Overview

Dataset statistics

Number of variables22
Number of observations4577
Missing cells18170
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory840.4 KiB
Average record size in memory188.0 B

Variable types

Text6
Numeric10
Categorical3
Boolean2
Unsupported1

Dataset

Description산업안전보건 직무교육 코스 현황 및 코스 소개 자료
Author한국산업안전보건공단
URLhttps://www.data.go.kr/data/15065605/fileData.do

Alerts

COURSE_TYPE_CD is highly imbalanced (59.6%)Imbalance
USE_YN is highly imbalanced (72.6%)Imbalance
MOBILE_YN is highly imbalanced (82.1%)Imbalance
COURSE_YEAR has 86 (1.9%) missing valuesMissing
STUDY_OBJECTIVE has 1306 (28.5%) missing valuesMissing
STUDY_CONTENTS has 1461 (31.9%) missing valuesMissing
STUDY_TARGET has 2581 (56.4%) missing valuesMissing
COURSE_EDUFEE has 139 (3.0%) missing valuesMissing
RE_STUDY_PERIOD has 1075 (23.5%) missing valuesMissing
EDU_PLACE_CD has 4577 (100.0%) missing valuesMissing
ORG_CD has 659 (14.4%) missing valuesMissing
LAW_ALLOW_TIME has 905 (19.8%) missing valuesMissing
MOBILE_YN has 905 (19.8%) missing valuesMissing
REVIEW_PERIOD has 4443 (97.1%) missing valuesMissing
CAPACITY is highly skewed (γ1 = 33.78542104)Skewed
TOTAL_EDU_TIME is highly skewed (γ1 = 62.18336409)Skewed
COURSE_EDUFEE is highly skewed (γ1 = 37.27044649)Skewed
COURSE_CD has unique valuesUnique
EDU_PLACE_CD is an unsupported type, check if it needs cleaning or further analysisUnsupported
TOTAL_EDU_TIME has 146 (3.2%) zerosZeros
COURSE_EDUFEE has 976 (21.3%) zerosZeros
RE_STUDY_PERIOD has 201 (4.4%) zerosZeros
EXAM_APPR_COUNT has 69 (1.5%) zerosZeros

Reproduction

Analysis started2023-12-12 04:09:17.662798
Analysis finished2023-12-12 04:09:19.648613
Duration1.99 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

COURSE_CD
Text

UNIQUE 

Distinct4577
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
2023-12-12T13:09:19.819975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length15.537688
Min length14

Characters and Unicode

Total characters71116
Distinct characters36
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

Unique4577 ?
Unique (%)100.0%

Sample

1st rowCO170404150476
2nd rowCO170405160481
3rd rowCO161005110388
4th rowCO161005110389
5th rowCO161222150415
ValueCountFrequency (%)
co170404150476 1
 
< 0.1%
16f646f170ezviudldim 1
 
< 0.1%
16f639dd4d7bbcsojcfa 1
 
< 0.1%
169dd50a384sdvdvlyxy 1
 
< 0.1%
16f591fbc5fjmegfdarv 1
 
< 0.1%
16b0ce69259laecrjvrv 1
 
< 0.1%
16f7858bdecbkheshyha 1
 
< 0.1%
16c99707e5cphhwoapzc 1
 
< 0.1%
16a9f8f53b9sdhgsywev 1
 
< 0.1%
16f7882b286boeqmwrtf 1
 
< 0.1%
Other values (4567) 4567
99.8%
2023-12-12T13:09:20.238701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12933
18.2%
0 9434
13.3%
2 4939
 
6.9%
C 4349
 
6.1%
6 4018
 
5.6%
O 3841
 
5.4%
8 3558
 
5.0%
7 3549
 
5.0%
5 2991
 
4.2%
3 2969
 
4.2%
Other values (26) 18535
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49860
70.1%
Uppercase Letter 21256
29.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 4349
20.5%
O 3841
18.1%
F 1347
 
6.3%
A 1125
 
5.3%
E 958
 
4.5%
D 954
 
4.5%
B 945
 
4.4%
N 440
 
2.1%
L 438
 
2.1%
S 437
 
2.1%
Other values (16) 6422
30.2%
Decimal Number
ValueCountFrequency (%)
1 12933
25.9%
0 9434
18.9%
2 4939
 
9.9%
6 4018
 
8.1%
8 3558
 
7.1%
7 3549
 
7.1%
5 2991
 
6.0%
3 2969
 
6.0%
4 2851
 
5.7%
9 2618
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 49860
70.1%
Latin 21256
29.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 4349
20.5%
O 3841
18.1%
F 1347
 
6.3%
A 1125
 
5.3%
E 958
 
4.5%
D 954
 
4.5%
B 945
 
4.4%
N 440
 
2.1%
L 438
 
2.1%
S 437
 
2.1%
Other values (16) 6422
30.2%
Common
ValueCountFrequency (%)
1 12933
25.9%
0 9434
18.9%
2 4939
 
9.9%
6 4018
 
8.1%
8 3558
 
7.1%
7 3549
 
7.1%
5 2991
 
6.0%
3 2969
 
6.0%
4 2851
 
5.7%
9 2618
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71116
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12933
18.2%
0 9434
13.3%
2 4939
 
6.9%
C 4349
 
6.1%
6 4018
 
5.6%
O 3841
 
5.4%
8 3558
 
5.0%
7 3549
 
5.0%
5 2991
 
4.2%
3 2969
 
4.2%
Other values (26) 18535
26.1%

COURSE_YEAR
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)0.3%
Missing86
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean2016.9083
Minimum2009
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:20.389476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2012
Q12016
median2017
Q32018
95-th percentile2020
Maximum2020
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1588479
Coefficient of variation (CV)0.0010703749
Kurtosis0.53589596
Mean2016.9083
Median Absolute Deviation (MAD)1
Skewness-0.83300056
Sum9057935
Variance4.6606244
MonotonicityNot monotonic
2023-12-12T13:09:20.524248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2018 1121
24.5%
2016 866
18.9%
2017 686
15.0%
2019 482
10.5%
2020 476
10.4%
2015 289
 
6.3%
2014 193
 
4.2%
2012 127
 
2.8%
2013 127
 
2.8%
2011 112
 
2.4%
Other values (2) 12
 
0.3%
(Missing) 86
 
1.9%
ValueCountFrequency (%)
2009 3
 
0.1%
2010 9
 
0.2%
2011 112
 
2.4%
2012 127
 
2.8%
2013 127
 
2.8%
2014 193
 
4.2%
2015 289
 
6.3%
2016 866
18.9%
2017 686
15.0%
2018 1121
24.5%
ValueCountFrequency (%)
2020 476
10.4%
2019 482
10.5%
2018 1121
24.5%
2017 686
15.0%
2016 866
18.9%
2015 289
 
6.3%
2014 193
 
4.2%
2013 127
 
2.8%
2012 127
 
2.8%
2011 112
 
2.4%

COURSE_NM
Text

Distinct3296
Distinct (%)72.0%
Missing2
Missing (%)< 0.1%
Memory size35.9 KiB
2023-12-12T13:09:20.860066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length91
Median length55
Mean length20.67541
Min length1

Characters and Unicode

Total characters94590
Distinct characters563
Distinct categories16 ?
Distinct scripts4 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2818 ?
Unique (%)61.6%

Sample

1st row안전보건관리책임자 보수교육(건설업)
2nd row안전관리자(보수)
3rd row안전보건관리책임자 신규(건설업)
4th row안전보건관리책임자 보수(건설업)
5th row직무스트레스와 감정노동(보건관리자보수면제)
ValueCountFrequency (%)
관리감독자 906
 
5.7%
교육 499
 
3.2%
안전관리자 256
 
1.6%
및 254
 
1.6%
2018년 238
 
1.5%
237
 
1.5%
안전보건교육 192
 
1.2%
관리감독자가 177
 
1.1%
알아야 172
 
1.1%
할 172
 
1.1%
Other values (2644) 12685
80.3%
2023-12-12T13:09:21.383626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11347
 
12.0%
관 2818
 
3.0%
교 2732
 
2.9%
육 2711
 
2.9%
자 2662
 
2.8%
리 2634
 
2.8%
전 2633
 
2.8%
0 2416
 
2.6%
1 2184
 
2.3%
( 2173
 
2.3%
Other values (553) 60280
63.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 63885
67.5%
Space Separator 11347
 
12.0%
Decimal Number 9162
 
9.7%
Open Punctuation 2351
 
2.5%
Close Punctuation 2349
 
2.5%
Uppercase Letter 1898
 
2.0%
Lowercase Letter 1660
 
1.8%
Other Punctuation 725
 
0.8%
Connector Punctuation 492
 
0.5%
Dash Punctuation 302
 
0.3%
Other values (6) 419
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 2818
 
4.4%
교 2732
 
4.3%
육 2711
 
4.2%
자 2662
 
4.2%
리 2634
 
4.1%
전 2633
 
4.1%
안 2016
 
3.2%
건 1997
 
3.1%
보 1814
 
2.8%
업 1646
 
2.6%
Other values (452) 40222
63.0%
Lowercase Letter
ValueCountFrequency (%)
e 242
14.6%
t 241
14.5%
a 153
9.2%
s 143
8.6%
n 129
 
7.8%
r 108
 
6.5%
i 92
 
5.5%
d 78
 
4.7%
f 70
 
4.2%
g 56
 
3.4%
Other values (16) 348
21.0%
Uppercase Letter
ValueCountFrequency (%)
S 254
13.4%
H 196
10.3%
L 182
9.6%
I 174
 
9.2%
C 137
 
7.2%
K 125
 
6.6%
M 111
 
5.8%
B 105
 
5.5%
G 95
 
5.0%
P 84
 
4.4%
Other values (15) 435
22.9%
Other Punctuation
ValueCountFrequency (%)
. 223
30.8%
: 209
28.8%
, 95
13.1%
' 76
 
10.5%
/ 40
 
5.5%
& 28
 
3.9%
· 25
 
3.4%
" 24
 
3.3%
; 2
 
0.3%
! 2
 
0.3%
Decimal Number
ValueCountFrequency (%)
0 2416
26.4%
1 2184
23.8%
2 2107
23.0%
8 619
 
6.8%
6 454
 
5.0%
3 328
 
3.6%
7 305
 
3.3%
5 268
 
2.9%
9 257
 
2.8%
4 224
 
2.4%
Other Number
ValueCountFrequency (%)
① 9
16.1%
② 8
14.3%
⑤ 7
12.5%
⑥ 7
12.5%
⑦ 7
12.5%
⑨ 5
8.9%
⑧ 5
8.9%
⑩ 4
7.1%
④ 2
 
3.6%
③ 2
 
3.6%
Letter Number
ValueCountFrequency (%)
Ⅰ 78
42.2%
Ⅱ 70
37.8%
Ⅲ 27
 
14.6%
Ⅴ 5
 
2.7%
Ⅳ 5
 
2.7%
Open Punctuation
ValueCountFrequency (%)
( 2173
92.4%
[ 170
 
7.2%
「 8
 
0.3%
Close Punctuation
ValueCountFrequency (%)
) 2171
92.4%
] 170
 
7.2%
」 8
 
0.3%
Math Symbol
ValueCountFrequency (%)
~ 69
53.9%
+ 59
46.1%
Space Separator
ValueCountFrequency (%)
11347
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 492
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 302
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 37
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 11
100.0%
Control
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 63920
67.6%
Common 26925
28.5%
Latin 3743
 
4.0%
Han 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 2818
 
4.4%
교 2732
 
4.3%
육 2711
 
4.2%
자 2662
 
4.2%
리 2634
 
4.1%
전 2633
 
4.1%
안 2016
 
3.2%
건 1997
 
3.1%
보 1814
 
2.8%
업 1646
 
2.6%
Other values (452) 40257
63.0%
Latin
ValueCountFrequency (%)
S 254
 
6.8%
e 242
 
6.5%
t 241
 
6.4%
H 196
 
5.2%
L 182
 
4.9%
I 174
 
4.6%
a 153
 
4.1%
s 143
 
3.8%
C 137
 
3.7%
n 129
 
3.4%
Other values (46) 1892
50.5%
Common
ValueCountFrequency (%)
11347
42.1%
0 2416
 
9.0%
1 2184
 
8.1%
( 2173
 
8.1%
) 2171
 
8.1%
2 2107
 
7.8%
8 619
 
2.3%
_ 492
 
1.8%
6 454
 
1.7%
3 328
 
1.2%
Other values (34) 2634
 
9.8%
Han
ValueCountFrequency (%)
年 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 63749
67.4%
ASCII 30386
32.1%
Number Forms 185
 
0.2%
Compat Jamo 134
 
0.1%
None 78
 
0.1%
Enclosed Alphanum 56
 
0.1%
CJK 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11347
37.3%
0 2416
 
8.0%
1 2184
 
7.2%
( 2173
 
7.2%
) 2171
 
7.1%
2 2107
 
6.9%
8 619
 
2.0%
_ 492
 
1.6%
6 454
 
1.5%
3 328
 
1.1%
Other values (72) 6095
20.1%
Hangul
ValueCountFrequency (%)
관 2818
 
4.4%
교 2732
 
4.3%
육 2711
 
4.3%
자 2662
 
4.2%
리 2634
 
4.1%
전 2633
 
4.1%
안 2016
 
3.2%
건 1997
 
3.1%
보 1814
 
2.8%
업 1646
 
2.6%
Other values (435) 40086
62.9%
Number Forms
ValueCountFrequency (%)
Ⅰ 78
42.2%
Ⅱ 70
37.8%
Ⅲ 27
 
14.6%
Ⅴ 5
 
2.7%
Ⅳ 5
 
2.7%
Compat Jamo
ValueCountFrequency (%)
ㅇ 42
31.3%
ㄹ 22
16.4%
ㄴ 14
 
10.4%
ㅎ 9
 
6.7%
ㄷ 8
 
6.0%
ㅁ 7
 
5.2%
ㅅ 6
 
4.5%
ㅈ 5
 
3.7%
ㅏ 4
 
3.0%
ㆍ 4
 
3.0%
Other values (6) 13
 
9.7%
None
ValueCountFrequency (%)
㈜ 37
47.4%
· 25
32.1%
」 8
 
10.3%
「 8
 
10.3%
Enclosed Alphanum
ValueCountFrequency (%)
① 9
16.1%
② 8
14.3%
⑤ 7
12.5%
⑥ 7
12.5%
⑦ 7
12.5%
⑨ 5
8.9%
⑧ 5
8.9%
⑩ 4
7.1%
④ 2
 
3.6%
③ 2
 
3.6%
CJK
ValueCountFrequency (%)
年 2
100.0%

COURSE_DIV_CD
Real number (ℝ)

Distinct28
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.35241
Minimum10
Maximum290
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:21.538122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q170
median150
Q3150
95-th percentile210
Maximum290
Range280
Interquartile range (IQR)80

Descriptive statistics

Standard deviation63.531228
Coefficient of variation (CV)0.52352669
Kurtosis-0.36400763
Mean121.35241
Median Absolute Deviation (MAD)20
Skewness-0.21315563
Sum555430
Variance4036.2169
MonotonicityNot monotonic
2023-12-12T13:09:21.679027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
150 2123
46.4%
10 350
 
7.6%
20 296
 
6.5%
100 194
 
4.2%
90 192
 
4.2%
190 165
 
3.6%
60 162
 
3.5%
30 133
 
2.9%
70 126
 
2.8%
210 109
 
2.4%
Other values (18) 727
 
15.9%
ValueCountFrequency (%)
10 350
7.6%
20 296
6.5%
30 133
 
2.9%
40 104
 
2.3%
50 5
 
0.1%
60 162
3.5%
70 126
 
2.8%
80 92
 
2.0%
90 192
4.2%
100 194
4.2%
ValueCountFrequency (%)
290 26
 
0.6%
280 20
 
0.4%
270 17
 
0.4%
260 16
 
0.3%
250 86
1.9%
240 32
 
0.7%
230 3
 
0.1%
220 26
 
0.6%
210 109
2.4%
200 6
 
0.1%

COURSE_TYPE_CD
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
KR1401COURSE00000002
3665 
KR1401COURSE00000001
688 
KR1401COURSE00000010
 
110
KR1401COURSE00000012
 
108
KR1401COURSE00000004
 
6

Length

Max length20
Median length20
Mean length20
Min length20

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKR1401COURSE00000001
2nd rowKR1401COURSE00000001
3rd rowKR1401COURSE00000001
4th rowKR1401COURSE00000001
5th rowKR1401COURSE00000001

Common Values

ValueCountFrequency (%)
KR1401COURSE00000002 3665
80.1%
KR1401COURSE00000001 688
 
15.0%
KR1401COURSE00000010 110
 
2.4%
KR1401COURSE00000012 108
 
2.4%
KR1401COURSE00000004 6
 
0.1%

Length

2023-12-12T13:09:21.843438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:21.956463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kr1401course00000002 3665
80.1%
kr1401course00000001 688
 
15.0%
kr1401course00000010 110
 
2.4%
kr1401course00000012 108
 
2.4%
kr1401course00000004 6
 
0.1%

CAPACITY
Real number (ℝ)

SKEWED 

Distinct70
Distinct (%)1.5%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean90384.231
Minimum0
Maximum99999999
Zeros12
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:22.114903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1100
median1000
Q31000
95-th percentile10000
Maximum99999999
Range99999999
Interquartile range (IQR)900

Descriptive statistics

Standard deviation2955847.1
Coefficient of variation (CV)32.703129
Kurtosis1139.9679
Mean90384.231
Median Absolute Deviation (MAD)0
Skewness33.785421
Sum4.1350786 × 108
Variance8.7370322 × 1012
MonotonicityNot monotonic
2023-12-12T13:09:22.304378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 2576
56.3%
50 438
 
9.6%
100 321
 
7.0%
10000 258
 
5.6%
500 142
 
3.1%
60 126
 
2.8%
40 101
 
2.2%
30 94
 
2.1%
10 60
 
1.3%
30000 59
 
1.3%
Other values (60) 400
 
8.7%
ValueCountFrequency (%)
0 12
 
0.3%
1 32
0.7%
2 4
 
0.1%
3 3
 
0.1%
5 10
 
0.2%
6 1
 
< 0.1%
10 60
1.3%
13 1
 
< 0.1%
15 2
 
< 0.1%
20 18
 
0.4%
ValueCountFrequency (%)
99999999 4
 
0.1%
100000 34
 
0.7%
50000 55
 
1.2%
30000 59
 
1.3%
20000 2
 
< 0.1%
10000 258
5.6%
9999 1
 
< 0.1%
5000 2
 
< 0.1%
4000 2
 
< 0.1%
3000 52
 
1.1%

TOTAL_EDU_TIME
Real number (ℝ)

SKEWED  ZEROS 

Distinct29
Distinct (%)0.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.734747
Minimum0
Maximum2020
Zeros146
Zeros (%)3.2%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:22.447843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median16
Q316
95-th percentile24
Maximum2020
Range2020
Interquartile range (IQR)8

Descriptive statistics

Standard deviation30.532075
Coefficient of variation (CV)2.3975407
Kurtosis4088.274
Mean12.734747
Median Absolute Deviation (MAD)8
Skewness62.183364
Sum58236
Variance932.20762
MonotonicityNot monotonic
2023-12-12T13:09:22.589326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
16.0 1932
42.2%
8.0 1148
25.1%
6.0 321
 
7.0%
24.0 266
 
5.8%
0.0 146
 
3.2%
4.0 138
 
3.0%
34.0 130
 
2.8%
2.0 113
 
2.5%
10.0 110
 
2.4%
0.5 72
 
1.6%
Other values (19) 197
 
4.3%
ValueCountFrequency (%)
0.0 146
 
3.2%
0.5 72
 
1.6%
1.0 69
 
1.5%
2.0 113
 
2.5%
3.0 32
 
0.7%
4.0 138
 
3.0%
5.0 25
 
0.5%
6.0 321
 
7.0%
7.0 1
 
< 0.1%
8.0 1148
25.1%
ValueCountFrequency (%)
2020.0 1
 
< 0.1%
60.0 1
 
< 0.1%
40.0 1
 
< 0.1%
35.0 1
 
< 0.1%
34.0 130
2.8%
32.0 2
 
< 0.1%
30.0 5
 
0.1%
24.0 266
5.8%
22.0 5
 
0.1%
20.0 5
 
0.1%

USE_YN
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
True
4361 
False
 
216
ValueCountFrequency (%)
True 4361
95.3%
False 216
 
4.7%
2023-12-12T13:09:22.722020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

STUDY_OBJECTIVE
Text

MISSING 

Distinct1239
Distinct (%)37.9%
Missing1306
Missing (%)28.5%
Memory size35.9 KiB
2023-12-12T13:09:23.158241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length588
Median length298
Mean length56.210945
Min length1

Characters and Unicode

Total characters183866
Distinct characters584
Distinct categories16 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique817 ?
Unique (%)25.0%

Sample

1st row안전보건괸리책임자의 직무와 역할을 이해하고 안전보건에 관한 전문적인 지식을 습득하여 안전보건경영 업무를 효과적으로 운영 할 수 있도록 하는 과정입니다.
2nd row현장 안전관리보건책임자의 안전보건관리능력을 함양하고 재해예방을 위한 안전의식 제고를 위한 교육
3rd row현장안전보건관리책임자의 직무능력의 함양과 안전보건관련법의 동향을 파악하여 현장의 재해예방에 기여
4th row보건관리에 필요한 산업보건 전문지식 및 전문기술을 습득하여 보건관리 업무역량을 강화하고자 함.
5th row보건관리자가 사업장내 전반적인 보건에 관한 전문적인 지식을 습득하여 직무와 역 할을 이해하여 자율안전관리 업무를 가능토록하는 과정입니다. - 신규로 선임된 보건 관리자의 사업장 내 보건에 관한 전문 지식 습득 - 자율 안전 관리 업무를 가능하도록 함
ValueCountFrequency (%)
및 1330
 
3.3%
수 1217
 
3.0%
937
 
2.3%
있다 786
 
1.9%
함 582
 
1.4%
대한 504
 
1.2%
산업재해를 402
 
1.0%
이해하고 376
 
0.9%
관한 362
 
0.9%
통하여 347
 
0.9%
Other values (3051) 33873
83.2%
2023-12-12T13:09:23.737338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36984
 
20.1%
전 4829
 
2.6%
안 3753
 
2.0%
업 3504
 
1.9%
관 3157
 
1.7%
자 3120
 
1.7%
을 2997
 
1.6%
하 2971
 
1.6%
건 2874
 
1.6%
에 2810
 
1.5%
Other values (574) 116867
63.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 134224
73.0%
Space Separator 36984
 
20.1%
Decimal Number 3835
 
2.1%
Other Punctuation 3146
 
1.7%
Lowercase Letter 1579
 
0.9%
Control 1232
 
0.7%
Close Punctuation 802
 
0.4%
Dash Punctuation 735
 
0.4%
Uppercase Letter 666
 
0.4%
Open Punctuation 388
 
0.2%
Other values (6) 275
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 4829
 
3.6%
안 3753
 
2.8%
업 3504
 
2.6%
관 3157
 
2.4%
자 3120
 
2.3%
을 2997
 
2.2%
하 2971
 
2.2%
건 2874
 
2.1%
에 2810
 
2.1%
의 2672
 
2.0%
Other values (482) 101537
75.6%
Lowercase Letter
ValueCountFrequency (%)
e 205
13.0%
a 160
 
10.1%
t 157
 
9.9%
s 137
 
8.7%
r 114
 
7.2%
n 100
 
6.3%
d 99
 
6.3%
f 78
 
4.9%
o 71
 
4.5%
g 52
 
3.3%
Other values (16) 406
25.7%
Uppercase Letter
ValueCountFrequency (%)
M 109
16.4%
S 73
11.0%
D 73
11.0%
I 71
10.7%
N 59
8.9%
P 57
8.6%
L 35
 
5.3%
K 32
 
4.8%
H 27
 
4.1%
G 26
 
3.9%
Other values (12) 104
15.6%
Other Punctuation
ValueCountFrequency (%)
. 2063
65.6%
, 617
 
19.6%
% 172
 
5.5%
· 116
 
3.7%
: 81
 
2.6%
/ 66
 
2.1%
* 14
 
0.4%
" 4
 
0.1%
※ 4
 
0.1%
& 4
 
0.1%
Other values (3) 5
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 961
25.1%
3 751
19.6%
0 695
18.1%
2 551
14.4%
6 279
 
7.3%
8 185
 
4.8%
4 171
 
4.5%
9 88
 
2.3%
5 80
 
2.1%
7 74
 
1.9%
Math Symbol
ValueCountFrequency (%)
~ 47
49.0%
> 24
25.0%
< 21
21.9%
→ 3
 
3.1%
+ 1
 
1.0%
Close Punctuation
ValueCountFrequency (%)
) 778
97.0%
」 16
 
2.0%
] 8
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 364
93.8%
「 16
 
4.1%
[ 8
 
2.1%
Control
ValueCountFrequency (%)
1225
99.4%
7
 
0.6%
Other Symbol
ValueCountFrequency (%)
○ 64
97.0%
◆ 2
 
3.0%
Space Separator
ValueCountFrequency (%)
36984
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 735
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 106
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 5
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 134223
73.0%
Common 47397
 
25.8%
Latin 2245
 
1.2%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 4829
 
3.6%
안 3753
 
2.8%
업 3504
 
2.6%
관 3157
 
2.4%
자 3120
 
2.3%
을 2997
 
2.2%
하 2971
 
2.2%
건 2874
 
2.1%
에 2810
 
2.1%
의 2672
 
2.0%
Other values (481) 101536
75.6%
Latin
ValueCountFrequency (%)
e 205
 
9.1%
a 160
 
7.1%
t 157
 
7.0%
s 137
 
6.1%
r 114
 
5.1%
M 109
 
4.9%
n 100
 
4.5%
d 99
 
4.4%
f 78
 
3.5%
S 73
 
3.3%
Other values (38) 1013
45.1%
Common
ValueCountFrequency (%)
36984
78.0%
. 2063
 
4.4%
1225
 
2.6%
1 961
 
2.0%
) 778
 
1.6%
3 751
 
1.6%
- 735
 
1.6%
0 695
 
1.5%
, 617
 
1.3%
2 551
 
1.2%
Other values (34) 2037
 
4.3%
Han
ValueCountFrequency (%)
年 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 134114
72.9%
ASCII 49415
 
26.9%
None 148
 
0.1%
Compat Jamo 109
 
0.1%
Geometric Shapes 66
 
< 0.1%
Punctuation 10
 
< 0.1%
Arrows 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36984
74.8%
. 2063
 
4.2%
1225
 
2.5%
1 961
 
1.9%
) 778
 
1.6%
3 751
 
1.5%
- 735
 
1.5%
0 695
 
1.4%
, 617
 
1.2%
2 551
 
1.1%
Other values (73) 4055
 
8.2%
Hangul
ValueCountFrequency (%)
전 4829
 
3.6%
안 3753
 
2.8%
업 3504
 
2.6%
관 3157
 
2.4%
자 3120
 
2.3%
을 2997
 
2.2%
하 2971
 
2.2%
건 2874
 
2.1%
에 2810
 
2.1%
의 2672
 
2.0%
Other values (469) 101427
75.6%
None
ValueCountFrequency (%)
· 116
78.4%
」 16
 
10.8%
「 16
 
10.8%
Geometric Shapes
ValueCountFrequency (%)
○ 64
97.0%
◆ 2
 
3.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 51
46.8%
ㄹ 14
 
12.8%
ㅏ 7
 
6.4%
ㅁ 6
 
5.5%
ㆍ 6
 
5.5%
ㄴ 6
 
5.5%
ㅣ 5
 
4.6%
ㅓ 5
 
4.6%
ㅎ 4
 
3.7%
ㅡ 2
 
1.8%
Other values (2) 3
 
2.8%
Punctuation
ValueCountFrequency (%)
‘ 5
50.0%
※ 4
40.0%
’ 1
 
10.0%
Arrows
ValueCountFrequency (%)
→ 3
100.0%
CJK
ValueCountFrequency (%)
年 1
100.0%

STUDY_CONTENTS
Text

MISSING 

Distinct1469
Distinct (%)47.1%
Missing1461
Missing (%)31.9%
Memory size35.9 KiB
2023-12-12T13:09:24.084451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1022
Median length483
Mean length85.591463
Min length1

Characters and Unicode

Total characters266703
Distinct characters646
Distinct categories14 ?
Distinct scripts4 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1111 ?
Unique (%)35.7%

Sample

1st row이 과정은 산업안전보건법 제32조에 의한 법정직무교육(안전보건관리책임자)입니다.
2nd row1. 산업안전보건법의 이해와 동향 2. 안전보건경영시스템과 위험성 평가에 대한 이해
3rd row1. 산업안전보건개론의 이해 2. 자율안전보건관리 운용에 관한 능력함양
4th row창의적 산업보건교육 방법론, 코칭기법을 활용한 보건관리, 건강증진사업 기획하기, 근골격계부담작업과 증상관리, 작업환경관리 및 대책수립하기, 산업보건정보를 활용한 사업장 보건관리 등 ****의료인(간호사)면허 보수교육과 동시 이수 가능****
5th row이 과정은 산업안전보건법 제32조에 의한 법정직무교육(보건관리자)입니다. * 산안법 이해 및 재난 대응 * 작업 환경 측정 및 건강 검진 * 직업성 질환 관리 및 응급 처치 * 근골격계 예방 및 보건 관리 실무 * 개인 건강 관리
ValueCountFrequency (%)
4360
 
7.7%
및 1358
 
2.4%
· 651
 
1.1%
수 625
 
1.1%
산업안전보건법 609
 
1.1%
작업 487
 
0.9%
관리감독자 445
 
0.8%
이상 400
 
0.7%
근로자 361
 
0.6%
위한 322
 
0.6%
Other values (4186) 47165
83.1%
2023-12-12T13:09:24.593709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
53697
 
20.1%
5700
 
2.1%
전 4523
 
1.7%
업 4126
 
1.5%
교 3731
 
1.4%
안 3684
 
1.4%
육 3679
 
1.4%
0 3423
 
1.3%
관 3314
 
1.2%
자 3294
 
1.2%
Other values (636) 177532
66.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 164520
61.7%
Space Separator 53697
 
20.1%
Lowercase Letter 11555
 
4.3%
Decimal Number 11389
 
4.3%
Other Punctuation 10186
 
3.8%
Control 5724
 
2.1%
Dash Punctuation 2707
 
1.0%
Math Symbol 2210
 
0.8%
Close Punctuation 1747
 
0.7%
Open Punctuation 1618
 
0.6%
Other values (4) 1350
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 4523
 
2.7%
업 4126
 
2.5%
교 3731
 
2.3%
안 3684
 
2.2%
육 3679
 
2.2%
관 3314
 
2.0%
자 3294
 
2.0%
이 3063
 
1.9%
건 2958
 
1.8%
시 2846
 
1.7%
Other values (535) 129302
78.6%
Lowercase Letter
ValueCountFrequency (%)
o 1449
12.5%
r 1212
 
10.5%
e 1096
 
9.5%
t 1011
 
8.7%
n 673
 
5.8%
a 622
 
5.4%
b 577
 
5.0%
s 547
 
4.7%
c 486
 
4.2%
k 460
 
4.0%
Other values (16) 3422
29.6%
Uppercase Letter
ValueCountFrequency (%)
S 187
19.5%
M 99
10.3%
P 95
9.9%
T 63
 
6.6%
E 60
 
6.3%
H 49
 
5.1%
A 48
 
5.0%
B 45
 
4.7%
C 45
 
4.7%
D 37
 
3.9%
Other values (15) 229
23.9%
Other Punctuation
ValueCountFrequency (%)
. 3036
29.8%
, 2198
21.6%
: 2045
20.1%
/ 1058
 
10.4%
· 897
 
8.8%
* 586
 
5.8%
% 198
 
1.9%
※ 59
 
0.6%
' 45
 
0.4%
" 25
 
0.2%
Other values (3) 39
 
0.4%
Decimal Number
ValueCountFrequency (%)
0 3423
30.1%
1 2151
18.9%
3 1592
14.0%
2 1308
 
11.5%
6 1172
 
10.3%
8 582
 
5.1%
9 467
 
4.1%
4 333
 
2.9%
5 266
 
2.3%
7 95
 
0.8%
Math Symbol
ValueCountFrequency (%)
> 896
40.5%
< 772
34.9%
~ 314
 
14.2%
= 129
 
5.8%
+ 71
 
3.2%
→ 28
 
1.3%
Other Symbol
ValueCountFrequency (%)
★ 210
85.0%
○ 20
 
8.1%
◎ 14
 
5.7%
◇ 2
 
0.8%
◆ 1
 
0.4%
Letter Number
ValueCountFrequency (%)
Ⅱ 26
33.3%
Ⅰ 19
24.4%
Ⅲ 12
15.4%
Ⅳ 11
14.1%
Ⅴ 10
 
12.8%
Close Punctuation
ValueCountFrequency (%)
) 1537
88.0%
] 204
 
11.7%
」 6
 
0.3%
Open Punctuation
ValueCountFrequency (%)
( 1408
87.0%
[ 204
 
12.6%
「 6
 
0.4%
Control
ValueCountFrequency (%)
5700
99.6%
24
 
0.4%
Space Separator
ValueCountFrequency (%)
53697
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2707
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 164504
61.7%
Common 89593
33.6%
Latin 12590
 
4.7%
Han 16
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 4523
 
2.7%
업 4126
 
2.5%
교 3731
 
2.3%
안 3684
 
2.2%
육 3679
 
2.2%
관 3314
 
2.0%
자 3294
 
2.0%
이 3063
 
1.9%
건 2958
 
1.8%
시 2846
 
1.7%
Other values (534) 129286
78.6%
Latin
ValueCountFrequency (%)
o 1449
 
11.5%
r 1212
 
9.6%
e 1096
 
8.7%
t 1011
 
8.0%
n 673
 
5.3%
a 622
 
4.9%
b 577
 
4.6%
s 547
 
4.3%
c 486
 
3.9%
k 460
 
3.7%
Other values (46) 4457
35.4%
Common
ValueCountFrequency (%)
53697
59.9%
5700
 
6.4%
0 3423
 
3.8%
. 3036
 
3.4%
- 2707
 
3.0%
, 2198
 
2.5%
1 2151
 
2.4%
: 2045
 
2.3%
3 1592
 
1.8%
) 1537
 
1.7%
Other values (35) 11507
 
12.8%
Han
ValueCountFrequency (%)
內 16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 164242
61.6%
ASCII 100862
37.8%
None 909
 
0.3%
Compat Jamo 262
 
0.1%
Misc Symbols 210
 
0.1%
Number Forms 78
 
< 0.1%
Punctuation 59
 
< 0.1%
Geometric Shapes 37
 
< 0.1%
Arrows 28
 
< 0.1%
CJK 16
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
53697
53.2%
5700
 
5.7%
0 3423
 
3.4%
. 3036
 
3.0%
- 2707
 
2.7%
, 2198
 
2.2%
1 2151
 
2.1%
: 2045
 
2.0%
3 1592
 
1.6%
) 1537
 
1.5%
Other values (76) 22776
22.6%
Hangul
ValueCountFrequency (%)
전 4523
 
2.8%
업 4126
 
2.5%
교 3731
 
2.3%
안 3684
 
2.2%
육 3679
 
2.2%
관 3314
 
2.0%
자 3294
 
2.0%
이 3063
 
1.9%
건 2958
 
1.8%
시 2846
 
1.7%
Other values (524) 129024
78.6%
None
ValueCountFrequency (%)
· 897
98.7%
「 6
 
0.7%
」 6
 
0.7%
Compat Jamo
ValueCountFrequency (%)
ㅇ 211
80.5%
ㄹ 16
 
6.1%
ㄴ 10
 
3.8%
ㅣ 5
 
1.9%
ㅁ 4
 
1.5%
ㅎ 4
 
1.5%
ㅓ 4
 
1.5%
ㅏ 4
 
1.5%
ㅡ 2
 
0.8%
ㄷ 2
 
0.8%
Misc Symbols
ValueCountFrequency (%)
★ 210
100.0%
Punctuation
ValueCountFrequency (%)
※ 59
100.0%
Arrows
ValueCountFrequency (%)
→ 28
100.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 26
33.3%
Ⅰ 19
24.4%
Ⅲ 12
15.4%
Ⅳ 11
14.1%
Ⅴ 10
 
12.8%
Geometric Shapes
ValueCountFrequency (%)
○ 20
54.1%
◎ 14
37.8%
◇ 2
 
5.4%
◆ 1
 
2.7%
CJK
ValueCountFrequency (%)
內 16
100.0%

STUDY_TARGET
Text

MISSING 

Distinct426
Distinct (%)21.3%
Missing2581
Missing (%)56.4%
Memory size35.9 KiB
2023-12-12T13:09:24.869680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length100
Median length35
Mean length10.038577
Min length1

Characters and Unicode

Total characters20037
Distinct characters336
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique246 ?
Unique (%)12.3%

Sample

1st row안전보건관리책임자 신규교육 이수자
2nd row안전관리자(보수)
3rd row안전보건관리책임자
4th row안전보건관리책임자
5th row보건관리자
ValueCountFrequency (%)
관리감독자 294
 
7.8%
안전보건관리책임자 216
 
5.7%
및 216
 
5.7%
안전관리자 209
 
5.5%
교육희망자 202
 
5.3%
직원 102
 
2.7%
보건관리자 95
 
2.5%
92
 
2.4%
민간위탁기관 83
 
2.2%
지도요원 83
 
2.2%
Other values (459) 2189
57.9%
2023-12-12T13:09:25.329833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1839
 
9.2%
자 1609
 
8.0%
관 1213
 
6.1%
리 1016
 
5.1%
전 611
 
3.0%
안 543
 
2.7%
건 463
 
2.3%
사 409
 
2.0%
보 404
 
2.0%
원 389
 
1.9%
Other values (326) 11541
57.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 17106
85.4%
Space Separator 1839
 
9.2%
Lowercase Letter 350
 
1.7%
Other Punctuation 249
 
1.2%
Uppercase Letter 153
 
0.8%
Decimal Number 111
 
0.6%
Open Punctuation 89
 
0.4%
Close Punctuation 89
 
0.4%
Connector Punctuation 36
 
0.2%
Dash Punctuation 15
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
자 1609
 
9.4%
관 1213
 
7.1%
리 1016
 
5.9%
전 611
 
3.6%
안 543
 
3.2%
건 463
 
2.7%
사 409
 
2.4%
보 404
 
2.4%
원 389
 
2.3%
육 378
 
2.2%
Other values (273) 10071
58.9%
Uppercase Letter
ValueCountFrequency (%)
S 34
22.2%
L 21
13.7%
G 21
13.7%
K 16
10.5%
H 13
 
8.5%
T 12
 
7.8%
M 10
 
6.5%
E 7
 
4.6%
O 4
 
2.6%
B 3
 
2.0%
Other values (7) 12
 
7.8%
Lowercase Letter
ValueCountFrequency (%)
a 66
18.9%
e 44
12.6%
s 44
12.6%
h 36
10.3%
t 32
9.1%
o 28
8.0%
k 27
7.7%
n 17
 
4.9%
g 12
 
3.4%
f 12
 
3.4%
Other values (5) 32
9.1%
Decimal Number
ValueCountFrequency (%)
0 45
40.5%
2 30
27.0%
1 14
 
12.6%
8 7
 
6.3%
5 5
 
4.5%
6 5
 
4.5%
3 2
 
1.8%
4 2
 
1.8%
9 1
 
0.9%
Other Punctuation
ValueCountFrequency (%)
: 100
40.2%
. 81
32.5%
/ 21
 
8.4%
· 16
 
6.4%
, 15
 
6.0%
& 10
 
4.0%
' 6
 
2.4%
Space Separator
ValueCountFrequency (%)
1839
100.0%
Open Punctuation
ValueCountFrequency (%)
( 89
100.0%
Close Punctuation
ValueCountFrequency (%)
) 89
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 36
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 17105
85.4%
Common 2428
 
12.1%
Latin 503
 
2.5%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
자 1609
 
9.4%
관 1213
 
7.1%
리 1016
 
5.9%
전 611
 
3.6%
안 543
 
3.2%
건 463
 
2.7%
사 409
 
2.4%
보 404
 
2.4%
원 389
 
2.3%
육 378
 
2.2%
Other values (272) 10070
58.9%
Latin
ValueCountFrequency (%)
a 66
13.1%
e 44
 
8.7%
s 44
 
8.7%
h 36
 
7.2%
S 34
 
6.8%
t 32
 
6.4%
o 28
 
5.6%
k 27
 
5.4%
L 21
 
4.2%
G 21
 
4.2%
Other values (22) 150
29.8%
Common
ValueCountFrequency (%)
1839
75.7%
: 100
 
4.1%
( 89
 
3.7%
) 89
 
3.7%
. 81
 
3.3%
0 45
 
1.9%
_ 36
 
1.5%
2 30
 
1.2%
/ 21
 
0.9%
· 16
 
0.7%
Other values (11) 82
 
3.4%
Han
ValueCountFrequency (%)
全 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 17102
85.4%
ASCII 2915
 
14.5%
None 16
 
0.1%
Compat Jamo 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1839
63.1%
: 100
 
3.4%
( 89
 
3.1%
) 89
 
3.1%
. 81
 
2.8%
a 66
 
2.3%
0 45
 
1.5%
e 44
 
1.5%
s 44
 
1.5%
h 36
 
1.2%
Other values (42) 482
 
16.5%
Hangul
ValueCountFrequency (%)
자 1609
 
9.4%
관 1213
 
7.1%
리 1016
 
5.9%
전 611
 
3.6%
안 543
 
3.2%
건 463
 
2.7%
사 409
 
2.4%
보 404
 
2.4%
원 389
 
2.3%
육 378
 
2.2%
Other values (270) 10067
58.9%
None
ValueCountFrequency (%)
· 16
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅡ 2
66.7%
ㅈ 1
33.3%
CJK
ValueCountFrequency (%)
全 1
100.0%

SITE_CD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
E
3672 
J
905 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
E 3672
80.2%
J 905
 
19.8%

Length

2023-12-12T13:09:25.491618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:25.594563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
e 3672
80.2%
j 905
 
19.8%

EDU_TYPE_CD
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4817566
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:25.696440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7081497
Coefficient of variation (CV)1.0912229
Kurtosis0.22751485
Mean2.4817566
Median Absolute Deviation (MAD)0
Skewness1.3981736
Sum11359
Variance7.3340749
MonotonicityNot monotonic
2023-12-12T13:09:25.855077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 3454
75.5%
7 686
 
15.0%
9 133
 
2.9%
8 110
 
2.4%
6 77
 
1.7%
2 39
 
0.9%
11 26
 
0.6%
4 24
 
0.5%
3 18
 
0.4%
5 10
 
0.2%
ValueCountFrequency (%)
1 3454
75.5%
2 39
 
0.9%
3 18
 
0.4%
4 24
 
0.5%
5 10
 
0.2%
6 77
 
1.7%
7 686
 
15.0%
8 110
 
2.4%
9 133
 
2.9%
11 26
 
0.6%
ValueCountFrequency (%)
11 26
 
0.6%
9 133
 
2.9%
8 110
 
2.4%
7 686
 
15.0%
6 77
 
1.7%
5 10
 
0.2%
4 24
 
0.5%
3 18
 
0.4%
2 39
 
0.9%
1 3454
75.5%

EDU_DIV
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size35.9 KiB
<NA>
3672 
30
487 
20
418 

Length

Max length4
Median length4
Mean length3.6045445
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30
2nd row30
3rd row20
4th row30
5th row30

Common Values

ValueCountFrequency (%)
<NA> 3672
80.2%
30 487
 
10.6%
20 418
 
9.1%

Length

2023-12-12T13:09:26.014829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:09:26.134266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 3672
80.2%
30 487
 
10.6%
20 418
 
9.1%

COURSE_EDUFEE
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct74
Distinct (%)1.7%
Missing139
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean47023.336
Minimum0
Maximum19980000
Zeros976
Zeros (%)21.3%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:26.580387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q115000
median30000
Q330000
95-th percentile165000
Maximum19980000
Range19980000
Interquartile range (IQR)15000

Descriptive statistics

Standard deviation411841.62
Coefficient of variation (CV)8.758239
Kurtosis1550.9476
Mean47023.336
Median Absolute Deviation (MAD)15000
Skewness37.270446
Sum2.0868957 × 108
Variance1.6961352 × 1011
MonotonicityNot monotonic
2023-12-12T13:09:26.761775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30000 1595
34.8%
15000 1057
23.1%
0 976
21.3%
44000 242
 
5.3%
141000 93
 
2.0%
186000 50
 
1.1%
8000 49
 
1.1%
47000 32
 
0.7%
29000 29
 
0.6%
54000 24
 
0.5%
Other values (64) 291
 
6.4%
(Missing) 139
 
3.0%
ValueCountFrequency (%)
0 976
21.3%
1 1
 
< 0.1%
3 1
 
< 0.1%
30 1
 
< 0.1%
1000 1
 
< 0.1%
1500 2
 
< 0.1%
3000 4
 
0.1%
8000 49
 
1.1%
10000 3
 
0.1%
12000 3
 
0.1%
ValueCountFrequency (%)
19980000 1
 
< 0.1%
12000000 1
 
< 0.1%
11400000 1
 
< 0.1%
7500000 1
 
< 0.1%
3105000 1
 
< 0.1%
2250000 1
 
< 0.1%
720000 2
 
< 0.1%
400000 11
0.2%
387766 2
 
< 0.1%
315000 1
 
< 0.1%

RE_STUDY_PERIOD
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)0.7%
Missing1075
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean87.289263
Minimum0
Maximum365
Zeros201
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:26.923325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q190
median100
Q3100
95-th percentile100
Maximum365
Range365
Interquartile range (IQR)10

Descriptive statistics

Standard deviation34.582423
Coefficient of variation (CV)0.39618187
Kurtosis19.348606
Mean87.289263
Median Absolute Deviation (MAD)0
Skewness1.0055995
Sum305687
Variance1195.944
MonotonicityNot monotonic
2023-12-12T13:09:27.055771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
100 2186
47.8%
90 790
 
17.3%
0 201
 
4.4%
30 128
 
2.8%
10 74
 
1.6%
60 73
 
1.6%
365 16
 
0.3%
40 8
 
0.2%
20 5
 
0.1%
31 4
 
0.1%
Other values (13) 17
 
0.4%
(Missing) 1075
23.5%
ValueCountFrequency (%)
0 201
4.4%
1 2
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 3
 
0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 74
 
1.6%
11 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
365 16
 
0.3%
180 1
 
< 0.1%
150 1
 
< 0.1%
120 1
 
< 0.1%
100 2186
47.8%
90 790
 
17.3%
60 73
 
1.6%
50 2
 
< 0.1%
40 8
 
0.2%
31 4
 
0.1%

EDU_PLACE_CD
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4577
Missing (%)100.0%
Memory size40.4 KiB

ORG_CD
Text

MISSING 

Distinct331
Distinct (%)8.4%
Missing659
Missing (%)14.4%
Memory size35.9 KiB
2023-12-12T13:09:27.337330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length11.804747
Min length10

Characters and Unicode

Total characters46251
Distinct characters34
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

Unique116 ?
Unique (%)3.0%

Sample

1st rowCM150114104811
2nd rowCM170314150501
3rd rowCM161004170245
4th rowCM161004170245
5th rowCM111014180015
ValueCountFrequency (%)
12282034530 736
 
18.8%
12282048980 308
 
7.9%
cm111210230164 208
 
5.3%
21581406566 148
 
3.8%
61381324190 98
 
2.5%
31485148460 89
 
2.3%
cm111017170024 80
 
2.0%
cm111021170065 79
 
2.0%
cm150114104811 66
 
1.7%
0000g990005 59
 
1.5%
Other values (321) 2047
52.2%
2023-12-12T13:09:27.778355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8987
19.4%
1 8697
18.8%
2 5921
12.8%
8 4216
9.1%
4 3699
8.0%
3 3153
 
6.8%
5 2695
 
5.8%
6 2466
 
5.3%
9 2030
 
4.4%
7 1718
 
3.7%
Other values (24) 2669
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43582
94.2%
Uppercase Letter 2669
 
5.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 938
35.1%
M 834
31.2%
D 124
 
4.6%
E 104
 
3.9%
Y 94
 
3.5%
G 85
 
3.2%
W 58
 
2.2%
J 54
 
2.0%
Q 52
 
1.9%
L 48
 
1.8%
Other values (14) 278
 
10.4%
Decimal Number
ValueCountFrequency (%)
0 8987
20.6%
1 8697
20.0%
2 5921
13.6%
8 4216
9.7%
4 3699
8.5%
3 3153
 
7.2%
5 2695
 
6.2%
6 2466
 
5.7%
9 2030
 
4.7%
7 1718
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common 43582
94.2%
Latin 2669
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 938
35.1%
M 834
31.2%
D 124
 
4.6%
E 104
 
3.9%
Y 94
 
3.5%
G 85
 
3.2%
W 58
 
2.2%
J 54
 
2.0%
Q 52
 
1.9%
L 48
 
1.8%
Other values (14) 278
 
10.4%
Common
ValueCountFrequency (%)
0 8987
20.6%
1 8697
20.0%
2 5921
13.6%
8 4216
9.7%
4 3699
8.5%
3 3153
 
7.2%
5 2695
 
6.2%
6 2466
 
5.7%
9 2030
 
4.7%
7 1718
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 46251
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8987
19.4%
1 8697
18.8%
2 5921
12.8%
8 4216
9.1%
4 3699
8.0%
3 3153
 
6.8%
5 2695
 
5.8%
6 2466
 
5.3%
9 2030
 
4.4%
7 1718
 
3.7%
Other values (24) 2669
 
5.8%

LAW_ALLOW_TIME
Real number (ℝ)

MISSING 

Distinct42
Distinct (%)1.1%
Missing905
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean37.852941
Minimum0
Maximum365
Zeros7
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:27.939224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q130
median30
Q331
95-th percentile90
Maximum365
Range365
Interquartile range (IQR)1

Descriptive statistics

Standard deviation33.390904
Coefficient of variation (CV)0.88212179
Kurtosis52.898917
Mean37.852941
Median Absolute Deviation (MAD)0
Skewness6.4063651
Sum138996
Variance1114.9525
MonotonicityNot monotonic
2023-12-12T13:09:28.110363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
30 2504
54.7%
31 510
 
11.1%
60 151
 
3.3%
90 119
 
2.6%
5 95
 
2.1%
100 62
 
1.4%
120 49
 
1.1%
10 21
 
0.5%
40 20
 
0.4%
365 17
 
0.4%
Other values (32) 124
 
2.7%
(Missing) 905
 
19.8%
ValueCountFrequency (%)
0 7
 
0.2%
1 7
 
0.2%
2 2
 
< 0.1%
3 2
 
< 0.1%
5 95
2.1%
7 4
 
0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 21
 
0.5%
11 1
 
< 0.1%
ValueCountFrequency (%)
365 17
 
0.4%
300 11
 
0.2%
180 8
 
0.2%
161 1
 
< 0.1%
150 1
 
< 0.1%
139 1
 
< 0.1%
120 49
1.1%
100 62
1.4%
90 119
2.6%
64 6
 
0.1%

MOBILE_YN
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing905
Missing (%)19.8%
Memory size9.1 KiB
False
3573 
True
 
99
(Missing)
905 
ValueCountFrequency (%)
False 3573
78.1%
True 99
 
2.2%
(Missing) 905
 
19.8%
2023-12-12T13:09:28.245287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

REVIEW_PERIOD
Real number (ℝ)

MISSING 

Distinct6
Distinct (%)4.5%
Missing4443
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean32.746269
Minimum0
Maximum422
Zeros25
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:28.339169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q330
95-th percentile30
Maximum422
Range422
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.302706
Coefficient of variation (CV)1.8415138
Kurtosis37.916879
Mean32.746269
Median Absolute Deviation (MAD)0
Skewness6.1268845
Sum4388
Variance3636.4163
MonotonicityNot monotonic
2023-12-12T13:09:28.460546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 102
 
2.2%
0 25
 
0.5%
422 3
 
0.1%
10 2
 
< 0.1%
31 1
 
< 0.1%
11 1
 
< 0.1%
(Missing) 4443
97.1%
ValueCountFrequency (%)
0 25
 
0.5%
10 2
 
< 0.1%
11 1
 
< 0.1%
30 102
2.2%
31 1
 
< 0.1%
422 3
 
0.1%
ValueCountFrequency (%)
422 3
 
0.1%
31 1
 
< 0.1%
30 102
2.2%
11 1
 
< 0.1%
10 2
 
< 0.1%
0 25
 
0.5%

EXAM_APPR_COUNT
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)0.1%
Missing25
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5.254833
Minimum0
Maximum10
Zeros69
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size40.4 KiB
2023-12-12T13:09:28.586613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median5
Q35
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.7160809
Coefficient of variation (CV)0.32657192
Kurtosis4.0944648
Mean5.254833
Median Absolute Deviation (MAD)0
Skewness1.3511633
Sum23920
Variance2.9449338
MonotonicityNot monotonic
2023-12-12T13:09:28.736368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 3762
82.2%
10 425
 
9.3%
3 280
 
6.1%
0 69
 
1.5%
1 12
 
0.3%
2 4
 
0.1%
(Missing) 25
 
0.5%
ValueCountFrequency (%)
0 69
 
1.5%
1 12
 
0.3%
2 4
 
0.1%
3 280
 
6.1%
5 3762
82.2%
10 425
 
9.3%
ValueCountFrequency (%)
10 425
 
9.3%
5 3762
82.2%
3 280
 
6.1%
2 4
 
0.1%
1 12
 
0.3%
0 69
 
1.5%

Sample

COURSE_CDCOURSE_YEARCOURSE_NMCOURSE_DIV_CDCOURSE_TYPE_CDCAPACITYTOTAL_EDU_TIMEUSE_YNSTUDY_OBJECTIVESTUDY_CONTENTSSTUDY_TARGETSITE_CDEDU_TYPE_CDEDU_DIVCOURSE_EDUFEERE_STUDY_PERIODEDU_PLACE_CDORG_CDLAW_ALLOW_TIMEMOBILE_YNREVIEW_PERIODEXAM_APPR_COUNT
0CO1704041504762017안전보건관리책임자 보수교육(건설업)10KR1401COURSE00000001506.0Y안전보건괸리책임자의 직무와 역할을 이해하고 안전보건에 관한 전문적인 지식을 습득하여 안전보건경영 업무를 효과적으로 운영 할 수 있도록 하는 과정입니다.이 과정은 산업안전보건법 제32조에 의한 법정직무교육(안전보건관리책임자)입니다.안전보건관리책임자 신규교육 이수자J13052000<NA><NA>CM150114104811<NA><NA><NA>5
1CO1704051604812017안전관리자(보수)20KR1401COURSE000000014024.0Y<NA><NA>안전관리자(보수)J130141000<NA><NA>CM170314150501<NA><NA><NA>5
2CO1610051103882016안전보건관리책임자 신규(건설업)10KR1401COURSE00000001406.0Y현장 안전관리보건책임자의 안전보건관리능력을 함양하고 재해예방을 위한 안전의식 제고를 위한 교육1. 산업안전보건법의 이해와 동향 2. 안전보건경영시스템과 위험성 평가에 대한 이해안전보건관리책임자J12044000<NA><NA>CM161004170245<NA><NA><NA>5
3CO1610051103892016안전보건관리책임자 보수(건설업)10KR1401COURSE00000001406.0Y현장안전보건관리책임자의 직무능력의 함양과 안전보건관련법의 동향을 파악하여 현장의 재해예방에 기여1. 산업안전보건개론의 이해 2. 자율안전보건관리 운용에 관한 능력함양안전보건관리책임자J13044000<NA><NA>CM161004170245<NA><NA><NA>5
4CO1612221504152016직무스트레스와 감정노동(보건관리자보수면제)30KR1401COURSE000000013024.0Y보건관리에 필요한 산업보건 전문지식 및 전문기술을 습득하여 보건관리 업무역량을 강화하고자 함.창의적 산업보건교육 방법론, 코칭기법을 활용한 보건관리, 건강증진사업 기획하기, 근골격계부담작업과 증상관리, 작업환경관리 및 대책수립하기, 산업보건정보를 활용한 사업장 보건관리 등 ****의료인(간호사)면허 보수교육과 동시 이수 가능****<NA>J130400000<NA><NA>CM111014180015<NA><NA><NA>5
5CO1612221404112016보건관리자 신규(제조, 병원 등 전 사업장)30KR1401COURSE000000013034.0Y보건관리자가 사업장내 전반적인 보건에 관한 전문적인 지식을 습득하여 직무와 역 할을 이해하여 자율안전관리 업무를 가능토록하는 과정입니다. - 신규로 선임된 보건 관리자의 사업장 내 보건에 관한 전문 지식 습득 - 자율 안전 관리 업무를 가능하도록 함이 과정은 산업안전보건법 제32조에 의한 법정직무교육(보건관리자)입니다. * 산안법 이해 및 재난 대응 * 작업 환경 측정 및 건강 검진 * 직업성 질환 관리 및 응급 처치 * 근골격계 예방 및 보건 관리 실무 * 개인 건강 관리보건관리자J120226000<NA><NA>CM150114104811<NA><NA><NA>5
6CO1612221404122016보건관리자 보수(제조, 병원 등 전 사업장)30KR1401COURSE000000013024.0Y보건관리자가 사업장내 전반적인 보건에 관한 전문적인 지식을 습득하여 직무와 역할 을 이해하여 자율안전관리 업무를 가능토록하는 과정입니다. - 보건 관리자의 사업장 내 보건에 관한 전문 지식 습득 - 자율 안전 관리 업무를 가능하도록 함이 과정은 산업안전보건법 제32조에 의한 법정직무교육(보건관리자)입니다. ※ 의료인(간호사)면허 보수교육 동시 이수 불가 - 보건 관리 핵심 실무 및 비상 대응 전략 - 보관 관리 실무보건관리자 신규교육 이수자J130165000<NA><NA>CM150114104811<NA><NA><NA>5
7CO1703171604662017보건관리전문기관 종사자 보수교육40KR1401COURSE000000014024.0Y산업안전보건법 제 32조 및 동 시행규칙 39조에 규정된 사업주의 책무로서 선임된 보건관리전문기관의 기술지도 업무 종사자를 대상으로 분야별 전문지식 및 사례 습득을 통한 직무 수행 능력을 향상시키고자 함.교육과목 - 1일차 (09:00~18:00) - 2일차 (09:00~18:00) - 3일차 (09:00~18:00) *식비는 포함되어 있지 않으며, 시간표는 강사 및 기타 여건에 의해 변경될 수 있습니다.보건관리전문기관 종사자J130190000<NA><NA>CM111014180012<NA><NA><NA>5
8CO1704051204772017안전보건관리책임자(신규)10KR1401COURSE00000001406.0Y안전보건진흥원 집체과정 과정명:안전보건관리책임자 신규 6H안전보건진흥원 집체과정 과정명:안전보건관리책임자 신규 6H선임된지 3개월 이내인 안전보건관리책임자J12044000<NA><NA>CM170314150501<NA><NA><NA>5
9CO1604051703832016롯데건설 안전보수(건설업)20KR1401COURSE000000015024.0N산업안전보건법의 관리책임자등에 대한 직무교육을 통하여 안전관리자의 안전의식 제고 및 건설현장의 산업재해를 예방하고자함.* 맞춤형 직무교육으로 개별 신청 되지 않는 과정입니다.롯데건설 안전관리자J130<NA><NA><NA>CM111210230164<NA><NA><NA>5
COURSE_CDCOURSE_YEARCOURSE_NMCOURSE_DIV_CDCOURSE_TYPE_CDCAPACITYTOTAL_EDU_TIMEUSE_YNSTUDY_OBJECTIVESTUDY_CONTENTSSTUDY_TARGETSITE_CDEDU_TYPE_CDEDU_DIVCOURSE_EDUFEERE_STUDY_PERIODEDU_PLACE_CDORG_CDLAW_ALLOW_TIMEMOBILE_YNREVIEW_PERIODEXAM_APPR_COUNT
456717282558BBFGBLLFGKLG2020고용노동부 사이버교육(건설장비의 안전 II)180KR1401COURSE0000000210006.0Y산재예방분야 직무능력 향상-고용노동부 직원E7<NA>090<NA><NA>31N<NA>0
45681719C89A510VDHIFDEXC2020특수형태근로종사자 최초노무교육 및 특별교육(0.5H)_천공기90KR1401COURSE00000002300000.5Y건설기계(천공기)에서의 위험요소 및 재해예방 대책을 설명할 수 있다.-건설기계(천공기) 사고 예방 -경력자 최초노무교육 단기간,간헐적 작업교육 30분 인정 -신규자 특별교육 단기간, 간헐적 작업교육 30분 인정 -경력자 특별교육 단기간, 간헐적 작업교육 20분 인정 · (신규자) 경력 6개월 미만 신규 근로자, (경력자) 동일 업종·작업 6개월 이상 경력 근로자 · (단기간) 2개월 이내에 종료되는 1회성 작업, (간헐적) 연간 총 작업일수가 60일을 초과하지 않는 작업 -수료기준 : 100점 만점 60점 이상 - 학습 진도 80% 이상 시 평가 응시 가능 - 3문항 객관식 문항(60분 간 실시), 시험 응시 제한 3회특수형태근로종사자:천공기E9<NA>060<NA><NA>60N<NA>10
45691735B8B2BDFIXOVGXPGJ2020안전보건관리책임자 신규교육10KR1401COURSE00000001406.0N안전보건관리책임자를 대상으로 안전보건 분야별 전문지식 및 사례 습윽을 통한 직무수행 능력 향상안전보건관리책임자 신규교육으로 집체 6시간 진행관리책임자J12044000<NA><NA>17345DA1D5AKUSLGNGXD<NA><NA><NA>10
457017162FF36E9KQYFGECZZ2020안전관리전문기관종사자 보수40KR1401COURSE000000014024.0Y안전관리전문기관의 기술지도 업무 종사자를 대상으로 최신의 안전보건관련 전문지식 및 사례 습득을 통한 직무수행능력 향상교육안내공문 : 직무교육센터 > 나의 직무교육 > 수강신청현황,취소 > 참석공문 확인바랍니다. [교육시간] 1일차 09:00 ~ 18:00 2일차 09:00 ~ 18:00 3일차 09:00 ~ 18:00 [교육내용] 산업안전보건법, 건강증진 및 응급처치, 산업안전기준해설(화공), 산업안전기준해설(기계), 산업안전기준해설(전기), 위험성평가(이론), 위험성평가(실습), 보호구의 종류와 사용법, 관리대상유해물질에 의한 건강장해 예방 [편의사항] 간편한 아침식사대용 빵제공, 여러종류의 다과, 간식제공 [교육생 후기 및 교육맛보기] 홈페이지 : http://www.esti.or.kr/kor/ 카페 : https://cafe.naver.com/estipeople 블로그 : https://blog.naver.com/estikorea<NA>J130162000<NA><NA>16995873D9EDYJHLWYCQ<NA><NA><NA>10
45711744832DE7AMMAHVEYTI2020안전관리자 신규교육 (건설업, 제조업 및 기타업종)20KR1401COURSE000000016034.0Y<NA><NA>안전관리자J120211000<NA><NA>CM111014180014<NA><NA><NA>10
45721749ED9672ATRFOLJAUT2020석면조사기관의 종사자(12월신규)40KR1401COURSE000000015034.0Y석면조사기관의 기술지도 업무 종사자를 대상으로 분야별 전문지식 및 사례 습득을 통한 직무 수행 능력 향상-석면 제품의 종류 및 구별 방법 -석면에 의한 건강유해성 -석면 관련 법령 및 제도, 산업안전 보건 정책 방향 -석면 시료채취 및 분석 방법 -보호구 착용 -석면조사결과서 및 석면지도 작성 방법 -석면 조사 실습석면조사기관의 석면조사·분석 업무 신규 종사자J120236000<NA><NA>172C0840CC1ENWEVOMIC<NA><NA><NA>10
457317398129A06RNCBXVYYZ2020산재예방요율제 사업주교육(부산·울산·경남)290KR1401COURSE0000000210004.0Y「코로나19」장기화에 따른 안전보건교육의 수요자 요구 충족을 위하여 일부 교육을 인터넷 과정(개설)으로 전환하여 교육 효과성 제고ㅇ 코로나19로 인한 한시적 안전보건 교육과정입니다. * 기존 집체교육 → 한시적으로 인터넷교육 전환 ㅇ 산재예방요율제 사업주교육은 "사업주" 대상 교육으로서, 반드시 사업주께서 교육수강 및 진행하시기 바랍니다. 대리수강 시 예방요율제 적용 사업장으로 인정 받으실 수 없습니다.'20년 인정기간 도래 사업장E11<NA>090<NA><NA>30N<NA>0
45741719C5E7762JQMKXDMOH2020특수형태근로종사자 최초노무교육 및 특별교육(0.5H)_콘크리트뱃칭플랜트90KR1401COURSE00000002300000.5Y건설기계(콘크리트뱃칭플랜트)에서의 위험요소 및 재해예방 대책을 설명할 수 있다.-건설기계(콘크리트뱃칭플랜트) 사고 예방 -경력자 최초노무교육 단기간,간헐적 작업교육 30분 인정 -신규자 특별교육 단기간, 간헐적 작업교육 30분 인정 -경력자 특별교육 단기간, 간헐적 작업교육 20분 인정 · (신규자) 경력 6개월 미만 신규 근로자, (경력자) 동일 업종·작업 6개월 이상 경력 근로자 · (단기간) 2개월 이내에 종료되는 1회성 작업, (간헐적) 연간 총 작업일수가 60일을 초과하지 않는 작업 -수료기준 : 100점 만점 60점 이상 - 학습 진도 80% 이상 시 평가 응시 가능 - 3문항 객관식 문항(60분 간 실시), 시험 응시 제한 3회특수형태근로종사자_콘크리트뱃칭플랜트E9<NA>060<NA><NA>60N<NA>10
4575172C51AE7E3UZIUNJGAN2020건설현장 화재·폭발 예방교육(현장관리자)290KR1401COURSE0000000210002.0Y「코로나19」장기화에 따른 안전보건교육 추진의 어려움 및 수요자 요구 충족을 위하여 일부 교육을 인터넷 과정(개설)으로 전환하여 교육 효과성 제고안전보건교육 코로나19로 인한 한시적 교육과정입니다. * 기존 집체교육 → 한시적으로 인터넷교육 전환교육대상자E11<NA>090<NA><NA>30N<NA>0
45761719C39A033GOINKXOFG2020특수형태근로종사자 최초노무교육 및 특별교육(0.5H)_골재살포기90KR1401COURSE00000002300000.5Y건설기계(골재살포기)에서의 위험요소 및 재해예방 대책을 설명할 수 있다.-건설기계(골재살포기) 사고 예방 -경력자 최초노무교육 단기간,간헐적 작업교육 30분 인정 -신규자 특별교육 단기간, 간헐적 작업교육 30분 인정 -경력자 특별교육 단기간, 간헐적 작업교육 20분 인정 · (신규자) 경력 6개월 미만 신규 근로자, (경력자) 동일 업종·작업 6개월 이상 경력 근로자 · (단기간) 2개월 이내에 종료되는 1회성 작업, (간헐적) 연간 총 작업일수가 60일을 초과하지 않는 작업 -수료기준 : 100점 만점 60점 이상 - 학습 진도 80% 이상 시 평가 응시 가능 - 3문항 객관식 문항(60분 간 실시), 시험 응시 제한 3회특수형태근로종사자_골재살포기E9<NA>060<NA><NA>60N<NA>10