Overview

Dataset statistics

Number of variables13
Number of observations2687
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory288.8 KiB
Average record size in memory110.0 B

Variable types

Numeric6
Text4
Categorical3

Dataset

Description기업(사업주)이 근로자의 직업능력개발 향상을 위해 교육 · 훈련 과정(프로그램)을 개발할 때 공단에서 시행하고 있는 직업능력개발 훈련 사업 중 사업주 자체훈련에 적합한 과정을 참고할 수 있도록 지원하는 도구
URLhttps://www.data.go.kr/data/15084045/fileData.do

Alerts

사업구분 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
훈련방법 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 사업구분 and 1 other fieldsHigh correlation
국가직무능력표준(NCS) 코드 is highly overall correlated with 국가직무능력표준(NCS) 코드1 and 1 other fieldsHigh correlation
국가직무능력표준(NCS) 코드1 is highly overall correlated with 국가직무능력표준(NCS) 코드 and 1 other fieldsHigh correlation
국가직무능력표준(NCS) 코드명1 is highly overall correlated with 국가직무능력표준(NCS) 코드 and 1 other fieldsHigh correlation
훈련방법 is highly imbalanced (66.7%)Imbalance
연번 has unique valuesUnique
국가직무능력표준(NCS) 코드3 has 65 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-12 04:11:21.134214
Analysis finished2023-12-12 04:11:28.187723
Duration7.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2687
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1344
Minimum1
Maximum2687
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:28.276283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile135.3
Q1672.5
median1344
Q32015.5
95-th percentile2552.7
Maximum2687
Range2686
Interquartile range (IQR)1343

Descriptive statistics

Standard deviation775.81441
Coefficient of variation (CV)0.57724287
Kurtosis-1.2
Mean1344
Median Absolute Deviation (MAD)672
Skewness0
Sum3611328
Variance601888
MonotonicityStrictly increasing
2023-12-12T13:11:28.431613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1796 1
 
< 0.1%
1788 1
 
< 0.1%
1789 1
 
< 0.1%
1790 1
 
< 0.1%
1791 1
 
< 0.1%
1792 1
 
< 0.1%
1793 1
 
< 0.1%
1794 1
 
< 0.1%
1795 1
 
< 0.1%
Other values (2677) 2677
99.6%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
2687 1
< 0.1%
2686 1
< 0.1%
2685 1
< 0.1%
2684 1
< 0.1%
2683 1
< 0.1%
2682 1
< 0.1%
2681 1
< 0.1%
2680 1
< 0.1%
2679 1
< 0.1%
2678 1
< 0.1%

훈련과정명
Text

Distinct2664
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
2023-12-12T13:11:28.790494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length43
Mean length16.926684
Min length3

Characters and Unicode

Total characters45482
Distinct characters615
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2644 ?
Unique (%)98.4%

Sample

1st row고글 제품 설계 및 Scale-Up
2nd rowCAM
3rd row기계수동조립
4th row소프트웨어 사용탐지를 위한 네트워크 모니터링 기술 실무
5th row전기설비 기본 및 표준설계 스킬향상
ValueCountFrequency (%)
실무 419
 
4.4%
및 324
 
3.4%
과정 268
 
2.8%
위한 237
 
2.5%
활용한 156
 
1.6%
설계 90
 
0.9%
활용 78
 
0.8%
분석 59
 
0.6%
3d 49
 
0.5%
자동차 47
 
0.5%
Other values (3882) 7765
81.8%
2023-12-12T13:11:29.246551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6887
 
15.1%
기 910
 
2.0%
정 878
 
1.9%
실 831
 
1.8%
무 817
 
1.8%
용 726
 
1.6%
과 670
 
1.5%
전 554
 
1.2%
C 513
 
1.1%
리 502
 
1.1%
Other values (605) 32194
70.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 30923
68.0%
Space Separator 6887
 
15.1%
Uppercase Letter 3466
 
7.6%
Lowercase Letter 2473
 
5.4%
Decimal Number 531
 
1.2%
Close Punctuation 472
 
1.0%
Open Punctuation 472
 
1.0%
Other Punctuation 190
 
0.4%
Dash Punctuation 41
 
0.1%
Connector Punctuation 16
 
< 0.1%
Other values (3) 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 910
 
2.9%
정 878
 
2.8%
실 831
 
2.7%
무 817
 
2.6%
용 726
 
2.3%
과 670
 
2.2%
전 554
 
1.8%
리 502
 
1.6%
한 492
 
1.6%
자 490
 
1.6%
Other values (523) 24053
77.8%
Uppercase Letter
ValueCountFrequency (%)
C 513
14.8%
A 353
10.2%
S 320
 
9.2%
D 268
 
7.7%
M 225
 
6.5%
I 219
 
6.3%
T 203
 
5.9%
P 198
 
5.7%
N 175
 
5.0%
L 151
 
4.4%
Other values (16) 841
24.3%
Lowercase Letter
ValueCountFrequency (%)
e 289
11.7%
i 240
 
9.7%
o 217
 
8.8%
r 200
 
8.1%
n 200
 
8.1%
a 192
 
7.8%
t 180
 
7.3%
s 141
 
5.7%
l 126
 
5.1%
g 87
 
3.5%
Other values (16) 601
24.3%
Decimal Number
ValueCountFrequency (%)
2 166
31.3%
3 154
29.0%
0 78
14.7%
1 36
 
6.8%
5 32
 
6.0%
4 26
 
4.9%
6 19
 
3.6%
9 14
 
2.6%
7 5
 
0.9%
8 1
 
0.2%
Other Punctuation
ValueCountFrequency (%)
/ 70
36.8%
, 47
24.7%
& 40
21.1%
· 16
 
8.4%
. 13
 
6.8%
% 1
 
0.5%
: 1
 
0.5%
* 1
 
0.5%
' 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 445
94.3%
] 27
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 445
94.3%
[ 27
 
5.7%
Letter Number
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%
Space Separator
ValueCountFrequency (%)
6887
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 16
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 30921
68.0%
Common 8616
 
18.9%
Latin 5943
 
13.1%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 910
 
2.9%
정 878
 
2.8%
실 831
 
2.7%
무 817
 
2.6%
용 726
 
2.3%
과 670
 
2.2%
전 554
 
1.8%
리 502
 
1.6%
한 492
 
1.6%
자 490
 
1.6%
Other values (522) 24051
77.8%
Latin
ValueCountFrequency (%)
C 513
 
8.6%
A 353
 
5.9%
S 320
 
5.4%
e 289
 
4.9%
D 268
 
4.5%
i 240
 
4.0%
M 225
 
3.8%
I 219
 
3.7%
o 217
 
3.7%
T 203
 
3.4%
Other values (44) 3096
52.1%
Common
ValueCountFrequency (%)
6887
79.9%
) 445
 
5.2%
( 445
 
5.2%
2 166
 
1.9%
3 154
 
1.8%
0 78
 
0.9%
/ 70
 
0.8%
, 47
 
0.5%
- 41
 
0.5%
& 40
 
0.5%
Other values (18) 243
 
2.8%
Han
ValueCountFrequency (%)
新 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 30919
68.0%
ASCII 14538
32.0%
None 16
 
< 0.1%
Number Forms 4
 
< 0.1%
CJK 2
 
< 0.1%
Compat Jamo 2
 
< 0.1%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6887
47.4%
C 513
 
3.5%
) 445
 
3.1%
( 445
 
3.1%
A 353
 
2.4%
S 320
 
2.2%
e 289
 
2.0%
D 268
 
1.8%
i 240
 
1.7%
M 225
 
1.5%
Other values (68) 4553
31.3%
Hangul
ValueCountFrequency (%)
기 910
 
2.9%
정 878
 
2.8%
실 831
 
2.7%
무 817
 
2.6%
용 726
 
2.3%
과 670
 
2.2%
전 554
 
1.8%
리 502
 
1.6%
한 492
 
1.6%
자 490
 
1.6%
Other values (521) 24049
77.8%
None
ValueCountFrequency (%)
· 16
100.0%
Number Forms
ValueCountFrequency (%)
Ⅰ 2
50.0%
Ⅱ 2
50.0%
CJK
ValueCountFrequency (%)
新 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 2
100.0%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

사업구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
국가인적자원개발컨소시엄 사업
1309 
지역산업맞춤형인력양성 사업
1213 
s-ojt
165 

Length

Max length15
Median length14
Mean length13.934499
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rows-ojt
2nd rows-ojt
3rd rows-ojt
4th rows-ojt
5th rows-ojt

Common Values

ValueCountFrequency (%)
국가인적자원개발컨소시엄 사업 1309
48.7%
지역산업맞춤형인력양성 사업 1213
45.1%
s-ojt 165
 
6.1%

Length

2023-12-12T13:11:29.376967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:11:29.467602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업 2522
48.4%
국가인적자원개발컨소시엄 1309
25.1%
지역산업맞춤형인력양성 1213
23.3%
s-ojt 165
 
3.2%

훈련기관명
Text

Distinct306
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
2023-12-12T13:11:29.697043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length8.5634537
Min length2

Characters and Unicode

Total characters23010
Distinct characters301
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151 ?
Unique (%)5.6%

Sample

1st row㈜오리온엔이에스
2nd row은성스퀘어
3rd row㈜케이씨
4th row닥터소프트
5th row숲이엔지
ValueCountFrequency (%)
한국폴리텍대학 386
 
11.7%
대한상공회의소 118
 
3.6%
자체 68
 
2.1%
현대로템주식회사 51
 
1.5%
조광페인트 43
 
1.3%
서울시 41
 
1.2%
한국해양대학교 39
 
1.2%
한국전파진흥협회 37
 
1.1%
대한상의 36
 
1.1%
경남테크노파크 34
 
1.0%
Other values (305) 2438
74.1%
2023-12-12T13:11:30.230404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 1341
 
5.8%
한 1207
 
5.2%
국 1043
 
4.5%
학 1001
 
4.4%
748
 
3.3%
산 596
 
2.6%
교 581
 
2.5%
원 558
 
2.4%
회 512
 
2.2%
스 503
 
2.2%
Other values (291) 14920
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21272
92.4%
Space Separator 748
 
3.3%
Other Symbol 322
 
1.4%
Uppercase Letter 257
 
1.1%
Open Punctuation 195
 
0.8%
Close Punctuation 195
 
0.8%
Decimal Number 20
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
대 1341
 
6.3%
한 1207
 
5.7%
국 1043
 
4.9%
학 1001
 
4.7%
산 596
 
2.8%
교 581
 
2.7%
원 558
 
2.6%
회 512
 
2.4%
스 503
 
2.4%
리 446
 
2.1%
Other values (271) 13484
63.4%
Uppercase Letter
ValueCountFrequency (%)
T 65
25.3%
E 44
17.1%
D 41
16.0%
I 33
12.8%
Y 21
 
8.2%
C 21
 
8.2%
S 14
 
5.4%
X 11
 
4.3%
N 2
 
0.8%
G 1
 
0.4%
Other values (4) 4
 
1.6%
Space Separator
ValueCountFrequency (%)
748
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 322
100.0%
Open Punctuation
ValueCountFrequency (%)
( 195
100.0%
Close Punctuation
ValueCountFrequency (%)
) 195
100.0%
Decimal Number
ValueCountFrequency (%)
3 20
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21594
93.8%
Common 1159
 
5.0%
Latin 257
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
대 1341
 
6.2%
한 1207
 
5.6%
국 1043
 
4.8%
학 1001
 
4.6%
산 596
 
2.8%
교 581
 
2.7%
원 558
 
2.6%
회 512
 
2.4%
스 503
 
2.3%
리 446
 
2.1%
Other values (272) 13806
63.9%
Latin
ValueCountFrequency (%)
T 65
25.3%
E 44
17.1%
D 41
16.0%
I 33
12.8%
Y 21
 
8.2%
C 21
 
8.2%
S 14
 
5.4%
X 11
 
4.3%
N 2
 
0.8%
G 1
 
0.4%
Other values (4) 4
 
1.6%
Common
ValueCountFrequency (%)
748
64.5%
( 195
 
16.8%
) 195
 
16.8%
3 20
 
1.7%
. 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21272
92.4%
ASCII 1416
 
6.2%
None 322
 
1.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 1341
 
6.3%
한 1207
 
5.7%
국 1043
 
4.9%
학 1001
 
4.7%
산 596
 
2.8%
교 581
 
2.7%
원 558
 
2.6%
회 512
 
2.4%
스 503
 
2.4%
리 446
 
2.1%
Other values (271) 13484
63.4%
ASCII
ValueCountFrequency (%)
748
52.8%
( 195
 
13.8%
) 195
 
13.8%
T 65
 
4.6%
E 44
 
3.1%
D 41
 
2.9%
I 33
 
2.3%
Y 21
 
1.5%
C 21
 
1.5%
3 20
 
1.4%
Other values (9) 33
 
2.3%
None
ValueCountFrequency (%)
㈜ 322
100.0%

국가직무능력표준(NCS) 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean154031.29
Minimum10101
Maximum240303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:30.418494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10101
5-th percentile20402
Q1150102
median150807
Q3190108
95-th percentile230101
Maximum240303
Range230202
Interquartile range (IQR)40006

Descriptive statistics

Standard deviation51165.426
Coefficient of variation (CV)0.33217555
Kurtosis1.1779211
Mean154031.29
Median Absolute Deviation (MAD)29296
Skewness-1.1572183
Sum4.1388206 × 108
Variance2.6179009 × 109
MonotonicityNot monotonic
2023-12-12T13:11:30.587737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150102 190
 
7.1%
160105 184
 
6.8%
150301 120
 
4.5%
150201 105
 
3.9%
200102 103
 
3.8%
20402 90
 
3.3%
150603 83
 
3.1%
150302 77
 
2.9%
90301 51
 
1.9%
190107 50
 
1.9%
Other values (157) 1634
60.8%
ValueCountFrequency (%)
10101 9
 
0.3%
10102 1
 
< 0.1%
20101 4
 
0.1%
20102 1
 
< 0.1%
20103 3
 
0.1%
20202 5
 
0.2%
20401 45
1.7%
20402 90
3.3%
20403 26
 
1.0%
40301 2
 
0.1%
ValueCountFrequency (%)
240303 4
 
0.1%
240302 7
 
0.3%
240301 4
 
0.1%
240103 1
 
< 0.1%
230603 10
 
0.4%
230602 4
 
0.1%
230601 24
0.9%
230506 1
 
< 0.1%
230505 39
1.5%
230402 4
 
0.1%

국가직무능력표준(NCS) 코드1
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.375884
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:30.727897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q115
median15
Q319
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1201628
Coefficient of variation (CV)0.33299958
Kurtosis1.1736572
Mean15.375884
Median Absolute Deviation (MAD)3
Skewness-1.1561446
Sum41315
Variance26.216067
MonotonicityNot monotonic
2023-12-12T13:11:30.885821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
15 850
31.6%
19 352
13.1%
20 268
 
10.0%
16 236
 
8.8%
2 174
 
6.5%
23 124
 
4.6%
14 124
 
4.6%
18 94
 
3.5%
8 87
 
3.2%
17 84
 
3.1%
Other values (12) 294
 
10.9%
ValueCountFrequency (%)
1 10
 
0.4%
2 174
6.5%
4 2
 
0.1%
5 18
 
0.7%
6 21
 
0.8%
7 9
 
0.3%
8 87
3.2%
9 74
2.8%
10 27
 
1.0%
12 8
 
0.3%
ValueCountFrequency (%)
24 16
 
0.6%
23 124
 
4.6%
22 51
 
1.9%
21 50
 
1.9%
20 268
 
10.0%
19 352
13.1%
18 94
 
3.5%
17 84
 
3.1%
16 236
 
8.8%
15 850
31.6%

국가직무능력표준(NCS) 코드2
Real number (ℝ)

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6985486
Minimum0
Maximum10
Zeros11
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:31.022165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0740054
Coefficient of variation (CV)0.76856329
Kurtosis2.1238987
Mean2.6985486
Median Absolute Deviation (MAD)1
Skewness1.5078961
Sum7251
Variance4.3014983
MonotonicityNot monotonic
2023-12-12T13:11:31.155240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 1093
40.7%
3 564
21.0%
2 353
 
13.1%
4 268
 
10.0%
6 139
 
5.2%
5 105
 
3.9%
8 74
 
2.8%
10 45
 
1.7%
9 21
 
0.8%
7 14
 
0.5%
ValueCountFrequency (%)
0 11
 
0.4%
1 1093
40.7%
2 353
 
13.1%
3 564
21.0%
4 268
 
10.0%
5 105
 
3.9%
6 139
 
5.2%
7 14
 
0.5%
8 74
 
2.8%
9 21
 
0.8%
ValueCountFrequency (%)
10 45
 
1.7%
9 21
 
0.8%
8 74
 
2.8%
7 14
 
0.5%
6 139
 
5.2%
5 105
 
3.9%
4 268
 
10.0%
3 564
21.0%
2 353
 
13.1%
1 1093
40.7%

국가직무능력표준(NCS) 코드3
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5913658
Minimum0
Maximum11
Zeros65
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:31.301170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1315032
Coefficient of variation (CV)0.82254044
Kurtosis2.8620977
Mean2.5913658
Median Absolute Deviation (MAD)1
Skewness1.7035755
Sum6963
Variance4.5433059
MonotonicityNot monotonic
2023-12-12T13:11:31.468278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 976
36.3%
2 737
27.4%
3 302
 
11.2%
5 251
 
9.3%
4 119
 
4.4%
8 70
 
2.6%
0 65
 
2.4%
7 59
 
2.2%
6 46
 
1.7%
11 33
 
1.2%
Other values (2) 29
 
1.1%
ValueCountFrequency (%)
0 65
 
2.4%
1 976
36.3%
2 737
27.4%
3 302
 
11.2%
4 119
 
4.4%
5 251
 
9.3%
6 46
 
1.7%
7 59
 
2.2%
8 70
 
2.6%
9 26
 
1.0%
ValueCountFrequency (%)
11 33
 
1.2%
10 3
 
0.1%
9 26
 
1.0%
8 70
 
2.6%
7 59
 
2.2%
6 46
 
1.7%
5 251
 
9.3%
4 119
 
4.4%
3 302
11.2%
2 737
27.4%

국가직무능력표준(NCS) 코드명1
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
기계
850 
전기·전자
352 
정보통신
268 
재료
236 
경영·회계·사무
174 
Other values (17)
807 

Length

Max length15
Median length14
Mean length4.2370674
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전기·전자
2nd row기계
3rd row기계
4th row정보통신
5th row전기·전자

Common Values

ValueCountFrequency (%)
기계 850
31.6%
전기·전자 352
13.1%
정보통신 268
 
10.0%
재료 236
 
8.8%
경영·회계·사무 174
 
6.5%
환경·에너지·안전 124
 
4.6%
건설 124
 
4.6%
섬유·의복 94
 
3.5%
문화·예술·디자인·방송 87
 
3.2%
화학 84
 
3.1%
Other values (12) 294
 
10.9%

Length

2023-12-12T13:11:31.690907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기계 850
31.6%
전기·전자 352
13.1%
정보통신 268
 
10.0%
재료 236
 
8.8%
경영·회계·사무 174
 
6.5%
환경·에너지·안전 124
 
4.6%
건설 124
 
4.6%
섬유·의복 94
 
3.5%
문화·예술·디자인·방송 87
 
3.2%
화학 84
 
3.1%
Other values (12) 294
 
10.9%

국가직무능력표준(NCS) 코드명2
Text

Distinct156
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
2023-12-12T13:11:32.054112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length5.1269073
Min length2

Characters and Unicode

Total characters13776
Distinct characters188
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)0.8%

Sample

1st row디스플레이개발
2nd row절삭가공
3rd row기계생산관리
4th row정보기술개발
5th row전기설비설계·감리
ValueCountFrequency (%)
기계설계 190
 
7.1%
용접 184
 
6.8%
기계조립 120
 
4.5%
절삭가공 105
 
3.9%
정보기술개발 103
 
3.8%
품질관리 90
 
3.3%
자동차정비 83
 
3.1%
기계생산관리 77
 
2.9%
미분류 65
 
2.4%
선박운항 51
 
1.9%
Other values (147) 1621
60.3%
2023-12-12T13:11:32.653012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 997
 
7.2%
계 823
 
6.0%
리 552
 
4.0%
설 470
 
3.4%
관 442
 
3.2%
공 374
 
2.7%
비 350
 
2.5%
정 339
 
2.5%
전 324
 
2.4%
조 318
 
2.3%
Other values (178) 8787
63.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13407
97.3%
Other Punctuation 271
 
2.0%
Uppercase Letter 33
 
0.2%
Decimal Number 33
 
0.2%
Close Punctuation 10
 
0.1%
Open Punctuation 10
 
0.1%
Dash Punctuation 5
 
< 0.1%
Lowercase Letter 5
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 997
 
7.4%
계 823
 
6.1%
리 552
 
4.1%
설 470
 
3.5%
관 442
 
3.3%
공 374
 
2.8%
비 350
 
2.6%
정 339
 
2.5%
전 324
 
2.4%
조 318
 
2.4%
Other values (170) 8418
62.8%
Other Punctuation
ValueCountFrequency (%)
· 271
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 33
100.0%
Decimal Number
ValueCountFrequency (%)
3 33
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 5
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13407
97.3%
Common 331
 
2.4%
Latin 38
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 997
 
7.4%
계 823
 
6.1%
리 552
 
4.1%
설 470
 
3.5%
관 442
 
3.3%
공 374
 
2.8%
비 350
 
2.6%
정 339
 
2.5%
전 324
 
2.4%
조 318
 
2.4%
Other values (170) 8418
62.8%
Common
ValueCountFrequency (%)
· 271
81.9%
3 33
 
10.0%
) 10
 
3.0%
( 10
 
3.0%
- 5
 
1.5%
2
 
0.6%
Latin
ValueCountFrequency (%)
D 33
86.8%
e 5
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13407
97.3%
None 271
 
2.0%
ASCII 98
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 997
 
7.4%
계 823
 
6.1%
리 552
 
4.1%
설 470
 
3.5%
관 442
 
3.3%
공 374
 
2.8%
비 350
 
2.6%
정 339
 
2.5%
전 324
 
2.4%
조 318
 
2.4%
Other values (170) 8418
62.8%
None
ValueCountFrequency (%)
· 271
100.0%
ASCII
ValueCountFrequency (%)
D 33
33.7%
3 33
33.7%
) 10
 
10.2%
( 10
 
10.2%
- 5
 
5.1%
e 5
 
5.1%
2
 
2.0%

국가직무능력표준(NCS) 코드명3
Text

Distinct65
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
2023-12-12T13:11:32.953429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4979531
Min length2

Characters and Unicode

Total characters12086
Distinct characters118
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.2%

Sample

1st row전자기기개발
2nd row기계가공
3rd row기계조립·관리
4th row정보기술
5th row전기
ValueCountFrequency (%)
정보기술 224
 
8.3%
금속재료 217
 
8.1%
기계설계 199
 
7.4%
기계조립·관리 197
 
7.3%
전기 192
 
7.1%
생산ㆍ품질관리 161
 
6.0%
전자기기개발 143
 
5.3%
기계가공 112
 
4.2%
자동차 101
 
3.8%
건축 60
 
2.2%
Other values (56) 1083
40.3%
2023-12-12T13:11:33.462789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 1439
 
11.9%
계 827
 
6.8%
전 478
 
4.0%
리 452
 
3.7%
관 450
 
3.7%
조 372
 
3.1%
· 372
 
3.1%
자 366
 
3.0%
품 315
 
2.6%
정 305
 
2.5%
Other values (108) 6710
55.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11712
96.9%
Other Punctuation 372
 
3.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 1439
 
12.3%
계 827
 
7.1%
전 478
 
4.1%
리 452
 
3.9%
관 450
 
3.8%
조 372
 
3.2%
자 366
 
3.1%
품 315
 
2.7%
정 305
 
2.6%
설 276
 
2.4%
Other values (106) 6432
54.9%
Other Punctuation
ValueCountFrequency (%)
· 372
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11712
96.9%
Common 374
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 1439
 
12.3%
계 827
 
7.1%
전 478
 
4.1%
리 452
 
3.9%
관 450
 
3.8%
조 372
 
3.2%
자 366
 
3.1%
품 315
 
2.7%
정 305
 
2.6%
설 276
 
2.4%
Other values (106) 6432
54.9%
Common
ValueCountFrequency (%)
· 372
99.5%
2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11516
95.3%
None 372
 
3.1%
Compat Jamo 196
 
1.6%
ASCII 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 1439
 
12.5%
계 827
 
7.2%
전 478
 
4.2%
리 452
 
3.9%
관 450
 
3.9%
조 372
 
3.2%
자 366
 
3.2%
품 315
 
2.7%
정 305
 
2.6%
설 276
 
2.4%
Other values (105) 6236
54.2%
None
ValueCountFrequency (%)
· 372
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 196
100.0%
ASCII
ValueCountFrequency (%)
2
100.0%

훈련방법
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.1 KiB
집체훈련
2522 
OJT
 
165

Length

Max length4
Median length4
Mean length3.9385932
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
집체훈련 2522
93.9%
OJT 165
 
6.1%

Length

2023-12-12T13:11:33.670182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:11:33.818531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
집체훈련 2522
93.9%
ojt 165
 
6.1%

훈련시간
Real number (ℝ)

Distinct123
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.707853
Minimum1
Maximum1400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.7 KiB
2023-12-12T13:11:33.965007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q114
median16
Q324
95-th percentile300
Maximum1400
Range1399
Interquartile range (IQR)10

Descriptive statistics

Standard deviation176.97903
Coefficient of variation (CV)2.9640829
Kurtosis28.854508
Mean59.707853
Median Absolute Deviation (MAD)6
Skewness5.2030416
Sum160435
Variance31321.576
MonotonicityNot monotonic
2023-12-12T13:11:34.169130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 682
25.4%
8 519
19.3%
20 160
 
6.0%
40 157
 
5.8%
15 149
 
5.5%
14 140
 
5.2%
24 139
 
5.2%
30 80
 
3.0%
12 74
 
2.8%
32 58
 
2.2%
Other values (113) 529
19.7%
ValueCountFrequency (%)
1 2
 
0.1%
4 4
 
0.1%
5 1
 
< 0.1%
8 519
19.3%
9 12
 
0.4%
10 16
 
0.6%
11 9
 
0.3%
12 74
 
2.8%
13 2
 
0.1%
14 140
 
5.2%
ValueCountFrequency (%)
1400 10
0.4%
1240 1
 
< 0.1%
1200 13
0.5%
1191 2
 
0.1%
1190 2
 
0.1%
1180 1
 
< 0.1%
1044 3
 
0.1%
1040 1
 
< 0.1%
1020 1
 
< 0.1%
1000 5
 
0.2%

Interactions

2023-12-12T13:11:27.211318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:22.716765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:23.631740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:24.443207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:25.262900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T13:11:22.855344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:23.761703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T13:11:23.893418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:24.723850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-12T13:11:26.352853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:11:27.095073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:11:34.326726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업구분국가직무능력표준(NCS) 코드국가직무능력표준(NCS) 코드1국가직무능력표준(NCS) 코드2국가직무능력표준(NCS) 코드3국가직무능력표준(NCS) 코드명1국가직무능력표준(NCS) 코드명3훈련방법훈련시간
연번1.0000.9010.4220.4200.3140.3560.4580.6530.9220.161
사업구분0.9011.0000.2480.2470.2410.1720.3900.5901.0000.156
국가직무능력표준(NCS) 코드0.4220.2481.0001.0000.7670.6771.0001.0000.1010.125
국가직무능력표준(NCS) 코드10.4200.2471.0001.0000.7610.6761.0001.0000.1030.125
국가직무능력표준(NCS) 코드20.3140.2410.7670.7611.0000.6390.7611.0000.1440.089
국가직무능력표준(NCS) 코드30.3560.1720.6770.6760.6391.0000.7510.8800.0870.000
국가직무능력표준(NCS) 코드명10.4580.3901.0001.0000.7610.7511.0001.0000.2030.086
국가직무능력표준(NCS) 코드명30.6530.5901.0001.0001.0000.8801.0001.0000.3220.000
훈련방법0.9221.0000.1010.1030.1440.0870.2030.3221.0000.055
훈련시간0.1610.1560.1250.1250.0890.0000.0860.0000.0551.000
2023-12-12T13:11:34.896631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업구분국가직무능력표준(NCS) 코드명1훈련방법
사업구분1.0000.2211.000
국가직무능력표준(NCS) 코드명10.2211.0000.160
훈련방법1.0000.1601.000
2023-12-12T13:11:35.018396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번국가직무능력표준(NCS) 코드국가직무능력표준(NCS) 코드1국가직무능력표준(NCS) 코드2국가직무능력표준(NCS) 코드3훈련시간사업구분국가직무능력표준(NCS) 코드명1훈련방법
연번1.000-0.113-0.089-0.0550.0400.0530.8520.1870.765
국가직무능력표준(NCS) 코드-0.1131.0000.982-0.2360.2450.0620.1520.9980.079
국가직무능력표준(NCS) 코드1-0.0890.9821.000-0.3640.2180.0670.1520.9980.079
국가직무능력표준(NCS) 코드2-0.055-0.236-0.3641.000-0.095-0.0730.1430.4030.105
국가직무능력표준(NCS) 코드30.0400.2450.218-0.0951.0000.0140.1030.3940.067
훈련시간0.0530.0620.067-0.0730.0141.0000.0930.0320.042
사업구분0.8520.1520.1520.1430.1030.0931.0000.2211.000
국가직무능력표준(NCS) 코드명10.1870.9980.9980.4030.3940.0320.2211.0000.160
훈련방법0.7650.0790.0790.1050.0670.0421.0000.1601.000

Missing values

2023-12-12T13:11:27.896642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:11:28.098915image/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

연번훈련과정명사업구분훈련기관명국가직무능력표준(NCS) 코드국가직무능력표준(NCS) 코드1국가직무능력표준(NCS) 코드2국가직무능력표준(NCS) 코드3국가직무능력표준(NCS) 코드명1국가직무능력표준(NCS) 코드명2국가직무능력표준(NCS) 코드명3훈련방법훈련시간
01고글 제품 설계 및 Scale-Ups-ojt㈜오리온엔이에스1903071937전기·전자디스플레이개발전자기기개발OJT20
12CAMs-ojt은성스퀘어1502011521기계절삭가공기계가공OJT20
23기계수동조립s-ojt㈜케이씨1503021532기계기계생산관리기계조립·관리OJT20
34소프트웨어 사용탐지를 위한 네트워크 모니터링 기술 실무s-ojt닥터소프트2001022012정보통신정보기술개발정보기술OJT40
45전기설비 기본 및 표준설계 스킬향상s-ojt숲이엔지1901061916전기·전자전기설비설계·감리전기OJT4
56공정검사와 측정실무s-ojt㈜에이스테크원1502011521기계절삭가공기계가공OJT30
67DC모터 생산 및 시험s-ojt상신에스앤피㈜1503011531기계기계조립기계조립·관리OJT40
78FA적용 AMS 센서 S/W프로그램 개발 및 성능구현s-ojt㈜에이씨엑스1903001930전기·전자미분류전자기기개발OJT8
895G용 안테나의 감도향상 및 측정s-ojt㈜아트시그널1903051935전기·전자전자부품개발전자기기개발OJT8
910딥러닝 Edge컴퓨팅을 위한 보드의 SW 성능구현s-ojt㈜오스코2001012011정보통신정보기술전략·계획정보기술OJT8
연번훈련과정명사업구분훈련기관명국가직무능력표준(NCS) 코드국가직무능력표준(NCS) 코드1국가직무능력표준(NCS) 코드2국가직무능력표준(NCS) 코드3국가직무능력표준(NCS) 코드명1국가직무능력표준(NCS) 코드명2국가직무능력표준(NCS) 코드명3훈련방법훈련시간
26772678품질보증 체계확립(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원20402242경영·회계·사무품질관리생산ㆍ품질관리집체훈련21
26782679Solidworks 시뮬레이션 구조/모션/유동해석 설계검증(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원1501021512기계기계설계기계설계집체훈련21
26792680AutoCAD 활용 도면분석(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원1501021512기계기계설계기계설계집체훈련21
26802681EMI/EMC 저감을 위한 회로 설계 대책(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원1903011931전기·전자가전기기 개발전자기기개발집체훈련21
26812682PADS TOOL을 활용한 PCB Artwork설계(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원1903031933전기·전자정보통신기기개발전자기기개발집체훈련21
26822683이더넷과 WiFi를 이용한 IoT 디바이스 설계 중급(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원2001022012정보통신정보기술개발정보기술집체훈련35
26832684Cortex-M3기반 펌웨어 및 IoT 구현을 위한 디바이스제어(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원2001022012정보통신정보기술개발정보기술집체훈련35
26842685IoT 제품 개발자를 위한 디지털 전자회로 입문(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원2001022012정보통신정보기술개발정보기술집체훈련35
26852686가구용 접착제 및 화학도료 활용기법(향상)지역산업맞춤형인력양성 사업경기도경제과학진흥원2202012221인쇄·목재·가구·공예공예공예집체훈련12
26862687건설기계 자동차 정비지역산업맞춤형인력양성 사업대한상공회의소 경기인력개발원1407061476건설건설기계정비건설기계운전·정비집체훈련1180