

퀴즈 패스 점수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9999 
10
 
1

Length

Max length2
Median length1
Mean length1.0001
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9999
> 99.9%
10 1
 
< 0.1%

Length

2023-12-12T15:24:29.983395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:24:30.116196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9999
> 99.9%
10 1
 
< 0.1%

과제 패스 점수
Real number (ℝ)

SKEWED  ZEROS 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0325
Minimum0
Maximum80
Zeros9988
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:30.241009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum80
Range80
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.2948403
Coefficient of variation (CV)39.84124
Kurtosis2796.0825
Mean0.0325
Median Absolute Deviation (MAD)0
Skewness50.994969
Sum325
Variance1.6766114
MonotonicityNot monotonic
2023-12-12T15:24:30.411805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 9988
99.9%
10 6
 
0.1%
25 2
 
< 0.1%
70 1
 
< 0.1%
80 1
 
< 0.1%
60 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 9988
99.9%
5 1
 
< 0.1%
10 6
 
0.1%
25 2
 
< 0.1%
60 1
 
< 0.1%
70 1
 
< 0.1%
80 1
 
< 0.1%
ValueCountFrequency (%)
80 1
 
< 0.1%
70 1
 
< 0.1%
60 1
 
< 0.1%
25 2
 
< 0.1%
10 6
 
0.1%
5 1
 
< 0.1%
0 9988
99.9%

포럼 패스 점수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9999 
10
 
1

Length

Max length2
Median length1
Mean length1.0001
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9999
> 99.9%
10 1
 
< 0.1%

Length

2023-12-12T15:24:30.562759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:24:30.683928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9999
> 99.9%
10 1
 
< 0.1%

라이브 세미나 패스 점수
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
10000 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10000
100.0%

Length

2023-12-12T15:24:30.810146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:24:30.931350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 10000
100.0%

출석 콘텐츠 시간 비율
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
100
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 10000
100.0%

Length

2023-12-12T15:24:31.028091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:24:31.125281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
100 10000
100.0%

출석 패스 점수
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6862
Minimum0
Maximum100
Zeros9613
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:31.211536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.28513
Coefficient of variation (CV)5.3179696
Kurtosis29.30134
Mean2.6862
Median Absolute Deviation (MAD)0
Skewness5.4888669
Sum26862
Variance204.06494
MonotonicityNot monotonic
2023-12-12T15:24:31.435593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 9613
96.1%
80 202
 
2.0%
32 100
 
1.0%
100 65
 
0.7%
70 4
 
< 0.1%
50 3
 
< 0.1%
90 3
 
< 0.1%
10 3
 
< 0.1%
60 3
 
< 0.1%
3 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
ValueCountFrequency (%)
0 9613
96.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
10 3
 
< 0.1%
20 1
 
< 0.1%
32 100
 
1.0%
50 3
 
< 0.1%
60 3
 
< 0.1%
67 1
 
< 0.1%
70 4
 
< 0.1%
ValueCountFrequency (%)
100 65
 
0.7%
90 3
 
< 0.1%
80 202
2.0%
70 4
 
< 0.1%
67 1
 
< 0.1%
60 3
 
< 0.1%
50 3
 
< 0.1%
32 100
1.0%
20 1
 
< 0.1%
10 3
 
< 0.1%

출석 만점 점수
Real number (ℝ)

SKEWED 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.888
Minimum0
Maximum100
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T15:24:31.652162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.9374
Coefficient of variation (CV)0.029406936
Kurtosis891.7805
Mean99.888
Median Absolute Deviation (MAD)0
Skewness-29.194195
Sum998880
Variance8.6283188
MonotonicityNot monotonic
2023-12-12T15:24:31.785013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
100 9981
99.8%
80 5
 
0.1%
0 4
 
< 0.1%
40 2
 
< 0.1%
10 2
 
< 0.1%
60 2
 
< 0.1%
20 2
 
< 0.1%
30 1
 
< 0.1%
90 1
 
< 0.1%
ValueCountFrequency (%)
0 4
 
< 0.1%
10 2
 
< 0.1%
20 2
 
< 0.1%
30 1
 
< 0.1%
40 2
 
< 0.1%
60 2
 
< 0.1%
80 5
 
0.1%
90 1
 
< 0.1%
100 9981
99.8%
ValueCountFrequency (%)
100 9981
99.8%
90 1
 
< 0.1%
80 5
 
0.1%
60 2
 
< 0.1%
40 2
 
< 0.1%
30 1
 
< 0.1%
20 2
 
< 0.1%
10 2
 
< 0.1%
0 4
 
< 0.1%

Interactions

2023-12-12T15:24:27.514328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:23.550889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.374571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.062997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.878373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.787680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.650923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:23.672544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.509192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.180820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.080230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.918665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.759895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:23.787530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.616540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.301750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.226599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.040903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.878334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:23.918500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.732042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.476981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.384993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.154202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.986241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.058347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.852295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.618923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.546590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.273137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:28.081509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.205994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:24.967579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:25.753325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:26.673350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:24:27.394010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:24:31.863019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목 아이디누적 패스 점수시험 패스 점수퀴즈 패스 점수과제 패스 점수포럼 패스 점수출석 패스 점수출석 만점 점수
과목 아이디1.0000.6720.2440.0720.0000.0720.5530.142
누적 패스 점수0.6721.0000.1770.0000.0570.0000.6780.179
시험 패스 점수0.2440.1771.0000.1210.0000.1210.6090.000
퀴즈 패스 점수0.0720.0000.1211.0000.5620.7070.7360.000
과제 패스 점수0.0000.0570.0000.5621.0000.5620.4860.000
포럼 패스 점수0.0720.0000.1210.7070.5621.0000.7360.000
출석 패스 점수0.5530.6780.6090.7360.4860.7361.0000.000
출석 만점 점수0.1420.1790.0000.0000.0000.0000.0001.000
2023-12-12T15:24:31.964129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
포럼 패스 점수퀴즈 패스 점수
포럼 패스 점수1.0000.500
퀴즈 패스 점수0.5001.000
2023-12-12T15:24:32.045207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과목 아이디누적 패스 점수시험 패스 점수과제 패스 점수출석 패스 점수출석 만점 점수퀴즈 패스 점수포럼 패스 점수
과목 아이디1.0000.7240.0330.0230.156-0.0350.0550.055
누적 패스 점수0.7241.000-0.106-0.0010.0290.0030.0000.000
시험 패스 점수0.033-0.1061.0000.0150.2540.0060.1300.130
과제 패스 점수0.023-0.0010.0151.0000.053-0.0650.4080.408
출석 패스 점수0.1560.0290.2540.0531.000-0.0040.0000.000
출석 만점 점수-0.0350.0030.006-0.065-0.0041.0000.0000.000
퀴즈 패스 점수0.0550.0000.1300.4080.0000.0001.0000.500
포럼 패스 점수0.0550.0000.1300.4080.0000.0000.5001.000

Missing values

2023-12-12T15:24:28.575423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:24:28.799609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

과목 아이디누적 패스 점수시험 패스 점수퀴즈 패스 점수과제 패스 점수포럼 패스 점수라이브 세미나 패스 점수출석 콘텐츠 시간 비율출석 패스 점수출석 만점 점수
10545181661100000001000100
5938440000001000100
4184559460000001000100
7088859160000001000100
80310780000001000100
2908429860000001000100
6314778860000001000100
6859836060000001000100
926731067100000001000100
2744413260000001000100
과목 아이디누적 패스 점수시험 패스 점수퀴즈 패스 점수과제 패스 점수포럼 패스 점수라이브 세미나 패스 점수출석 콘텐츠 시간 비율출석 패스 점수출석 만점 점수
10617181823100000001000100
6751822860000001000100
919130918100000001000100
3825523060000001000100
10924860000001000100
4494591660000001000100
3083447860000001000100
922530988100000001000100
3717512260000001000100
1021514669810000000100100100