Overview

Dataset statistics

Number of variables5
Number of observations1514
Missing cells0
Missing cells (%)0.0%
Duplicate rows530
Duplicate rows (%)35.0%
Total size in memory60.7 KiB
Average record size in memory41.1 B

Variable types

Categorical3
Text1
Numeric1

Dataset

Description중소벤처기업진흥공단에서 운영하는 중소벤처기업연수원의 NCS 구분에 따른 과정 현황과 연수비 정보입니다.- 컬럼명 : NCS대분류, NCS중분류, NCS소분류, 훈련과정명, 연수비
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15124963/fileData.do

Alerts

Dataset has 530 (35.0%) duplicate rowsDuplicates
NCS(국가직무능력표준)_중분류 is highly overall correlated with NCS(국가직무능력표준)_대분류 and 1 other fieldsHigh correlation
NCS(국가직무능력표준)_대분류 is highly overall correlated with NCS(국가직무능력표준)_중분류 and 1 other fieldsHigh correlation
NCS(국가직무능력표준)_소분류 is highly overall correlated with NCS(국가직무능력표준)_대분류 and 1 other fieldsHigh correlation
연수비 has 595 (39.3%) zerosZeros

Reproduction

Analysis started2023-12-12 02:23:42.857419
Analysis finished2023-12-12 02:23:43.631051
Duration0.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

NCS(국가직무능력표준)_대분류
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
02.경영ㆍ회계ㆍ사무
858 
15.기계
397 
19.전기ㆍ전자
 
69
16.재료
 
64
20.정보통신
 
50
Other values (7)
 
76

Length

Max length17
Median length11
Mean length8.7040951
Min length5

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row02.경영ㆍ회계ㆍ사무
2nd row02.경영ㆍ회계ㆍ사무
3rd row02.경영ㆍ회계ㆍ사무
4th row02.경영ㆍ회계ㆍ사무
5th row02.경영ㆍ회계ㆍ사무

Common Values

ValueCountFrequency (%)
02.경영ㆍ회계ㆍ사무 858
56.7%
15.기계 397
26.2%
19.전기ㆍ전자 69
 
4.6%
16.재료 64
 
4.2%
20.정보통신 50
 
3.3%
17.화학 31
 
2.0%
10.영업판매 29
 
1.9%
04.교육ㆍ자연ㆍ사회과학 5
 
0.3%
23.환경ㆍ에너지ㆍ안전 4
 
0.3%
01.사업관리 3
 
0.2%
Other values (2) 4
 
0.3%

Length

2023-12-12T11:23:43.716632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
02.경영ㆍ회계ㆍ사무 858
56.7%
15.기계 397
26.2%
19.전기ㆍ전자 69
 
4.6%
16.재료 64
 
4.2%
20.정보통신 50
 
3.3%
17.화학 31
 
2.0%
10.영업판매 29
 
1.9%
04.교육ㆍ자연ㆍ사회과학 5
 
0.3%
23.환경ㆍ에너지ㆍ안전 4
 
0.3%
01.사업관리 3
 
0.2%
Other values (2) 4
 
0.3%

NCS(국가직무능력표준)_중분류
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
04.생산ㆍ품질관리
520 
02.총무ㆍ인사
219 
03.기계조립ㆍ관리
203 
01.기계설계
117 
01.기획사무
65 
Other values (20)
390 

Length

Max length14
Median length11
Mean length8.5779392
Min length4

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row02.총무ㆍ인사
2nd row02.총무ㆍ인사
3rd row02.총무ㆍ인사
4th row02.총무ㆍ인사
5th row02.총무ㆍ인사

Common Values

ValueCountFrequency (%)
04.생산ㆍ품질관리 520
34.3%
02.총무ㆍ인사 219
14.5%
03.기계조립ㆍ관리 203
 
13.4%
01.기계설계 117
 
7.7%
01.기획사무 65
 
4.3%
01.금속재료 64
 
4.2%
01.정보기술 48
 
3.2%
03.재무ㆍ회계 47
 
3.1%
02.기계가공 43
 
2.8%
01.전기 38
 
2.5%
Other values (15) 150
 
9.9%

Length

2023-12-12T11:23:43.873509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
04.생산ㆍ품질관리 520
34.3%
02.총무ㆍ인사 219
14.5%
03.기계조립ㆍ관리 203
 
13.4%
01.기계설계 117
 
7.7%
01.기획사무 65
 
4.3%
01.금속재료 64
 
4.2%
01.정보기술 48
 
3.2%
03.재무ㆍ회계 47
 
3.1%
02.기계가공 43
 
2.8%
01.전기 38
 
2.5%
Other values (16) 151
 
10.0%

NCS(국가직무능력표준)_소분류
Categorical

HIGH CORRELATION 

Distinct40
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
01.생산관리
429 
01.기계조립
203 
02.인사ㆍ조직
153 
02.기계설계
108 
02.품질관리
78 
Other values (35)
543 

Length

Max length14
Median length7
Mean length7.2899604
Min length4

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row02.인사ㆍ조직
2nd row02.인사ㆍ조직
3rd row02.인사ㆍ조직
4th row02.인사ㆍ조직
5th row02.인사ㆍ조직

Common Values

ValueCountFrequency (%)
01.생산관리 429
28.3%
01.기계조립 203
13.4%
02.인사ㆍ조직 153
 
10.1%
02.기계설계 108
 
7.1%
02.품질관리 78
 
5.2%
03.일반사무 61
 
4.0%
<NA> 55
 
3.6%
01.경영기획 49
 
3.2%
02.회계 47
 
3.1%
02.정보기술개발 40
 
2.6%
Other values (30) 291
19.2%

Length

2023-12-12T11:23:44.029537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01.생산관리 429
28.3%
01.기계조립 203
13.4%
02.인사ㆍ조직 153
 
10.1%
02.기계설계 108
 
7.1%
02.품질관리 78
 
5.2%
03.일반사무 61
 
4.0%
na 55
 
3.6%
01.경영기획 49
 
3.2%
02.회계 47
 
3.1%
02.정보기술개발 40
 
2.6%
Other values (30) 291
19.2%

훈련과정명
Text

Distinct741
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2023-12-12T11:23:44.364227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length41
Mean length20.711361
Min length5

Characters and Unicode

Total characters31357
Distinct characters504
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique215 ?
Unique (%)14.2%

Sample

1st row초급사원 직무역량 향상과정
2nd row초급사원 직무역량 향상과정
3rd row직무태도 향상 및 마인드 변화
4th row직무태도 향상 및 마인드 변화
5th row중소기업혁신바우처 사업 소개
ValueCountFrequency (%)
스마트공장 241
 
3.7%
및 206
 
3.1%
웨비나 180
 
2.7%
실무 178
 
2.7%
위한 172
 
2.6%
plc 116
 
1.8%
활용 76
 
1.2%
기초 64
 
1.0%
구축 64
 
1.0%
이해 60
 
0.9%
Other values (1384) 5207
79.3%
2023-12-12T11:23:44.923444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5375
 
17.1%
기 782
 
2.5%
스 540
 
1.7%
공 515
 
1.6%
장 494
 
1.6%
트 453
 
1.4%
마 440
 
1.4%
] 368
 
1.2%
[ 368
 
1.2%
무 354
 
1.1%
Other values (494) 21668
69.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20725
66.1%
Space Separator 5375
 
17.1%
Uppercase Letter 2266
 
7.2%
Lowercase Letter 806
 
2.6%
Close Punctuation 683
 
2.2%
Open Punctuation 683
 
2.2%
Decimal Number 320
 
1.0%
Other Punctuation 308
 
1.0%
Dash Punctuation 83
 
0.3%
Connector Punctuation 73
 
0.2%
Other values (2) 35
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 782
 
3.8%
스 540
 
2.6%
공 515
 
2.5%
장 494
 
2.4%
트 453
 
2.2%
마 440
 
2.1%
무 354
 
1.7%
이 348
 
1.7%
리 339
 
1.6%
실 338
 
1.6%
Other values (413) 16122
77.8%
Uppercase Letter
ValueCountFrequency (%)
C 301
13.3%
P 279
12.3%
E 220
9.7%
L 199
8.8%
S 192
8.5%
A 181
 
8.0%
I 134
 
5.9%
M 121
 
5.3%
T 91
 
4.0%
D 86
 
3.8%
Other values (16) 462
20.4%
Lowercase Letter
ValueCountFrequency (%)
o 133
16.5%
e 81
10.0%
t 80
9.9%
r 68
 
8.4%
n 62
 
7.7%
l 49
 
6.1%
i 45
 
5.6%
c 39
 
4.8%
a 37
 
4.6%
u 35
 
4.3%
Other values (13) 177
22.0%
Other Punctuation
ValueCountFrequency (%)
, 123
39.9%
! 67
21.8%
/ 54
17.5%
· 19
 
6.2%
: 14
 
4.5%
" 10
 
3.2%
& 8
 
2.6%
? 5
 
1.6%
＆ 4
 
1.3%
; 2
 
0.6%
Decimal Number
ValueCountFrequency (%)
3 71
22.2%
4 71
22.2%
2 57
17.8%
0 35
10.9%
9 28
 
8.8%
5 22
 
6.9%
1 20
 
6.2%
6 14
 
4.4%
7 2
 
0.6%
Close Punctuation
ValueCountFrequency (%)
] 368
53.9%
) 314
46.0%
』 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
[ 368
53.9%
( 314
46.0%
『 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 29
90.6%
~ 3
 
9.4%
Space Separator
ValueCountFrequency (%)
5375
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 73
100.0%
Letter Number
ValueCountFrequency (%)
Ⅳ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20723
66.1%
Common 7557
 
24.1%
Latin 3075
 
9.8%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 782
 
3.8%
스 540
 
2.6%
공 515
 
2.5%
장 494
 
2.4%
트 453
 
2.2%
마 440
 
2.1%
무 354
 
1.7%
이 348
 
1.7%
리 339
 
1.6%
실 338
 
1.6%
Other values (411) 16120
77.8%
Latin
ValueCountFrequency (%)
C 301
 
9.8%
P 279
 
9.1%
E 220
 
7.2%
L 199
 
6.5%
S 192
 
6.2%
A 181
 
5.9%
I 134
 
4.4%
o 133
 
4.3%
M 121
 
3.9%
T 91
 
3.0%
Other values (40) 1224
39.8%
Common
ValueCountFrequency (%)
5375
71.1%
] 368
 
4.9%
[ 368
 
4.9%
( 314
 
4.2%
) 314
 
4.2%
, 123
 
1.6%
- 83
 
1.1%
_ 73
 
1.0%
3 71
 
0.9%
4 71
 
0.9%
Other values (21) 397
 
5.3%
Han
ValueCountFrequency (%)
上 1
50.0%
飛 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20723
66.1%
ASCII 10604
33.8%
None 25
 
0.1%
Number Forms 3
 
< 0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5375
50.7%
] 368
 
3.5%
[ 368
 
3.5%
( 314
 
3.0%
) 314
 
3.0%
C 301
 
2.8%
P 279
 
2.6%
E 220
 
2.1%
L 199
 
1.9%
S 192
 
1.8%
Other values (66) 2674
25.2%
Hangul
ValueCountFrequency (%)
기 782
 
3.8%
스 540
 
2.6%
공 515
 
2.5%
장 494
 
2.4%
트 453
 
2.2%
마 440
 
2.1%
무 354
 
1.7%
이 348
 
1.7%
리 339
 
1.6%
실 338
 
1.6%
Other values (411) 16120
77.8%
None
ValueCountFrequency (%)
· 19
76.0%
＆ 4
 
16.0%
』 1
 
4.0%
『 1
 
4.0%
Number Forms
ValueCountFrequency (%)
Ⅳ 3
100.0%
CJK
ValueCountFrequency (%)
上 1
50.0%
飛 1
50.0%

연수비
Real number (ℝ)

ZEROS 

Distinct54
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean342637.75
Minimum-1
Maximum19500000
Zeros595
Zeros (%)39.3%
Negative5
Negative (%)0.3%
Memory size13.4 KiB
2023-12-12T11:23:45.074479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median253000
Q3341000
95-th percentile470800
Maximum19500000
Range19500001
Interquartile range (IQR)341000

Descriptive statistics

Standard deviation1104882.8
Coefficient of variation (CV)3.2246383
Kurtosis111.95703
Mean342637.75
Median Absolute Deviation (MAD)187000
Skewness9.5986338
Sum5.1875356 × 108
Variance1.220766 × 1012
MonotonicityNot monotonic
2023-12-12T11:23:45.218863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 595
39.3%
330000 270
17.8%
341000 167
 
11.0%
440000 164
 
10.8%
242000 101
 
6.7%
253000 43
 
2.8%
429000 40
 
2.6%
90000 32
 
2.1%
539000 25
 
1.7%
528000 10
 
0.7%
Other values (44) 67
 
4.4%
ValueCountFrequency (%)
-1 5
 
0.3%
0 595
39.3%
45000 1
 
0.1%
60000 1
 
0.1%
80000 1
 
0.1%
90000 32
 
2.1%
105000 8
 
0.5%
120000 6
 
0.4%
220000 1
 
0.1%
242000 101
 
6.7%
ValueCountFrequency (%)
19500000 1
0.1%
13000000 1
0.1%
12000000 1
0.1%
11200000 1
0.1%
10750000 1
0.1%
10326000 1
0.1%
9900000 1
0.1%
9800000 1
0.1%
9500000 1
0.1%
9200000 1
0.1%

Interactions

2023-12-12T11:23:43.276962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:23:45.312857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NCS(국가직무능력표준)_대분류NCS(국가직무능력표준)_중분류NCS(국가직무능력표준)_소분류연수비
NCS(국가직무능력표준)_대분류1.0001.0001.0000.000
NCS(국가직무능력표준)_중분류1.0001.0001.0000.000
NCS(국가직무능력표준)_소분류1.0001.0001.0000.000
연수비0.0000.0000.0001.000
2023-12-12T11:23:45.403997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
NCS(국가직무능력표준)_중분류NCS(국가직무능력표준)_대분류NCS(국가직무능력표준)_소분류
NCS(국가직무능력표준)_중분류1.0000.9960.995
NCS(국가직무능력표준)_대분류0.9961.0000.991
NCS(국가직무능력표준)_소분류0.9950.9911.000
2023-12-12T11:23:45.701911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연수비NCS(국가직무능력표준)_대분류NCS(국가직무능력표준)_중분류NCS(국가직무능력표준)_소분류
연수비1.0000.0000.0000.000
NCS(국가직무능력표준)_대분류0.0001.0000.9960.991
NCS(국가직무능력표준)_중분류0.0000.9961.0000.995
NCS(국가직무능력표준)_소분류0.0000.9910.9951.000

Missing values

2023-12-12T11:23:43.459924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:23:43.587814image/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

NCS(국가직무능력표준)_대분류NCS(국가직무능력표준)_중분류NCS(국가직무능력표준)_소분류훈련과정명연수비
002.경영ㆍ회계ㆍ사무02.총무ㆍ인사02.인사ㆍ조직초급사원 직무역량 향상과정330000
102.경영ㆍ회계ㆍ사무02.총무ㆍ인사02.인사ㆍ조직초급사원 직무역량 향상과정330000
202.경영ㆍ회계ㆍ사무02.총무ㆍ인사02.인사ㆍ조직직무태도 향상 및 마인드 변화330000
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