Overview

Dataset statistics

Number of variables18
Number of observations1614
Missing cells10279
Missing cells (%)35.4%
Duplicate rows365
Duplicate rows (%)22.6%
Total size in memory236.6 KiB
Average record size in memory150.1 B

Variable types

Text3
Categorical10
Unsupported2
Numeric3

Dataset

Description학점은행제 정보공시제도에서 따라 제공되는 공시항목 정보이며 공시년도, 공시시기, 공시대구분명, 공시중구분명, 공시소구분명, 공시입력시작일자, 공시입력시작시간, 공시입력종료일자, 공시입력종료시간, 공시자료기준시작일, 공시자료기준종료일, 공시지침, 공시횟수_알리미, 공시시기_알리미, 자료기준일_알리미, 점검기준, 생성일시, 수정일시 항목 정보를 제공합니다.
Author국가평생교육진흥원
URLhttps://www.data.go.kr/data/15087909/fileData.do

Alerts

Dataset has 365 (22.6%) duplicate rowsDuplicates
자료기준일_알리미 is highly overall correlated with 공시입력종료일자 and 4 other fieldsHigh correlation
공시시기_알리미 is highly overall correlated with 공시입력종료일자 and 8 other fieldsHigh correlation
생성일시 is highly overall correlated with 공시횟수_알리미 and 2 other fieldsHigh correlation
공시시기 is highly overall correlated with 공시대구분명 and 3 other fieldsHigh correlation
수정일시 is highly overall correlated with 공시입력종료일자 and 5 other fieldsHigh correlation
공시입력시작일자 is highly overall correlated with 공시입력종료일자 and 6 other fieldsHigh correlation
공시횟수_알리미 is highly overall correlated with 공시입력종료일자 and 8 other fieldsHigh correlation
공시대구분명 is highly overall correlated with 공시시기 and 3 other fieldsHigh correlation
공시입력종료시간 is highly overall correlated with 공시입력시작일자 and 3 other fieldsHigh correlation
공시입력종료일자 is highly overall correlated with 공시자료기준시작일 and 7 other fieldsHigh correlation
공시자료기준시작일 is highly overall correlated with 공시입력종료일자 and 1 other fieldsHigh correlation
공시자료기준종료일 is highly overall correlated with 공시입력종료일자 and 1 other fieldsHigh correlation
공시지침 is highly overall correlated with 공시입력종료일자High correlation
공시시기 is highly imbalanced (61.9%)Imbalance
공시대구분명 is highly imbalanced (66.8%)Imbalance
공시입력시작일자 is highly imbalanced (68.7%)Imbalance
공시입력종료시간 is highly imbalanced (69.8%)Imbalance
공시지침 is highly imbalanced (84.1%)Imbalance
공시횟수_알리미 is highly imbalanced (90.0%)Imbalance
공시시기_알리미 is highly imbalanced (90.0%)Imbalance
자료기준일_알리미 is highly imbalanced (93.5%)Imbalance
생성일시 is highly imbalanced (82.4%)Imbalance
수정일시 is highly imbalanced (85.5%)Imbalance
공시년도 has 60 (3.7%) missing valuesMissing
공시중구분명 has 1343 (83.2%) missing valuesMissing
공시소구분명 has 1614 (100.0%) missing valuesMissing
공시입력시작시간 has 1271 (78.7%) missing valuesMissing
공시입력종료일자 has 1397 (86.6%) missing valuesMissing
공시자료기준시작일 has 1490 (92.3%) missing valuesMissing
공시자료기준종료일 has 1490 (92.3%) missing valuesMissing
점검기준 has 1614 (100.0%) missing valuesMissing
공시소구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported
점검기준 is an unsupported type, check if it needs cleaning or further analysisUnsupported
공시입력종료일자 has 63 (3.9%) zerosZeros

Reproduction

Analysis started2023-12-12 04:36:51.840836
Analysis finished2023-12-12 04:36:55.837252
Duration4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

공시년도
Text

MISSING 

Distinct604
Distinct (%)38.9%
Missing60
Missing (%)3.7%
Memory size12.7 KiB
2023-12-12T13:36:56.056341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length164
Median length100
Mean length42.285714
Min length4

Characters and Unicode

Total characters65712
Distinct characters400
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique232 ?
Unique (%)14.9%

Sample

1st row2016
2nd rowo [자료기준일] : 2016년 09월
3rd rowo 2016년 9월 기준 교육훈련기관의 원격교육 시설ㆍ설비 현황임.
4th rowo 평가인정신청 양식과 동일하며 자료기준일을 고려하여 작성함.
5th rowo 교육훈련기관에서 확보하고 있는 원격교육 시설 및 설비만을 기재함.
ValueCountFrequency (%)
843
 
5.7%
o 291
 
2.0%
경우 243
 
1.6%
또는 199
 
1.4%
※ 138
 
0.9%
교수 129
 
0.9%
평가인정 127
 
0.9%
등 123
 
0.8%
수 117
 
0.8%
해당 115
 
0.8%
Other values (1268) 12404
84.2%
2023-12-12T13:36:56.646212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17555
26.7%
학 1369
 
2.1%
기 1184
 
1.8%
의 1141
 
1.7%
수 918
 
1.4%
교 903
 
1.4%
정 825
 
1.3%
비 776
 
1.2%
시 741
 
1.1%
[ 739
 
1.1%
Other values (390) 39561
60.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 40266
61.3%
Space Separator 17555
26.7%
Decimal Number 2572
 
3.9%
Other Punctuation 1533
 
2.3%
Close Punctuation 1149
 
1.7%
Open Punctuation 1107
 
1.7%
Lowercase Letter 693
 
1.1%
Uppercase Letter 324
 
0.5%
Dash Punctuation 194
 
0.3%
Other Symbol 112
 
0.2%
Other values (4) 207
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
학 1369
 
3.4%
기 1184
 
2.9%
의 1141
 
2.8%
수 918
 
2.3%
교 903
 
2.2%
정 825
 
2.0%
비 776
 
1.9%
시 741
 
1.8%
는 702
 
1.7%
에 670
 
1.7%
Other values (312) 31037
77.1%
Lowercase Letter
ValueCountFrequency (%)
o 330
47.6%
n 53
 
7.6%
e 52
 
7.5%
a 41
 
5.9%
c 34
 
4.9%
s 32
 
4.6%
t 29
 
4.2%
i 29
 
4.2%
r 24
 
3.5%
m 16
 
2.3%
Other values (7) 53
 
7.6%
Uppercase Letter
ValueCountFrequency (%)
D 50
15.4%
P 49
15.1%
B 39
12.0%
F 24
7.4%
M 22
6.8%
C 22
6.8%
T 21
6.5%
O 17
 
5.2%
L 17
 
5.2%
I 14
 
4.3%
Other values (5) 49
15.1%
Decimal Number
ValueCountFrequency (%)
1 722
28.1%
2 655
25.5%
0 575
22.4%
3 147
 
5.7%
9 123
 
4.8%
6 99
 
3.8%
8 99
 
3.8%
7 73
 
2.8%
5 69
 
2.7%
4 10
 
0.4%
Other Punctuation
ValueCountFrequency (%)
: 632
41.2%
. 616
40.2%
※ 139
 
9.1%
/ 96
 
6.3%
· 28
 
1.8%
* 12
 
0.8%
% 8
 
0.5%
' 2
 
0.1%
Other Number
ValueCountFrequency (%)
③ 2
14.3%
② 2
14.3%
① 2
14.3%
④ 2
14.3%
⑤ 2
14.3%
⑦ 2
14.3%
⑥ 2
14.3%
Math Symbol
ValueCountFrequency (%)
~ 51
63.0%
× 12
 
14.8%
÷ 6
 
7.4%
< 5
 
6.2%
> 5
 
6.2%
+ 2
 
2.5%
Open Punctuation
ValueCountFrequency (%)
[ 739
66.8%
( 358
32.3%
「 10
 
0.9%
Close Punctuation
ValueCountFrequency (%)
] 739
64.3%
) 400
34.8%
」 10
 
0.9%
Other Symbol
ValueCountFrequency (%)
★ 67
59.8%
□ 40
35.7%
㎡ 5
 
4.5%
Final Punctuation
ValueCountFrequency (%)
’ 52
92.9%
” 4
 
7.1%
Initial Punctuation
ValueCountFrequency (%)
‘ 52
92.9%
“ 4
 
7.1%
Space Separator
ValueCountFrequency (%)
17555
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 194
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 40266
61.3%
Common 24429
37.2%
Latin 1017
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
학 1369
 
3.4%
기 1184
 
2.9%
의 1141
 
2.8%
수 918
 
2.3%
교 903
 
2.2%
정 825
 
2.0%
비 776
 
1.9%
시 741
 
1.8%
는 702
 
1.7%
에 670
 
1.7%
Other values (312) 31037
77.1%
Common
ValueCountFrequency (%)
17555
71.9%
[ 739
 
3.0%
] 739
 
3.0%
1 722
 
3.0%
2 655
 
2.7%
: 632
 
2.6%
. 616
 
2.5%
0 575
 
2.4%
) 400
 
1.6%
( 358
 
1.5%
Other values (36) 1438
 
5.9%
Latin
ValueCountFrequency (%)
o 330
32.4%
n 53
 
5.2%
e 52
 
5.1%
D 50
 
4.9%
P 49
 
4.8%
a 41
 
4.0%
B 39
 
3.8%
c 34
 
3.3%
s 32
 
3.1%
t 29
 
2.9%
Other values (22) 308
30.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 40252
61.3%
ASCII 25003
38.0%
Punctuation 251
 
0.4%
Misc Symbols 67
 
0.1%
None 66
 
0.1%
Geometric Shapes 40
 
0.1%
Compat Jamo 14
 
< 0.1%
Enclosed Alphanum 14
 
< 0.1%
CJK Compat 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17555
70.2%
[ 739
 
3.0%
] 739
 
3.0%
1 722
 
2.9%
2 655
 
2.6%
: 632
 
2.5%
. 616
 
2.5%
0 575
 
2.3%
) 400
 
1.6%
( 358
 
1.4%
Other values (48) 2012
 
8.0%
Hangul
ValueCountFrequency (%)
학 1369
 
3.4%
기 1184
 
2.9%
의 1141
 
2.8%
수 918
 
2.3%
교 903
 
2.2%
정 825
 
2.0%
비 776
 
1.9%
시 741
 
1.8%
는 702
 
1.7%
에 670
 
1.7%
Other values (311) 31023
77.1%
Punctuation
ValueCountFrequency (%)
※ 139
55.4%
’ 52
 
20.7%
‘ 52
 
20.7%
“ 4
 
1.6%
” 4
 
1.6%
Misc Symbols
ValueCountFrequency (%)
★ 67
100.0%
Geometric Shapes
ValueCountFrequency (%)
□ 40
100.0%
None
ValueCountFrequency (%)
· 28
42.4%
× 12
18.2%
」 10
 
15.2%
「 10
 
15.2%
÷ 6
 
9.1%
Compat Jamo
ValueCountFrequency (%)
ㆍ 14
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 5
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
③ 2
14.3%
② 2
14.3%
① 2
14.3%
④ 2
14.3%
⑤ 2
14.3%
⑦ 2
14.3%
⑥ 2
14.3%

공시시기
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1269 
연 1회
 
80
9
 
77
2
 
60
3
 
35
Other values (7)
 
93

Length

Max length17
Median length4
Mean length3.6586121
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row9
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1269
78.6%
연 1회 80
 
5.0%
9 77
 
4.8%
2 60
 
3.7%
3 35
 
2.2%
연 2회 33
 
2.0%
0 22
 
1.4%
8 15
 
0.9%
6 10
 
0.6%
조치 시 마다 6
 
0.4%
Other values (2) 7
 
0.4%

Length

2023-12-12T13:36:56.827737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1269
71.9%
연 113
 
6.4%
1회 80
 
4.5%
9 77
 
4.4%
2 60
 
3.4%
3 35
 
2.0%
2회 33
 
1.9%
0 22
 
1.2%
8 15
 
0.8%
6 10
 
0.6%
Other values (12) 52
 
2.9%

공시대구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct28
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1269 
1. 기관 운영규칙 시설 등 기본 현황
 
53
4. 교수 또는 강사현황에 관한 사항
 
45
5. 학습비 및 회계에 관한사항
 
39
정기공시(3월 9월)
 
30
Other values (23)
178 

Length

Max length33
Median length4
Mean length6.9653036
Min length2

Unique

Unique5 ?
Unique (%)0.3%

Sample

1st row1. 기관 운영규칙 시설 등 기본 현황
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1269
78.6%
1. 기관 운영규칙 시설 등 기본 현황 53
 
3.3%
4. 교수 또는 강사현황에 관한 사항 45
 
2.8%
5. 학습비 및 회계에 관한사항 39
 
2.4%
정기공시(3월 9월) 30
 
1.9%
정기공시(2월) 25
 
1.5%
3. 학습자 수 등 학습자 현황에 관한 사항 22
 
1.4%
2. 평가인정을 받은 학습과정 현황 및 그 운영에 관한 사항 18
 
1.1%
8. 그 밖의 교육여건 및 기관 운영현황 18
 
1.1%
정기공시(9월) 15
 
0.9%
Other values (18) 80
 
5.0%

Length

2023-12-12T13:36:57.004935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1269
42.0%
관한 97
 
3.2%
사항 97
 
3.2%
기관 83
 
2.7%
및 83
 
2.7%
등 75
 
2.5%
현황 71
 
2.4%
운영규칙 53
 
1.8%
시설 53
 
1.8%
기본 53
 
1.8%
Other values (67) 1087
36.0%

공시중구분명
Text

MISSING 

Distinct68
Distinct (%)25.1%
Missing1343
Missing (%)83.2%
Memory size12.7 KiB
2023-12-12T13:36:57.282726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length36
Mean length22.638376
Min length7

Characters and Unicode

Total characters6135
Distinct characters108
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)8.1%

Sample

1st row다. 원격교육 실시 관련 시설.설비 현황
2nd row2016년 9월
3rd row가. 평가인정 학습과정 현황
4th row2016년 9월
5th row나. 연간 학습과정 운영 일정
ValueCountFrequency (%)
103
 
6.7%
현황 71
 
4.6%
기준 63
 
4.1%
개설 55
 
3.6%
학습과정(개강일 55
 
3.6%
다 47
 
3.1%
및 46
 
3.0%
교수 40
 
2.6%
또는 40
 
2.6%
수 39
 
2.5%
Other values (95) 981
63.7%
2023-12-12T13:36:57.690134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1332
21.7%
1 398
 
6.5%
. 352
 
5.7%
2 273
 
4.4%
0 249
 
4.1%
정 134
 
2.2%
학 130
 
2.1%
개 124
 
2.0%
설 121
 
2.0%
기 120
 
2.0%
Other values (98) 2902
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2992
48.8%
Space Separator 1332
21.7%
Decimal Number 1214
19.8%
Other Punctuation 394
 
6.4%
Close Punctuation 69
 
1.1%
Open Punctuation 69
 
1.1%
Math Symbol 63
 
1.0%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 134
 
4.5%
학 130
 
4.3%
개 124
 
4.1%
설 121
 
4.0%
기 120
 
4.0%
강 120
 
4.0%
습 113
 
3.8%
일 111
 
3.7%
년 102
 
3.4%
시 96
 
3.2%
Other values (82) 1821
60.9%
Decimal Number
ValueCountFrequency (%)
1 398
32.8%
2 273
22.5%
0 249
20.5%
3 83
 
6.8%
9 57
 
4.7%
6 51
 
4.2%
7 47
 
3.9%
8 41
 
3.4%
5 15
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 352
89.3%
: 42
 
10.7%
Space Separator
ValueCountFrequency (%)
1332
100.0%
Close Punctuation
ValueCountFrequency (%)
) 69
100.0%
Open Punctuation
ValueCountFrequency (%)
( 69
100.0%
Math Symbol
ValueCountFrequency (%)
~ 63
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3143
51.2%
Hangul 2980
48.6%
Han 12
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 134
 
4.5%
학 130
 
4.4%
개 124
 
4.2%
설 121
 
4.1%
기 120
 
4.0%
강 120
 
4.0%
습 113
 
3.8%
일 111
 
3.7%
년 102
 
3.4%
시 96
 
3.2%
Other values (80) 1809
60.7%
Common
ValueCountFrequency (%)
1332
42.4%
1 398
 
12.7%
. 352
 
11.2%
2 273
 
8.7%
0 249
 
7.9%
3 83
 
2.6%
) 69
 
2.2%
( 69
 
2.2%
~ 63
 
2.0%
9 57
 
1.8%
Other values (6) 198
 
6.3%
Han
ValueCountFrequency (%)
舍 6
50.0%
校 6
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3143
51.2%
Hangul 2973
48.5%
CJK 12
 
0.2%
Compat Jamo 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1332
42.4%
1 398
 
12.7%
. 352
 
11.2%
2 273
 
8.7%
0 249
 
7.9%
3 83
 
2.6%
) 69
 
2.2%
( 69
 
2.2%
~ 63
 
2.0%
9 57
 
1.8%
Other values (6) 198
 
6.3%
Hangul
ValueCountFrequency (%)
정 134
 
4.5%
학 130
 
4.4%
개 124
 
4.2%
설 121
 
4.1%
기 120
 
4.0%
강 120
 
4.0%
습 113
 
3.8%
일 111
 
3.7%
년 102
 
3.4%
시 96
 
3.2%
Other values (79) 1802
60.6%
Compat Jamo
ValueCountFrequency (%)
ㆍ 7
100.0%
CJK
ValueCountFrequency (%)
舍 6
50.0%
校 6
50.0%

공시소구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1614
Missing (%)100.0%
Memory size14.3 KiB

공시입력시작일자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct39
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1273 
00000000
 
63
2017-10-11 00:00:00.0
 
40
20160905
 
29
2021-01-04 09:41:58.0
 
21
Other values (34)
188 

Length

Max length21
Median length4
Mean length5.8438662
Min length4

Unique

Unique6 ?
Unique (%)0.4%

Sample

1st row20160905
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1273
78.9%
00000000 63
 
3.9%
2017-10-11 00:00:00.0 40
 
2.5%
20160905 29
 
1.8%
2021-01-04 09:41:58.0 21
 
1.3%
2020-01-02 09:52:31.0 21
 
1.3%
2019-01-03 15:38:04.0 21
 
1.3%
2018-01-03 15:48:37.0 21
 
1.3%
20200901 12
 
0.7%
20210201 9
 
0.6%
Other values (29) 104
 
6.4%

Length

2023-12-12T13:36:57.842396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 1273
73.2%
00000000 63
 
3.6%
2017-10-11 40
 
2.3%
00:00:00.0 40
 
2.3%
20160905 29
 
1.7%
2019-01-03 21
 
1.2%
2018-01-03 21
 
1.2%
15:38:04.0 21
 
1.2%
15:48:37.0 21
 
1.2%
09:52:31.0 21
 
1.2%
Other values (34) 188
 
10.8%

공시입력시작시간
Text

MISSING 

Distinct55
Distinct (%)16.0%
Missing1271
Missing (%)78.7%
Memory size12.7 KiB
2023-12-12T13:36:58.129898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length10.145773
Min length4

Characters and Unicode

Total characters3480
Distinct characters14
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)10.8%

Sample

1st row0000
2nd row2017-10-11 00:00:00.0
3rd row0000
4th row0000
5th row2017-10-11 00:00:00.0
ValueCountFrequency (%)
0000 208
44.5%
2017-10-11 24
 
5.1%
00:00:00.0 24
 
5.1%
2018-03-30 13
 
2.8%
1000 10
 
2.1%
2021-01-04 8
 
1.7%
2018-09-10 6
 
1.3%
2020-09-15 6
 
1.3%
09:21:00.0 6
 
1.3%
09:41:58.0 6
 
1.3%
Other values (77) 156
33.4%
2023-12-12T13:36:58.521332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1525
43.8%
1 395
 
11.4%
- 248
 
7.1%
: 248
 
7.1%
2 242
 
7.0%
124
 
3.6%
. 124
 
3.6%
3 121
 
3.5%
4 101
 
2.9%
9 89
 
2.6%
Other values (4) 263
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2736
78.6%
Other Punctuation 372
 
10.7%
Dash Punctuation 248
 
7.1%
Space Separator 124
 
3.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1525
55.7%
1 395
 
14.4%
2 242
 
8.8%
3 121
 
4.4%
4 101
 
3.7%
9 89
 
3.3%
8 88
 
3.2%
5 83
 
3.0%
7 55
 
2.0%
6 37
 
1.4%
Other Punctuation
ValueCountFrequency (%)
: 248
66.7%
. 124
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 248
100.0%
Space Separator
ValueCountFrequency (%)
124
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3480
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1525
43.8%
1 395
 
11.4%
- 248
 
7.1%
: 248
 
7.1%
2 242
 
7.0%
124
 
3.6%
. 124
 
3.6%
3 121
 
3.5%
4 101
 
2.9%
9 89
 
2.6%
Other values (4) 263
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3480
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1525
43.8%
1 395
 
11.4%
- 248
 
7.1%
: 248
 
7.1%
2 242
 
7.0%
124
 
3.6%
. 124
 
3.6%
3 121
 
3.5%
4 101
 
2.9%
9 89
 
2.6%
Other values (4) 263
 
7.6%

공시입력종료일자
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct36
Distinct (%)16.6%
Missing1397
Missing (%)86.6%
Infinite0
Infinite (%)0.0%
Mean14324362
Minimum0
Maximum20211231
Zeros63
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2023-12-12T13:36:58.651551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median20170310
Q320190920
95-th percentile20210223
Maximum20211231
Range20211231
Interquartile range (IQR)20190920

Descriptive statistics

Standard deviation9183087.3
Coefficient of variation (CV)0.64108177
Kurtosis-1.1451613
Mean14324362
Median Absolute Deviation (MAD)29915
Skewness-0.93030488
Sum3.1083865 × 109
Variance8.4329092 × 1013
MonotonicityNot monotonic
2023-12-12T13:36:58.776597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 63
 
3.9%
20160930 20
 
1.2%
20200923 12
 
0.7%
20170219 9
 
0.6%
20200225 9
 
0.6%
20190219 9
 
0.6%
20210222 8
 
0.5%
20160923 8
 
0.5%
20190920 6
 
0.4%
20170913 6
 
0.4%
Other values (26) 67
 
4.2%
(Missing) 1397
86.6%
ValueCountFrequency (%)
0 63
3.9%
20160923 8
 
0.5%
20160930 20
 
1.2%
20161231 1
 
0.1%
20170210 3
 
0.2%
20170219 9
 
0.6%
20170310 5
 
0.3%
20170529 1
 
0.1%
20170619 1
 
0.1%
20170818 2
 
0.1%
ValueCountFrequency (%)
20211231 2
 
0.1%
20210825 2
 
0.1%
20210624 1
 
0.1%
20210325 5
0.3%
20210225 1
 
0.1%
20210222 8
0.5%
20201231 2
 
0.1%
20200923 12
0.7%
20200825 2
 
0.1%
20200624 1
 
0.1%

공시입력종료시간
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1395 
2359
175 
1800
 
26
1759
 
17
1730
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row2359
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1395
86.4%
2359 175
 
10.8%
1800 26
 
1.6%
1759 17
 
1.1%
1730 1
 
0.1%

Length

2023-12-12T13:36:58.920714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:36:59.039904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1395
86.4%
2359 175
 
10.8%
1800 26
 
1.6%
1759 17
 
1.1%
1730 1
 
0.1%

공시자료기준시작일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)19.4%
Missing1490
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean20018472
Minimum2016
Maximum20210301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2023-12-12T13:36:59.149825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile20160101
Q120170101
median20180101
Q320193126
95-th percentile20208706
Maximum20210301
Range20208285
Interquartile range (IQR)23025

Descriptive statistics

Standard deviation1812219.3
Coefficient of variation (CV)0.090527353
Kurtosis123.97914
Mean20018472
Median Absolute Deviation (MAD)10600
Skewness-11.134135
Sum2.4822906 × 109
Variance3.2841388 × 1012
MonotonicityNot monotonic
2023-12-12T13:36:59.337605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
20200101 16
 
1.0%
20190101 16
 
1.0%
20170101 15
 
0.9%
20180101 14
 
0.9%
20160101 9
 
0.6%
20160901 9
 
0.6%
20210101 6
 
0.4%
20150101 5
 
0.3%
20200801 4
 
0.2%
20180701 4
 
0.2%
Other values (14) 26
 
1.6%
(Missing) 1490
92.3%
ValueCountFrequency (%)
2016 1
 
0.1%
20150101 5
 
0.3%
20160101 9
0.6%
20160701 3
 
0.2%
20160901 9
0.6%
20161231 1
 
0.1%
20170101 15
0.9%
20170301 1
 
0.1%
20170701 3
 
0.2%
20170801 2
 
0.1%
ValueCountFrequency (%)
20210301 1
 
0.1%
20210101 6
 
0.4%
20200801 4
 
0.2%
20200701 3
 
0.2%
20200301 1
 
0.1%
20200101 16
1.0%
20190801 2
 
0.1%
20190701 3
 
0.2%
20190301 1
 
0.1%
20190101 16
1.0%

공시자료기준종료일
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)24.2%
Missing1490
Missing (%)92.3%
Infinite0
Infinite (%)0.0%
Mean20018998
Minimum2016
Maximum20211231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.3 KiB
2023-12-12T13:36:59.515212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile20160630
Q120170101
median20181231
Q320193448
95-th percentile20208770
Maximum20211231
Range20209215
Interquartile range (IQR)23347.5

Descriptive statistics

Standard deviation1812266.5
Coefficient of variation (CV)0.090527333
Kurtosis123.97925
Mean20018998
Median Absolute Deviation (MAD)11130
Skewness-11.134143
Sum2.4823557 × 109
Variance3.2843098 × 1012
MonotonicityNot monotonic
2023-12-12T13:36:59.674481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20181231 11
 
0.7%
20191231 11
 
0.7%
20161231 10
 
0.6%
20171231 10
 
0.6%
20201231 9
 
0.6%
20160901 9
 
0.6%
20200630 6
 
0.4%
20151231 5
 
0.3%
20170101 5
 
0.3%
20180101 5
 
0.3%
Other values (20) 43
 
2.7%
(Missing) 1490
92.3%
ValueCountFrequency (%)
2016 1
 
0.1%
20151231 5
0.3%
20160630 3
 
0.2%
20160901 9
0.6%
20161231 10
0.6%
20170101 5
0.3%
20170301 1
 
0.1%
20170630 3
 
0.2%
20170801 2
 
0.1%
20171231 10
0.6%
ValueCountFrequency (%)
20211231 2
 
0.1%
20210617 1
 
0.1%
20210301 1
 
0.1%
20210101 3
 
0.2%
20201231 9
0.6%
20200801 4
 
0.2%
20200630 6
0.4%
20200301 1
 
0.1%
20200101 4
 
0.2%
20191231 11
0.7%

공시지침
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1489 
□ 작성지침
 
94
□ 공시방법 : 학점은행제 정보시스템 연계(국가평생교육진흥원에서 직접 공시)
 
11
□ 공시방법 : 정보공시시스템에 PDF파일 형태로 업로드
 
6
□ 공시방법 : 학점은행제 정보공시 입력시스템에 직접 입력
 
5
Other values (4)
 
9

Length

Max length42
Median length4
Mean length4.6703841
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row□ 작성지침
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1489
92.3%
□ 작성지침 94
 
5.8%
□ 공시방법 : 학점은행제 정보시스템 연계(국가평생교육진흥원에서 직접 공시) 11
 
0.7%
□ 공시방법 : 정보공시시스템에 PDF파일 형태로 업로드 6
 
0.4%
□ 공시방법 : 학점은행제 정보공시 입력시스템에 직접 입력 5
 
0.3%
□ 공시방법 4
 
0.2%
□ 공시방법 : 학점은행제 정보공시 입력시스템에 PDF파일 형태로 업로드 2
 
0.1%
□ 공시방법 : 학점은행제 정보공시 입력시스템에 직접 입력 2
 
0.1%
2017년 1월 1일 기준 교육훈련기관의 원격교육 시설 설비 현황임. 1
 
0.1%

Length

2023-12-12T13:36:59.884912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:00.034579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1489
78.5%
□ 124
 
6.5%
작성지침 94
 
5.0%
공시방법 30
 
1.6%
26
 
1.4%
학점은행제 20
 
1.1%
직접 18
 
0.9%
정보시스템 11
 
0.6%
연계(국가평생교육진흥원에서 11
 
0.6%
공시 11
 
0.6%
Other values (16) 64
 
3.4%

공시횟수_알리미
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1593 
연 1회
 
21

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1593
98.7%
연 1회 21
 
1.3%

Length

2023-12-12T13:37:00.235492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:00.361460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1593
97.4%
연 21
 
1.3%
1회 21
 
1.3%

공시시기_알리미
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1593 
2월
 
21

Length

Max length4
Median length4
Mean length3.9739777
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1593
98.7%
2월 21
 
1.3%

Length

2023-12-12T13:37:00.486469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:00.623529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1593
98.7%
2월 21
 
1.3%

자료기준일_알리미
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1593 
2017년 1월 1일
 
20
2016년 9월
 
1

Length

Max length11
Median length4
Mean length4.0892193
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1593
98.7%
2017년 1월 1일 20
 
1.2%
2016년 9월 1
 
0.1%

Length

2023-12-12T13:37:00.774011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:00.933804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1593
96.3%
2017년 20
 
1.2%
1월 20
 
1.2%
1일 20
 
1.2%
2016년 1
 
0.1%
9월 1
 
0.1%

점검기준
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1614
Missing (%)100.0%
Memory size14.3 KiB

생성일시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1520 
2017-10-11 00:00:00.0
 
26
2018-01-03 15:48:37.0
 
17
2019-01-03 15:38:04.0
 
17
2020-01-02 09:52:31.0
 
17

Length

Max length21
Median length4
Mean length4.9900867
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1520
94.2%
2017-10-11 00:00:00.0 26
 
1.6%
2018-01-03 15:48:37.0 17
 
1.1%
2019-01-03 15:38:04.0 17
 
1.1%
2020-01-02 09:52:31.0 17
 
1.1%
2021-01-04 09:41:58.0 17
 
1.1%

Length

2023-12-12T13:37:01.056182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:01.225982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1520
89.0%
2017-10-11 26
 
1.5%
00:00:00.0 26
 
1.5%
2018-01-03 17
 
1.0%
15:48:37.0 17
 
1.0%
2019-01-03 17
 
1.0%
15:38:04.0 17
 
1.0%
2020-01-02 17
 
1.0%
09:52:31.0 17
 
1.0%
2021-01-04 17
 
1.0%

수정일시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
<NA>
1520 
2017-10-11 00:00:00.0
 
26
2018-01-03 15:48:37.0
 
17
2019-01-03 15:38:04.0
 
13
2021-01-04 09:41:58.0
 
13
Other values (5)
 
25

Length

Max length21
Median length4
Mean length4.9900867
Min length4

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1520
94.2%
2017-10-11 00:00:00.0 26
 
1.6%
2018-01-03 15:48:37.0 17
 
1.1%
2019-01-03 15:38:04.0 13
 
0.8%
2021-01-04 09:41:58.0 13
 
0.8%
2020-01-02 09:52:31.0 12
 
0.7%
2021-02-16 09:06:56.0 4
 
0.2%
2020-02-25 10:49:50.0 4
 
0.2%
2019-02-18 09:18:14.0 4
 
0.2%
2020-02-17 14:38:24.0 1
 
0.1%

Length

2023-12-12T13:37:01.387356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:37:01.538383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1520
89.0%
00:00:00.0 26
 
1.5%
2017-10-11 26
 
1.5%
2018-01-03 17
 
1.0%
15:48:37.0 17
 
1.0%
2019-01-03 13
 
0.8%
15:38:04.0 13
 
0.8%
2021-01-04 13
 
0.8%
09:41:58.0 13
 
0.8%
09:52:31.0 12
 
0.7%
Other values (9) 38
 
2.2%

Interactions

2023-12-12T13:36:54.067220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.317734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.675134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:54.183310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.432638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.815217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:54.295756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.550038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:36:53.938146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:37:01.747224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시시기공시대구분명공시중구분명공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시자료기준시작일공시자료기준종료일공시지침자료기준일_알리미생성일시수정일시
공시시기1.0000.9640.9820.9140.8770.1260.499NaNNaN0.5010.6280.0000.000
공시대구분명0.9641.0000.9900.5120.9390.1540.319NaNNaN0.647NaN0.0000.000
공시중구분명0.9820.9901.0000.0000.987NaN0.462NaNNaN0.8210.6280.0000.000
공시입력시작일자0.9140.5120.0001.0000.7151.0000.941NaNNaN0.5681.0000.5890.916
공시입력시작시간0.8770.9390.9870.7151.0000.0680.437NaNNaN0.0000.0000.3341.000
공시입력종료일자0.1260.154NaN1.0000.0681.0000.449NaNNaNNaNNaN0.1150.870
공시입력종료시간0.4990.3190.4620.9410.4370.4491.000NaNNaN0.7850.0000.4471.000
공시자료기준시작일NaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaN
공시자료기준종료일NaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaN
공시지침0.5010.6470.8210.5680.000NaN0.785NaNNaN1.000NaNNaNNaN
자료기준일_알리미0.628NaN0.6281.0000.000NaN0.000NaNNaNNaN1.0000.0000.000
생성일시0.0000.0000.0000.5890.3340.1150.447NaNNaNNaN0.0001.0001.000
수정일시0.0000.0000.0000.9161.0000.8701.000NaNNaNNaN0.0001.0001.000
2023-12-12T13:37:01.999078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시지침자료기준일_알리미공시시기_알리미생성일시공시시기수정일시공시입력시작일자공시횟수_알리미공시대구분명공시입력종료시간
공시지침1.000NaNNaNNaN0.304NaN0.229NaN0.3850.443
자료기준일_알리미NaN1.0001.0000.0000.4300.0000.9181.0001.0000.000
공시시기_알리미NaN1.0001.0001.0001.0001.0001.0001.0001.0001.000
생성일시NaN0.0001.0001.0000.0000.9770.4461.0000.0000.373
공시시기0.3040.4301.0000.0001.0000.0000.5981.0000.7850.342
수정일시NaN0.0001.0000.9770.0001.0000.7971.0000.0000.966
공시입력시작일자0.2290.9181.0000.4460.5980.7971.0001.0000.1350.739
공시횟수_알리미NaN1.0001.0001.0001.0001.0001.0001.0001.0001.000
공시대구분명0.3851.0001.0000.0000.7850.0000.1351.0001.0000.206
공시입력종료시간0.4430.0001.0000.3730.3420.9660.7391.0000.2061.000
2023-12-12T13:37:02.162747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공시입력종료일자공시자료기준시작일공시자료기준종료일공시시기공시대구분명공시입력시작일자공시입력종료시간공시지침공시횟수_알리미공시시기_알리미자료기준일_알리미생성일시수정일시
공시입력종료일자1.0000.9460.9820.0890.1510.9250.3011.0001.0001.0001.0000.1370.668
공시자료기준시작일0.9461.0000.9800.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
공시자료기준종료일0.9820.9801.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
공시시기0.0890.0000.0001.0000.7850.5980.3420.3041.0001.0000.4300.0000.000
공시대구분명0.1510.0000.0000.7851.0000.1350.2060.3851.0001.0001.0000.0000.000
공시입력시작일자0.9250.0000.0000.5980.1351.0000.7390.2291.0001.0000.9180.4460.797
공시입력종료시간0.3010.0000.0000.3420.2060.7391.0000.4431.0001.0000.0000.3730.966
공시지침1.0000.0000.0000.3040.3850.2290.4431.0000.0000.0000.0000.0000.000
공시횟수_알리미1.0000.0000.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
공시시기_알리미1.0000.0000.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.000
자료기준일_알리미1.0000.0000.0000.4301.0000.9180.0000.0001.0001.0001.0000.0000.000
생성일시0.1370.0000.0000.0000.0000.4460.3730.0001.0001.0000.0001.0000.977
수정일시0.6680.0000.0000.0000.0000.7970.9660.0001.0001.0000.0000.9771.000

Missing values

2023-12-12T13:36:54.474445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:36:55.226184image/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.
2023-12-12T13:36:55.557611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

공시년도공시시기공시대구분명공시중구분명공시소구분명공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시자료기준시작일공시자료기준종료일공시지침공시횟수_알리미공시시기_알리미자료기준일_알리미점검기준생성일시수정일시
0201691. 기관 운영규칙 시설 등 기본 현황다. 원격교육 실시 관련 시설.설비 현황<NA>2016090500002016093023592016090120160901□ 작성지침<NA><NA><NA><NA><NA><NA>
1o [자료기준일] : 2016년 09월<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2o 2016년 9월 기준 교육훈련기관의 원격교육 시설ㆍ설비 현황임.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3o 평가인정신청 양식과 동일하며 자료기준일을 고려하여 작성함.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
4o 교육훈련기관에서 확보하고 있는 원격교육 시설 및 설비만을 기재함.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5o [비고] : IDC 서비스 여부 서버의 독립 여부 등을 기재함.<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6※ 용어 정의<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7o [하드웨어-OLPT] OnLine Transction Processing(온라인 트랜젝션 처리)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8- 온라인 사용자 편익을 위해 데이타베이스에 저장되어 있는 정보를 실시간으로 갱신/조회하는 처리방식(사용자 요청에 즉각 반응 및 처리)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9o [하드웨어-tpmC] : Transaction Processing Performance Council(성능평가)<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
공시년도공시시기공시대구분명공시중구분명공시소구분명공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시자료기준시작일공시자료기준종료일공시지침공시횟수_알리미공시시기_알리미자료기준일_알리미점검기준생성일시수정일시
16042019.1.1~ 12.31(개강일 기준)에 운영된 평가인정 학습과정에 대한 학습비 반환 현황<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1605[학습비 수입 총액] : 2019년 학습비 수입 총액<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1606※ 반환된 학습비는 포함하고 규정에 근거하여 장학목적으로 할인 또는 감면 금액은 제외<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1607[학습비 반환 건수] : 학습비의 전체 또는 일부가 반환된 횟수<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1608[반환금액] : 학습자에게 반환된 학습비의 합계<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1609[반환 비율] : 전체 학습비 대비 반환된 학습비 비율<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1610[과오납] : 학습비를 초과하여 납부하거나 착오에 의해 잘못 납부되어 반환하는 경우<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1611[군입대] : 군입대로 인하여 학습비를 반환하는 경우<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1612[학습포기] : 학습포기로 인하여 학습비를 반환하는 경우<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1613<NA>연 1회정기공시(9월)2019년 개설 학습과정(개강일 기준 2019.1.1 ~ 12.31)<NA>2021-01-04 09:41:58.02021-01-04 09:41:58.0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

공시년도공시시기공시대구분명공시중구분명공시입력시작일자공시입력시작시간공시입력종료일자공시입력종료시간공시자료기준시작일공시자료기준종료일공시지침공시횟수_알리미공시시기_알리미자료기준일_알리미생성일시수정일시# duplicates
352□ 작성지침<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>30
53[운영시기] : 3월 공시 - 하반기 9월 공시 - 상반기<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>20
43[비전임교원] : 전임교원을 제외한 모든 교원<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>12
24[강의담당 학점] : 해당 교원(전임/비전임)이 강의를 담당한 총 학점<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8
25[개설 학급수] : 해당 학습과정의 개설 학급 수<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8
42[비율] : 총 개설 강의학점 중 해당 교원(전임/비전임)의 강의담당 비율<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8
50[실제 학습과정 운영 인원] : 해당 학습과정의 강의를 담당한 교수 또는 강사 수<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8
305o [자료기준일] : 2016년 09월<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>8
77[총 개설 강의학점] : 전임교원과 비전임 교원이 담당한 강의학점의 총합<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>7
3구입비 도서구입비 국내외여비 간행물구독료 회의비 용역비 등<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>6