Overview

Dataset statistics

Number of variables7
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory59.1 B

Variable types

Categorical3
Text3
DateTime1

Dataset

Description등록민간자격, 국가공인민간자격 등 민간자격에 대한 정보 중 국가공인민간자격 데이터로 구성되어 있습니다. 각 국가공인민간자격의 소관부처, 자격종목, 등급, 자격관리자, 유효공인기간 데이터로 이루어져 있습니다.
URLhttps://www.data.go.kr/data/15069563/fileData.do

Alerts

소관부처 is highly overall correlated with 공인유효기간(시작)High correlation
등급 is highly overall correlated with 공인유효기간(시작)High correlation
공인유효기간(시작) is highly overall correlated with 소관부처 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 00:18:02.956471
Analysis finished2023-12-12 00:18:03.482459
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소관부처
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
교육부
15 
고용노동부
13 
산업통상자원부
과학기술정보통신부
문화체육관광부
Other values (3)

Length

Max length9
Median length7
Mean length5
Min length3

Unique

Unique3 ?
Unique (%)7.0%

Sample

1st row금융위원회
2nd row과학기술정보통신부
3rd row과학기술정보통신부
4th row과학기술정보통신부
5th row과학기술정보통신부

Common Values

ValueCountFrequency (%)
교육부 15
34.9%
고용노동부 13
30.2%
산업통상자원부 6
 
14.0%
과학기술정보통신부 4
 
9.3%
문화체육관광부 2
 
4.7%
금융위원회 1
 
2.3%
행정안전부 1
 
2.3%
조달청 1
 
2.3%

Length

2023-12-12T09:18:03.561409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:18:03.677379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
교육부 15
34.9%
고용노동부 13
30.2%
산업통상자원부 6
 
14.0%
과학기술정보통신부 4
 
9.3%
문화체육관광부 2
 
4.7%
금융위원회 1
 
2.3%
행정안전부 1
 
2.3%
조달청 1
 
2.3%
Distinct41
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T09:18:03.896615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.8837209
Min length4

Characters and Unicode

Total characters339
Distinct characters111
Distinct categories6 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)90.7%

Sample

1st row재무설계사
2nd row공무원정보이용능력평가(NIT)
3rd row정보보호전문가(SIS)
4th row정보보호전문가(SIS)
5th row인터넷정보관리사
ValueCountFrequency (%)
정보보호전문가(sis 2
 
4.1%
mate 2
 
4.1%
한자급수인증 2
 
4.1%
컴퓨터 1
 
2.0%
국어능력인증시험 1
 
2.0%
기계 1
 
2.0%
1
 
2.0%
시스템제어사 1
 
2.0%
재무설계사 1
 
2.0%
산업전자기기제작사 1
 
2.0%
Other values (36) 36
73.5%
2023-12-12T09:18:04.260588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23
 
6.8%
15
 
4.4%
12
 
3.5%
11
 
3.2%
10
 
2.9%
10
 
2.9%
9
 
2.7%
9
 
2.7%
8
 
2.4%
7
 
2.1%
Other values (101) 225
66.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 285
84.1%
Uppercase Letter 23
 
6.8%
Lowercase Letter 19
 
5.6%
Space Separator 6
 
1.8%
Close Punctuation 3
 
0.9%
Open Punctuation 3
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
23
 
8.1%
15
 
5.3%
12
 
4.2%
11
 
3.9%
10
 
3.5%
10
 
3.5%
9
 
3.2%
9
 
3.2%
8
 
2.8%
7
 
2.5%
Other values (78) 171
60.0%
Uppercase Letter
ValueCountFrequency (%)
S 6
26.1%
I 3
13.0%
M 3
13.0%
C 2
 
8.7%
T 2
 
8.7%
N 2
 
8.7%
W 1
 
4.3%
Y 1
 
4.3%
B 1
 
4.3%
E 1
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
e 3
15.8%
t 3
15.8%
i 3
15.8%
a 3
15.8%
n 2
10.5%
g 2
10.5%
r 1
 
5.3%
p 1
 
5.3%
k 1
 
5.3%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 281
82.9%
Latin 42
 
12.4%
Common 12
 
3.5%
Han 4
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
23
 
8.2%
15
 
5.3%
12
 
4.3%
11
 
3.9%
10
 
3.6%
10
 
3.6%
9
 
3.2%
9
 
3.2%
8
 
2.8%
7
 
2.5%
Other values (74) 167
59.4%
Latin
ValueCountFrequency (%)
S 6
14.3%
e 3
 
7.1%
I 3
 
7.1%
t 3
 
7.1%
i 3
 
7.1%
a 3
 
7.1%
M 3
 
7.1%
n 2
 
4.8%
g 2
 
4.8%
C 2
 
4.8%
Other values (10) 12
28.6%
Han
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Common
ValueCountFrequency (%)
6
50.0%
) 3
25.0%
( 3
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 281
82.9%
ASCII 54
 
15.9%
CJK 4
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
23
 
8.2%
15
 
5.3%
12
 
4.3%
11
 
3.9%
10
 
3.6%
10
 
3.6%
9
 
3.2%
9
 
3.2%
8
 
2.8%
7
 
2.5%
Other values (74) 167
59.4%
ASCII
ValueCountFrequency (%)
S 6
 
11.1%
6
 
11.1%
e 3
 
5.6%
I 3
 
5.6%
t 3
 
5.6%
) 3
 
5.6%
i 3
 
5.6%
a 3
 
5.6%
M 3
 
5.6%
( 3
 
5.6%
Other values (13) 18
33.3%
CJK
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

등급
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Memory size476.0 B
등급없음
22 
1급, 2급, 3급
1급, 2급, 3급, 4급
1급
 
1
2급
 
1
Other values (10)
10 

Length

Max length129
Median length4
Mean length11.767442
Min length2

Unique

Unique12 ?
Unique (%)27.9%

Sample

1st row등급없음
2nd row등급없음
3rd row1급
4th row2급
5th row전문가, 1급, 2급

Common Values

ValueCountFrequency (%)
등급없음 22
51.2%
1급, 2급, 3급 6
 
14.0%
1급, 2급, 3급, 4급 3
 
7.0%
1급 1
 
2.3%
2급 1
 
2.3%
전문가, 1급, 2급 1
 
2.3%
4급 1
 
2.3%
특1급, 2급, 3급 1
 
2.3%
사범, 1급, 준1급, 2급, 준2급 1
 
2.3%
Expert expert, Expert emerging, Commanding High, Commanding Mid, Commanding Low, Commanding Emerging, Moderate High, Moderate Mid 1
 
2.3%
Other values (5) 5
 
11.6%

Length

2023-12-12T09:18:04.392616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등급없음 22
20.8%
2급 16
15.1%
1급 15
14.2%
3급 12
11.3%
commanding 7
 
6.6%
expert 5
 
4.7%
4급 5
 
4.7%
high 4
 
3.8%
moderate 4
 
3.8%
mid 3
 
2.8%
Other values (10) 13
12.3%
Distinct24
Distinct (%)55.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T09:18:04.571442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length9.372093
Min length5

Characters and Unicode

Total characters403
Distinct characters87
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)41.9%

Sample

1st row한국 FPSB
2nd row(사)한국정보통신진흥협회
3rd row(사)한국인터넷진흥원
4th row(사)한국인터넷진흥원
5th row(사)한국정보통신진흥협회
ValueCountFrequency (%)
대한상공회의소 14
31.8%
한국교육문화회 3
 
6.8%
사)한국인터넷진흥원 2
 
4.5%
사)한국외국어평가원 2
 
4.5%
숙명여자대학교 2
 
4.5%
사)한국정보통신진흥협회 2
 
4.5%
주)이에스피평가아카데미 1
 
2.3%
fpsb 1
 
2.3%
한국 1
 
2.3%
사)한국정보관리협회 1
 
2.3%
Other values (15) 15
34.1%
2023-12-12T09:18:04.883846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40
 
9.9%
25
 
6.2%
23
 
5.7%
( 21
 
5.2%
) 21
 
5.2%
18
 
4.5%
15
 
3.7%
15
 
3.7%
15
 
3.7%
14
 
3.5%
Other values (77) 196
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
88.1%
Open Punctuation 21
 
5.2%
Close Punctuation 21
 
5.2%
Uppercase Letter 4
 
1.0%
Space Separator 1
 
0.2%
Other Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
40
 
11.3%
25
 
7.0%
23
 
6.5%
18
 
5.1%
15
 
4.2%
15
 
4.2%
15
 
4.2%
14
 
3.9%
14
 
3.9%
11
 
3.1%
Other values (69) 165
46.5%
Uppercase Letter
ValueCountFrequency (%)
F 1
25.0%
P 1
25.0%
S 1
25.0%
B 1
25.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 356
88.3%
Common 43
 
10.7%
Latin 4
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
40
 
11.2%
25
 
7.0%
23
 
6.5%
18
 
5.1%
15
 
4.2%
15
 
4.2%
15
 
4.2%
14
 
3.9%
14
 
3.9%
11
 
3.1%
Other values (70) 166
46.6%
Latin
ValueCountFrequency (%)
F 1
25.0%
P 1
25.0%
S 1
25.0%
B 1
25.0%
Common
ValueCountFrequency (%)
( 21
48.8%
) 21
48.8%
1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
88.1%
ASCII 47
 
11.7%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
40
 
11.3%
25
 
7.0%
23
 
6.5%
18
 
5.1%
15
 
4.2%
15
 
4.2%
15
 
4.2%
14
 
3.9%
14
 
3.9%
11
 
3.1%
Other values (69) 165
46.5%
ASCII
ValueCountFrequency (%)
( 21
44.7%
) 21
44.7%
1
 
2.1%
F 1
 
2.1%
P 1
 
2.1%
S 1
 
2.1%
B 1
 
2.1%
None
ValueCountFrequency (%)
1
100.0%

공인유효기간(시작)
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
2000-12-22
17 
2011-11-17
2006-02-10
2010-11-24
2004-01-27
Other values (16)
16 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique16 ?
Unique (%)37.2%

Sample

1st row2007-06-01
2nd row2003-02-17
3rd row2005-02-17
4th row2004-01-20
5th row2001-01-12

Common Values

ValueCountFrequency (%)
2000-12-22 17
39.5%
2011-11-17 3
 
7.0%
2006-02-10 3
 
7.0%
2010-11-24 2
 
4.7%
2004-01-27 2
 
4.7%
2009-01-07 1
 
2.3%
2005-02-17 1
 
2.3%
2004-01-20 1
 
2.3%
2001-01-12 1
 
2.3%
2001-01-01 1
 
2.3%
Other values (11) 11
25.6%

Length

2023-12-12T09:18:05.007441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2000-12-22 17
39.5%
2006-02-10 3
 
7.0%
2011-11-17 3
 
7.0%
2010-11-24 2
 
4.7%
2004-01-27 2
 
4.7%
2007-06-01 1
 
2.3%
2009-09-21 1
 
2.3%
2009-10-08 1
 
2.3%
2006-02-17 1
 
2.3%
2010-11-17 1
 
2.3%
Other values (11) 11
25.6%
Distinct26
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2003-01-28 00:00:00
Maximum2022-12-31 00:00:00
2023-12-12T09:18:05.102577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:18:05.268170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)

비고
Text

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-12T09:18:05.476952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length34
Mean length20.255814
Min length17

Characters and Unicode

Total characters871
Distinct characters71
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)48.8%

Sample

1st row2010년 공인기간만료(금융위원회)
2nd row2013년 공인기간만료(방송통신위원회)
3rd row1급 공인기간 만료(미래창조과학부)
4th row2급 공인기간 만료(미래창조과학부)
5th row2022년 공인기간만료(과학기술정보통신부)
ValueCountFrequency (%)
공인기간만료(고용노동부 14
 
14.6%
2011년 13
 
13.5%
공인기간만료(교육부 10
 
10.4%
2019년 6
 
6.2%
2017년 4
 
4.2%
공인기간 3
 
3.1%
공인기간만료(고용고용노동부 3
 
3.1%
2014년 2
 
2.1%
공인기간만료(산업통상자원부 2
 
2.1%
2018년 2
 
2.1%
Other values (32) 37
38.5%
2023-12-12T09:18:05.774952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
 
6.7%
1 55
 
6.3%
2 54
 
6.2%
53
 
6.1%
46
 
5.3%
46
 
5.3%
( 43
 
4.9%
) 43
 
4.9%
42
 
4.8%
40
 
4.6%
Other values (61) 391
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 527
60.5%
Decimal Number 194
 
22.3%
Space Separator 53
 
6.1%
Open Punctuation 43
 
4.9%
Close Punctuation 43
 
4.9%
Dash Punctuation 8
 
0.9%
Other Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
46
 
8.7%
46
 
8.7%
42
 
8.0%
40
 
7.6%
40
 
7.6%
38
 
7.2%
38
 
7.2%
38
 
7.2%
21
 
4.0%
21
 
4.0%
Other values (46) 157
29.8%
Decimal Number
ValueCountFrequency (%)
0 58
29.9%
1 55
28.4%
2 54
27.8%
9 8
 
4.1%
7 7
 
3.6%
5 3
 
1.5%
8 3
 
1.5%
4 2
 
1.0%
3 2
 
1.0%
6 2
 
1.0%
Space Separator
ValueCountFrequency (%)
53
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 527
60.5%
Common 344
39.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
46
 
8.7%
46
 
8.7%
42
 
8.0%
40
 
7.6%
40
 
7.6%
38
 
7.2%
38
 
7.2%
38
 
7.2%
21
 
4.0%
21
 
4.0%
Other values (46) 157
29.8%
Common
ValueCountFrequency (%)
0 58
16.9%
1 55
16.0%
2 54
15.7%
53
15.4%
( 43
12.5%
) 43
12.5%
9 8
 
2.3%
- 8
 
2.3%
7 7
 
2.0%
5 3
 
0.9%
Other values (5) 12
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 527
60.5%
ASCII 344
39.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
16.9%
1 55
16.0%
2 54
15.7%
53
15.4%
( 43
12.5%
) 43
12.5%
9 8
 
2.3%
- 8
 
2.3%
7 7
 
2.0%
5 3
 
0.9%
Other values (5) 12
 
3.5%
Hangul
ValueCountFrequency (%)
46
 
8.7%
46
 
8.7%
42
 
8.0%
40
 
7.6%
40
 
7.6%
38
 
7.2%
38
 
7.2%
38
 
7.2%
21
 
4.0%
21
 
4.0%
Other values (46) 157
29.8%

Correlations

2023-12-12T09:18:05.857085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소관부처자격종목등급자격관리자공인유효기간(시작)공인유효기간(만료)비고
소관부처1.0001.0000.0000.9950.9770.9790.955
자격종목1.0001.0000.0001.0000.0000.9850.967
등급0.0000.0001.0000.9460.9180.8850.934
자격관리자0.9951.0000.9461.0000.9770.9840.983
공인유효기간(시작)0.9770.0000.9180.9771.0000.9640.971
공인유효기간(만료)0.9790.9850.8850.9840.9641.0000.994
비고0.9550.9670.9340.9830.9710.9941.000
2023-12-12T09:18:06.174661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소관부처공인유효기간(시작)등급
소관부처1.0000.7080.000
공인유효기간(시작)0.7081.0000.521
등급0.0000.5211.000
2023-12-12T09:18:06.247674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소관부처등급공인유효기간(시작)
소관부처1.0000.0000.708
등급0.0001.0000.521
공인유효기간(시작)0.7080.5211.000

Missing values

2023-12-12T09:18:03.333322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:18:03.435996image/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

소관부처자격종목등급자격관리자공인유효기간(시작)공인유효기간(만료)비고
0금융위원회재무설계사등급없음한국 FPSB2007-06-012010-05-312010년 공인기간만료(금융위원회)
1과학기술정보통신부공무원정보이용능력평가(NIT)등급없음(사)한국정보통신진흥협회2003-02-172013-02-162013년 공인기간만료(방송통신위원회)
2과학기술정보통신부정보보호전문가(SIS)1급(사)한국인터넷진흥원2005-02-172014-11-161급 공인기간 만료(미래창조과학부)
3과학기술정보통신부정보보호전문가(SIS)2급(사)한국인터넷진흥원2004-01-202016-09-292급 공인기간 만료(미래창조과학부)
4과학기술정보통신부인터넷정보관리사전문가, 1급, 2급(사)한국정보통신진흥협회2001-01-122022-02-162022년 공인기간만료(과학기술정보통신부)
5교육부한자능력급수4급한국어문회2001-01-012005-02-092005년 공인등급변경(교육과학기술부)
6교육부공용한자검정특1급, 2급, 3급(사)한중문자교육협회2011-11-172012-05-242012년 공인취소(교육과학기술부)
7교육부한자한문학습지도사1급, 2급, 3급한국교육문화회2010-11-242012-11-232012년 공인기간만료(교육과학기술부)
8교육부한국한자어능력인증시험1급, 2급, 3급, 4급(주)조선에듀케이션2011-11-172015-11-162015년 공인기간만료(교육부)
9교육부한자급수자격검정사범, 1급, 준1급, 2급, 준2급(사)대한민국한자교육연구회2004-01-272016-02-092016년 공인기간만료(교육부), 2016-10-01 신규공인
소관부처자격종목등급자격관리자공인유효기간(시작)공인유효기간(만료)비고
33고용노동부산업전자기기제작사등급없음대한상공회의소2000-12-222011-02-082011년 공인기간만료(고용노동부)
34고용노동부자동화설비제어사등급없음대한상공회의소2000-12-222011-02-082011년 공인기간만료(고용노동부)
35고용노동부치공구제작사등급없음대한상공회의소2000-12-222011-02-082011년 공인기간만료(고용노동부)
36고용노동부CNC기계절삭가공사등급없음대한상공회의소2000-12-222011-02-082011년 공인기간만료(고용노동부)
37고용노동부펜글씨검정1급, 2급, 3급(사)대한글씨검정교육회2000-12-222011-02-082011년 공인기간만료(고용노동부)
38고용노동부기계설계제도사등급없음대한상공회의소2000-12-222014-02-082014년 공인기간만료(고용노동부)
39고용노동부컴퓨터 운용사등급없음대한상공회의소2000-12-222017-02-082017년 공인기간만료(고용고용노동부), 2017-02-09 공인자격폐지
40고용노동부가구설계제도사등급없음대한상공회의소2000-12-222017-02-082017년 공인기간만료(고용고용노동부), 2017-02-09 공인자격폐지
41고용노동부문서실무사1급, 2급, 3급, 4급(사)한국정보관리협회2000-12-222018-02-082018년 공인기간만료(고용고용노동부)
42조달청구매자재관리사등급없음(사)한국구매자재관리협회2000-12-272007-12-262007년 공인기간만료(조달청)