Overview

Dataset statistics

Number of variables11
Number of observations375
Missing cells0
Missing cells (%)0.0%
Duplicate rows15
Duplicate rows (%)4.0%
Total size in memory33.1 KiB
Average record size in memory90.4 B

Variable types

Categorical8
Text2
Numeric1

Dataset

Description중소벤처기업 재직자의 역량강화를 위해 연수사업을 담당하는 중소벤처기업연수원의 NCS과정 운영현황입니다. 해당 목록에서 아래의 칼럼명에 해당하는 데이터를 확인해 주십시오.- 칼럼명: 인정연도, 훈련과정명, 훈련과정아이디, 코드명, 코드, 주 훈련대상, 특화과정, 심사유형, 심사연도 회차, 인정유효 시작일자, 인정유효 종료일자
Author중소벤처기업진흥공단
URLhttps://www.data.go.kr/data/15020238/fileData.do

Alerts

주 훈련대상 has constant value ""Constant
특화과정 has constant value ""Constant
Dataset has 15 (4.0%) duplicate rowsDuplicates
인정연도 is highly overall correlated with 심사유형 and 3 other fieldsHigh correlation
심사유형 is highly overall correlated with 인정연도 and 3 other fieldsHigh correlation
인정유효 종료일자 is highly overall correlated with 인정연도 and 3 other fieldsHigh correlation
심사연도 회차 is highly overall correlated with 인정연도 and 3 other fieldsHigh correlation
인정유효 시작일자 is highly overall correlated with 인정연도 and 3 other fieldsHigh correlation
코드 is highly overall correlated with 코드명High correlation
코드명 is highly overall correlated with 코드High correlation

Reproduction

Analysis started2023-12-12 07:36:51.339671
Analysis finished2023-12-12 07:36:52.493248
Duration1.15 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

인정연도
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2022
119 
2021
102 
2020
88 
2019
52 
2018
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2022 119
31.7%
2021 102
27.2%
2020 88
23.5%
2019 52
13.9%
2018 14
 
3.7%

Length

2023-12-12T16:36:52.571645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:52.704716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 119
31.7%
2021 102
27.2%
2020 88
23.5%
2019 52
13.9%
2018 14
 
3.7%

훈련과정명
Text

Distinct230
Distinct (%)61.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T16:36:53.036053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length29
Mean length15.637333
Min length4

Characters and Unicode

Total characters5864
Distinct characters320
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)31.2%

Sample

1st rowAutoCAD 2D도면작성-기초
2nd rowAutoCAD 2D도면작성-심화
3rd rowCATIA 3차원설계-기초
4th rowCATIA 3차원설계-심화
5th rowCreo(Pro/E) 3차원설계-기초
ValueCountFrequency (%)
실무 43
 
3.6%
및 34
 
2.9%
위한 25
 
2.1%
4차 19
 
1.6%
기초 16
 
1.3%
3차원설계-기초 14
 
1.2%
알기쉬운 14
 
1.2%
배우는 14
 
1.2%
plc 13
 
1.1%
inventor 11
 
0.9%
Other values (432) 983
82.9%
2023-12-12T16:36:53.532140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
822
 
14.0%
기 178
 
3.0%
무 101
 
1.7%
계 100
 
1.7%
실 96
 
1.6%
( 86
 
1.5%
) 85
 
1.4%
리 83
 
1.4%
설 80
 
1.4%
C 78
 
1.3%
Other values (310) 4155
70.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3754
64.0%
Space Separator 822
 
14.0%
Uppercase Letter 555
 
9.5%
Lowercase Letter 338
 
5.8%
Decimal Number 115
 
2.0%
Open Punctuation 86
 
1.5%
Close Punctuation 85
 
1.4%
Dash Punctuation 56
 
1.0%
Other Punctuation 46
 
0.8%
Math Symbol 7
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 178
 
4.7%
무 101
 
2.7%
계 100
 
2.7%
실 96
 
2.6%
리 83
 
2.2%
설 80
 
2.1%
원 72
 
1.9%
차 66
 
1.8%
관 64
 
1.7%
초 64
 
1.7%
Other values (246) 2850
75.9%
Uppercase Letter
ValueCountFrequency (%)
C 78
14.1%
P 69
12.4%
A 59
10.6%
S 53
9.5%
L 48
8.6%
E 43
7.7%
I 36
 
6.5%
M 28
 
5.0%
D 25
 
4.5%
W 15
 
2.7%
Other values (14) 101
18.2%
Lowercase Letter
ValueCountFrequency (%)
o 60
17.8%
r 47
13.9%
e 38
11.2%
n 37
10.9%
t 22
 
6.5%
i 18
 
5.3%
k 17
 
5.0%
l 15
 
4.4%
s 13
 
3.8%
c 12
 
3.6%
Other values (10) 59
17.5%
Decimal Number
ValueCountFrequency (%)
3 52
45.2%
4 26
22.6%
9 8
 
7.0%
2 6
 
5.2%
5 6
 
5.2%
0 6
 
5.2%
1 5
 
4.3%
6 4
 
3.5%
7 2
 
1.7%
Other Punctuation
ValueCountFrequency (%)
, 21
45.7%
/ 18
39.1%
! 3
 
6.5%
. 2
 
4.3%
· 1
 
2.2%
& 1
 
2.2%
Space Separator
ValueCountFrequency (%)
822
100.0%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3754
64.0%
Common 1217
 
20.8%
Latin 893
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 178
 
4.7%
무 101
 
2.7%
계 100
 
2.7%
실 96
 
2.6%
리 83
 
2.2%
설 80
 
2.1%
원 72
 
1.9%
차 66
 
1.8%
관 64
 
1.7%
초 64
 
1.7%
Other values (246) 2850
75.9%
Latin
ValueCountFrequency (%)
C 78
 
8.7%
P 69
 
7.7%
o 60
 
6.7%
A 59
 
6.6%
S 53
 
5.9%
L 48
 
5.4%
r 47
 
5.3%
E 43
 
4.8%
e 38
 
4.3%
n 37
 
4.1%
Other values (34) 361
40.4%
Common
ValueCountFrequency (%)
822
67.5%
( 86
 
7.1%
) 85
 
7.0%
- 56
 
4.6%
3 52
 
4.3%
4 26
 
2.1%
, 21
 
1.7%
/ 18
 
1.5%
9 8
 
0.7%
+ 7
 
0.6%
Other values (10) 36
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3754
64.0%
ASCII 2109
36.0%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
822
39.0%
( 86
 
4.1%
) 85
 
4.0%
C 78
 
3.7%
P 69
 
3.3%
o 60
 
2.8%
A 59
 
2.8%
- 56
 
2.7%
S 53
 
2.5%
3 52
 
2.5%
Other values (53) 689
32.7%
Hangul
ValueCountFrequency (%)
기 178
 
4.7%
무 101
 
2.7%
계 100
 
2.7%
실 96
 
2.6%
리 83
 
2.2%
설 80
 
2.1%
원 72
 
1.9%
차 66
 
1.8%
관 64
 
1.7%
초 64
 
1.7%
Other values (246) 2850
75.9%
None
ValueCountFrequency (%)
· 1
100.0%

훈련과정아이디
Text

Distinct358
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2023-12-12T16:36:53.807518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters6375
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique343 ?
Unique (%)91.5%

Sample

1st rowAIG20180000194046
2nd rowAIG20180000197237
3rd rowAIG20180000197727
4th rowAIG20180000197779
5th rowAIG20180000197816
ValueCountFrequency (%)
aig20210000365271 4
 
1.1%
aig20210000365064 2
 
0.5%
aig20210000343683 2
 
0.5%
aig20210000349845 2
 
0.5%
aig20210000349914 2
 
0.5%
aig20210000351381 2
 
0.5%
aig20210000351507 2
 
0.5%
aig20210000353305 2
 
0.5%
aig20210000351284 2
 
0.5%
aig20210000343779 2
 
0.5%
Other values (348) 353
94.1%
2023-12-12T16:36:54.204046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2165
34.0%
2 1018
16.0%
1 479
 
7.5%
A 375
 
5.9%
I 375
 
5.9%
G 375
 
5.9%
3 368
 
5.8%
9 259
 
4.1%
5 231
 
3.6%
6 206
 
3.2%
Other values (3) 524
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5250
82.4%
Uppercase Letter 1125
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2165
41.2%
2 1018
19.4%
1 479
 
9.1%
3 368
 
7.0%
9 259
 
4.9%
5 231
 
4.4%
6 206
 
3.9%
8 182
 
3.5%
7 180
 
3.4%
4 162
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
A 375
33.3%
I 375
33.3%
G 375
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 5250
82.4%
Latin 1125
 
17.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2165
41.2%
2 1018
19.4%
1 479
 
9.1%
3 368
 
7.0%
9 259
 
4.9%
5 231
 
4.4%
6 206
 
3.9%
8 182
 
3.5%
7 180
 
3.4%
4 162
 
3.1%
Latin
ValueCountFrequency (%)
A 375
33.3%
I 375
33.3%
G 375
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6375
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2165
34.0%
2 1018
16.0%
1 479
 
7.5%
A 375
 
5.9%
I 375
 
5.9%
G 375
 
5.9%
3 368
 
5.8%
9 259
 
4.1%
5 231
 
3.6%
6 206
 
3.2%
Other values (3) 524
 
8.2%

코드명
Categorical

HIGH CORRELATION 

Distinct42
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
기계소프트웨어개발
60 
기계요소설계
47 
QM/QC관리
38 
공정관리
32 
경영기획
32 
Other values (37)
166 

Length

Max length13
Median length11
Mean length5.8613333
Min length2

Unique

Unique3 ?
Unique (%)0.8%

Sample

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

Common Values

ValueCountFrequency (%)
기계소프트웨어개발 60
16.0%
기계요소설계 47
12.5%
QM/QC관리 38
 
10.1%
공정관리 32
 
8.5%
경영기획 32
 
8.5%
인사 12
 
3.2%
사무행정 12
 
3.2%
산업용전자기기하드웨어개발 10
 
2.7%
전기기기설계 10
 
2.7%
경력지도 8
 
2.1%
Other values (32) 114
30.4%

Length

2023-12-12T16:36:54.351794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기계소프트웨어개발 60
15.9%
기계요소설계 47
12.5%
qm/qc관리 38
 
10.1%
공정관리 32
 
8.5%
경영기획 32
 
8.5%
인사 12
 
3.2%
사무행정 12
 
3.2%
산업용전자기기하드웨어개발 10
 
2.7%
전기기기설계 10
 
2.7%
사출성형 8
 
2.1%
Other values (33) 116
30.8%

코드
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9305669.4
Minimum1010102
Maximum19031102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2023-12-12T16:36:54.481934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010102
5-th percentile2010101
Q12040103
median15010201
Q315030102
95-th percentile19010501
Maximum19031102
Range18021000
Interquartile range (IQR)12989999

Descriptive statistics

Standard deviation6838552.4
Coefficient of variation (CV)0.73488022
Kurtosis-1.8653362
Mean9305669.4
Median Absolute Deviation (MAD)4020601
Skewness-0.041339662
Sum3.489626 × 109
Variance4.6765799 × 1013
MonotonicityNot monotonic
2023-12-12T16:36:54.636564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
15030102 60
16.0%
15010201 47
12.5%
2040201 38
 
10.1%
2040103 32
 
8.5%
2010101 32
 
8.5%
2020201 12
 
3.2%
2020302 12
 
3.2%
19030201 10
 
2.7%
19010501 10
 
2.7%
2040101 8
 
2.1%
Other values (31) 114
30.4%
ValueCountFrequency (%)
1010102 4
 
1.1%
2010101 32
8.5%
2010301 3
 
0.8%
2020103 1
 
0.3%
2020201 12
 
3.2%
2020202 1
 
0.3%
2020301 2
 
0.5%
2020302 12
 
3.2%
2030201 5
 
1.3%
2030202 4
 
1.1%
ValueCountFrequency (%)
19031102 4
 
1.1%
19030802 2
 
0.5%
19030201 10
2.7%
19010501 10
2.7%
17040202 2
 
0.5%
17040201 3
 
0.8%
17040105 8
2.1%
17040104 2
 
0.5%
16010303 1
 
0.3%
16010301 2
 
0.5%

주 훈련대상
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
사업주(위탁)
375 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사업주(위탁)
2nd row사업주(위탁)
3rd row사업주(위탁)
4th row사업주(위탁)
5th row사업주(위탁)

Common Values

ValueCountFrequency (%)
사업주(위탁) 375
100.0%

Length

2023-12-12T16:36:54.784971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:54.896276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업주(위탁 375
100.0%

특화과정
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
우수과정
375 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
우수과정 375
100.0%

Length

2023-12-12T16:36:55.013389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:55.110071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수과정 375
100.0%

심사유형
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
통합심사
256 
(통합)심사
119 

Length

Max length6
Median length4
Mean length4.6346667
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
통합심사 256
68.3%
(통합)심사 119
31.7%

Length

2023-12-12T16:36:55.216101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:55.327385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
통합심사 256
68.3%
통합)심사 119
31.7%

심사연도 회차
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2021-34차
119 
2020-11차
59 
2020-2차
46 
2021-5차
43 
2019-9차
42 
Other values (3)
66 

Length

Max length8
Median length8
Mean length7.5093333
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-3차
2nd row2018-3차
3rd row2018-3차
4th row2018-3차
5th row2018-3차

Common Values

ValueCountFrequency (%)
2021-34차 119
31.7%
2020-11차 59
15.7%
2020-2차 46
 
12.3%
2021-5차 43
 
11.5%
2019-9차 42
 
11.2%
2019-2차 39
 
10.4%
2018-3차 14
 
3.7%
2018-10차 13
 
3.5%

Length

2023-12-12T16:36:55.453399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:55.576100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021-34차 119
31.7%
2020-11차 59
15.7%
2020-2차 46
 
12.3%
2021-5차 43
 
11.5%
2019-9차 42
 
11.2%
2019-2차 39
 
10.4%
2018-3차 14
 
3.7%
2018-10차 13
 
3.5%

인정유효 시작일자
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2022-01-01
119 
2021-01-01
59 
2020-07-16
46 
2021-07-01
43 
2020-01-01
41 
Other values (4)
67 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row2018-07-02
2nd row2018-07-02
3rd row2018-07-02
4th row2018-07-02
5th row2018-07-02

Common Values

ValueCountFrequency (%)
2022-01-01 119
31.7%
2021-01-01 59
15.7%
2020-07-16 46
 
12.3%
2021-07-01 43
 
11.5%
2020-01-01 41
 
10.9%
2019-07-01 39
 
10.4%
2018-07-02 14
 
3.7%
2019-01-01 13
 
3.5%
2020-03-01 1
 
0.3%

Length

2023-12-12T16:36:55.753975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:55.923348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-01-01 119
31.7%
2021-01-01 59
15.7%
2020-07-16 46
 
12.3%
2021-07-01 43
 
11.5%
2020-01-01 41
 
10.9%
2019-07-01 39
 
10.4%
2018-07-02 14
 
3.7%
2019-01-01 13
 
3.5%
2020-03-01 1
 
0.3%

인정유효 종료일자
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size3.1 KiB
2022-12-31
103 
2022-12-01
102 
2022-10-24
88 
2021-06-30
53 
2024-12-31
16 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-06-30
2nd row2021-06-30
3rd row2021-06-30
4th row2021-06-30
5th row2021-06-30

Common Values

ValueCountFrequency (%)
2022-12-31 103
27.5%
2022-12-01 102
27.2%
2022-10-24 88
23.5%
2021-06-30 53
14.1%
2024-12-31 16
 
4.3%
2021-12-31 13
 
3.5%

Length

2023-12-12T16:36:56.101119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:36:56.257325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 103
27.5%
2022-12-01 102
27.2%
2022-10-24 88
23.5%
2021-06-30 53
14.1%
2024-12-31 16
 
4.3%
2021-12-31 13
 
3.5%

Interactions

2023-12-12T16:36:51.949851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:36:56.375592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인정연도코드명코드심사유형심사연도 회차인정유효 시작일자인정유효 종료일자
인정연도1.0000.3840.0091.0001.0001.0000.917
코드명0.3841.0001.0000.2540.4820.5910.568
코드0.0091.0001.0000.0000.1650.2020.132
심사유형1.0000.2540.0001.0001.0001.0001.000
심사연도 회차1.0000.4820.1651.0001.0001.0000.957
인정유효 시작일자1.0000.5910.2021.0001.0001.0000.983
인정유효 종료일자0.9170.5680.1321.0000.9570.9831.000
2023-12-12T16:36:56.825780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인정연도심사유형인정유효 종료일자심사연도 회차코드명인정유효 시작일자
인정연도1.0000.9960.8730.9960.1790.995
심사유형0.9961.0000.9950.9920.1910.991
인정유효 종료일자0.8730.9951.0000.8900.2530.888
심사연도 회차0.9960.9920.8901.0000.1950.999
코드명0.1790.1910.2530.1951.0000.244
인정유효 시작일자0.9950.9910.8880.9990.2441.000
2023-12-12T16:36:56.936749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
코드인정연도코드명심사유형심사연도 회차인정유효 시작일자인정유효 종료일자
코드1.0000.0260.9510.0150.0920.1100.080
인정연도0.0261.0000.1790.9960.9960.9950.873
코드명0.9510.1791.0000.1910.1950.2440.253
심사유형0.0150.9960.1911.0000.9920.9910.995
심사연도 회차0.0920.9960.1950.9921.0000.9990.890
인정유효 시작일자0.1100.9950.2440.9910.9991.0000.888
인정유효 종료일자0.0800.8730.2530.9950.8900.8881.000

Missing values

2023-12-12T16:36:52.153439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:36:52.413705image/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

인정연도훈련과정명훈련과정아이디코드명코드주 훈련대상특화과정심사유형심사연도 회차인정유효 시작일자인정유효 종료일자
02018AutoCAD 2D도면작성-기초AIG20180000194046기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
12018AutoCAD 2D도면작성-심화AIG20180000197237기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
22018CATIA 3차원설계-기초AIG20180000197727기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
32018CATIA 3차원설계-심화AIG20180000197779기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
42018Creo(Pro/E) 3차원설계-기초AIG20180000197816기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
52018Creo(Pro/E) 3차원설계-심화AIG20180000197850기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
62018Inventor 3차원설계-기초AIG20180000197888기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
72018SolidWorks 3차원설계-기초AIG20180000197986기계요소설계15010201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
82018노이즈 방지대책AIG20180000201409산업용전자기기하드웨어개발19030201사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
92018무역실무(수출입계약)AIG20180000200437수출입관리2040302사업주(위탁)우수과정통합심사2018-3차2018-07-022021-06-30
인정연도훈련과정명훈련과정아이디코드명코드주 훈련대상특화과정심사유형심사연도 회차인정유효 시작일자인정유효 종료일자
3652022CATIA 3D설계 초급AIG20210000361221기계요소설계15010201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3662022Inventor 설계 중급AIG20210000361328기계요소설계15010201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3672022Inventor 설계 중급AIG20210000361328기계요소설계15010201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3682022Inventor 설계 초급AIG20210000361448기계요소설계15010201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3692022원가관리 기초AIG20210000365064구매조달2040101사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3702022원가관리 기초AIG20210000365064구매조달2040101사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3712022하루만에 배우는 현장품질 실무AIG20210000365271QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3722022하루만에 배우는 현장품질 실무AIG20210000365271QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3732022하루만에 배우는 현장품질 실무AIG20210000365271QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31
3742022하루만에 배우는 현장품질 실무AIG20210000365271QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-31

Duplicate rows

Most frequently occurring

인정연도훈련과정명훈련과정아이디코드명코드주 훈련대상특화과정심사유형심사연도 회차인정유효 시작일자인정유효 종료일자# duplicates
132022하루만에 배우는 현장품질 실무AIG20210000365271QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-314
020223정5S와 4M 실무AIG20210000358945QM/QC관리2040201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312
12022EXCEL 실무능력 활용(VBA, 매크로)AIG20210000351064사무행정2020302사업주(위탁)우수과정(통합)심사2021-34차2022-01-012024-12-312
22022Inventor 설계 중급AIG20210000361328기계요소설계15010201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312
32022PLC 제어 실무(MELSEC)AIG20210000351507기계소프트웨어개발15030102사업주(위탁)우수과정(통합)심사2021-34차2022-01-012024-12-312
42022구매/외주관리 실무AIG20210000351571구매조달2040101사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312
52022신입사원 핵심역량 강화AIG20210000343779인사2020201사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312
62022원가관리 기초AIG20210000365064구매조달2040101사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312
72022전기자동차(e-모빌리티) 최신 동향 및 핵심기술AIG20210000353305기계개발기획15010102사업주(위탁)우수과정(통합)심사2021-34차2022-01-012024-12-312
82022조직을 살리는 파워리더십AIG20210000343683경력지도4030101사업주(위탁)우수과정(통합)심사2021-34차2022-01-012022-12-312