Overview

Dataset statistics

Number of variables8
Number of observations170
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory66.8 B

Variable types

Text2
Categorical3
DateTime1
Numeric2

Dataset

Description과거 (2014년 10월 ~ 2020년 9월) 화평법 화관법 산업계 도움센터에서 제공한 화학법령 관련 온/오프라인 교육신청정보(교육명/교육지역/교육장소/교육일자/교육시작시간/교육종료시간/참석인원/정원)
Author한국환경공단
URLhttps://www.data.go.kr/data/15066943/fileData.do

Alerts

교육시작시간 is highly overall correlated with 교육종료시간High correlation
교육종료시간 is highly overall correlated with 교육시작시간High correlation
참석 has 61 (35.9%) zerosZeros

Reproduction

Analysis started2023-12-12 03:26:36.805450
Analysis finished2023-12-12 03:26:38.842213
Duration2.04 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

교육명
Text

Distinct148
Distinct (%)87.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T12:26:39.146327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length39
Mean length22.294118
Min length11

Characters and Unicode

Total characters3790
Distinct characters226
Distinct categories13 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique132 ?
Unique (%)77.6%

Sample

1st row[온라인교육]화평법 공동등록 전문교육(기본과정)
2nd row[온라인교육]위해성자료 작성 실무과정
3rd row[온라인교육]고분자 화합물 등록과정
4th row[온라인교육]화평법 공동등록 전문교육(실무과정)
5th row[온라인교육]화평법 공동등록 전문교육(기본과정)
ValueCountFrequency (%)
공동등록 52
 
6.5%
화학법령 50
 
6.3%
권역별 45
 
5.6%
화평법 34
 
4.3%
교육 28
 
3.5%
화학물질 26
 
3.3%
생활 23
 
2.9%
전과정 21
 
2.6%
화학 20
 
2.5%
안전주간 19
 
2.4%
Other values (227) 481
60.2%
2023-12-12T12:26:39.748675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
620
 
16.4%
화 184
 
4.9%
육 121
 
3.2%
교 121
 
3.2%
법 113
 
3.0%
학 112
 
3.0%
전 101
 
2.7%
[ 89
 
2.3%
] 89
 
2.3%
등 62
 
1.6%
Other values (216) 2178
57.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2664
70.3%
Space Separator 638
 
16.8%
Decimal Number 114
 
3.0%
Open Punctuation 111
 
2.9%
Close Punctuation 111
 
2.9%
Connector Punctuation 50
 
1.3%
Other Punctuation 44
 
1.2%
Lowercase Letter 22
 
0.6%
Dash Punctuation 13
 
0.3%
Letter Number 12
 
0.3%
Other values (3) 11
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
화 184
 
6.9%
육 121
 
4.5%
교 121
 
4.5%
법 113
 
4.2%
학 112
 
4.2%
전 101
 
3.8%
등 62
 
2.3%
록 61
 
2.3%
제 60
 
2.3%
동 58
 
2.2%
Other values (171) 1671
62.7%
Lowercase Letter
ValueCountFrequency (%)
e 4
18.2%
n 3
13.6%
c 2
9.1%
i 2
9.1%
h 2
9.1%
l 1
 
4.5%
m 1
 
4.5%
a 1
 
4.5%
t 1
 
4.5%
s 1
 
4.5%
Other values (4) 4
18.2%
Decimal Number
ValueCountFrequency (%)
1 28
24.6%
2 27
23.7%
0 22
19.3%
6 13
11.4%
7 11
 
9.6%
3 5
 
4.4%
5 3
 
2.6%
4 2
 
1.8%
8 2
 
1.8%
9 1
 
0.9%
Uppercase Letter
ValueCountFrequency (%)
C 2
22.2%
P 2
22.2%
G 2
22.2%
L 2
22.2%
I 1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 26
59.1%
· 13
29.5%
" 4
 
9.1%
. 1
 
2.3%
Space Separator
ValueCountFrequency (%)
620
97.2%
　 18
 
2.8%
Open Punctuation
ValueCountFrequency (%)
[ 89
80.2%
( 22
 
19.8%
Close Punctuation
ValueCountFrequency (%)
] 89
80.2%
) 22
 
19.8%
Letter Number
ValueCountFrequency (%)
Ⅱ 6
50.0%
Ⅰ 6
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 1
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2663
70.3%
Common 1083
28.6%
Latin 43
 
1.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
화 184
 
6.9%
육 121
 
4.5%
교 121
 
4.5%
법 113
 
4.2%
학 112
 
4.2%
전 101
 
3.8%
등 62
 
2.3%
록 61
 
2.3%
제 60
 
2.3%
동 58
 
2.2%
Other values (170) 1670
62.7%
Common
ValueCountFrequency (%)
620
57.2%
[ 89
 
8.2%
] 89
 
8.2%
_ 50
 
4.6%
1 28
 
2.6%
2 27
 
2.5%
, 26
 
2.4%
0 22
 
2.0%
( 22
 
2.0%
) 22
 
2.0%
Other values (14) 88
 
8.1%
Latin
ValueCountFrequency (%)
Ⅱ 6
14.0%
Ⅰ 6
14.0%
e 4
 
9.3%
n 3
 
7.0%
c 2
 
4.7%
i 2
 
4.7%
h 2
 
4.7%
C 2
 
4.7%
P 2
 
4.7%
G 2
 
4.7%
Other values (11) 12
27.9%
Han
ValueCountFrequency (%)
新 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2663
70.3%
ASCII 1081
28.5%
None 31
 
0.8%
Number Forms 12
 
0.3%
Punctuation 2
 
0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
620
57.4%
[ 89
 
8.2%
] 89
 
8.2%
_ 50
 
4.6%
1 28
 
2.6%
2 27
 
2.5%
, 26
 
2.4%
0 22
 
2.0%
( 22
 
2.0%
) 22
 
2.0%
Other values (29) 86
 
8.0%
Hangul
ValueCountFrequency (%)
화 184
 
6.9%
육 121
 
4.5%
교 121
 
4.5%
법 113
 
4.2%
학 112
 
4.2%
전 101
 
3.8%
등 62
 
2.3%
록 61
 
2.3%
제 60
 
2.3%
동 58
 
2.2%
Other values (170) 1670
62.7%
None
ValueCountFrequency (%)
　 18
58.1%
· 13
41.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 6
50.0%
Ⅰ 6
50.0%
CJK
ValueCountFrequency (%)
新 1
100.0%
Punctuation
ValueCountFrequency (%)
‘ 1
50.0%
’ 1
50.0%

교육지역
Categorical

Distinct16
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
서울
61 
경기
27 
<NA>
12 
부산
10 
대전
9 
Other values (11)
51 

Length

Max length4
Median length2
Mean length2.1411765
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
서울 61
35.9%
경기 27
15.9%
<NA> 12
 
7.1%
부산 10
 
5.9%
대전 9
 
5.3%
충북 7
 
4.1%
광주 6
 
3.5%
인천 6
 
3.5%
울산 6
 
3.5%
경남 5
 
2.9%
Other values (6) 21
 
12.4%

Length

2023-12-12T12:26:40.005382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 61
35.9%
경기 27
15.9%
na 12
 
7.1%
부산 10
 
5.9%
대전 9
 
5.3%
충북 7
 
4.1%
광주 6
 
3.5%
인천 6
 
3.5%
울산 6
 
3.5%
경남 5
 
2.9%
Other values (6) 21
 
12.4%

교육장소
Text

Distinct111
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2023-12-12T12:26:40.312774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length13.688235
Min length5

Characters and Unicode

Total characters2327
Distinct characters201
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

Unique82 ?
Unique (%)48.2%

Sample

1st row온라인교육
2nd row온라인교육
3rd row온라인교육
4th row온라인교육
5th row온라인교육
ValueCountFrequency (%)
대강당 20
 
4.2%
대회의실 15
 
3.2%
온라인교육 12
 
2.5%
엘타워 12
 
2.5%
코엑스 10
 
2.1%
제 10
 
2.1%
소재 10
 
2.1%
상공회의소 10
 
2.1%
킨텍스 9
 
1.9%
일산 9
 
1.9%
Other values (169) 355
75.2%
2023-12-12T12:26:40.806639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
301
 
12.9%
회 95
 
4.1%
의 94
 
4.0%
대 72
 
3.1%
소 56
 
2.4%
실 49
 
2.1%
강 45
 
1.9%
공 43
 
1.8%
관 40
 
1.7%
층 39
 
1.7%
Other values (191) 1493
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1718
73.8%
Space Separator 305
 
13.1%
Decimal Number 147
 
6.3%
Uppercase Letter 51
 
2.2%
Lowercase Letter 48
 
2.1%
Close Punctuation 23
 
1.0%
Open Punctuation 23
 
1.0%
Other Punctuation 8
 
0.3%
Connector Punctuation 2
 
0.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 95
 
5.5%
의 94
 
5.5%
대 72
 
4.2%
소 56
 
3.3%
실 49
 
2.9%
강 45
 
2.6%
공 43
 
2.5%
관 40
 
2.3%
층 39
 
2.3%
상 36
 
2.1%
Other values (152) 1149
66.9%
Uppercase Letter
ValueCountFrequency (%)
C 10
19.6%
B 6
11.8%
T 6
11.8%
N 6
11.8%
K 3
 
5.9%
D 3
 
5.9%
R 3
 
5.9%
X 3
 
5.9%
L 3
 
5.9%
M 2
 
3.9%
Other values (4) 6
11.8%
Decimal Number
ValueCountFrequency (%)
2 33
22.4%
1 33
22.4%
5 25
17.0%
3 19
12.9%
0 19
12.9%
7 9
 
6.1%
8 5
 
3.4%
4 4
 
2.7%
Lowercase Letter
ValueCountFrequency (%)
l 18
37.5%
h 12
25.0%
a 9
18.8%
z 3
 
6.2%
e 3
 
6.2%
i 3
 
6.2%
Other Punctuation
ValueCountFrequency (%)
& 5
62.5%
. 2
 
25.0%
, 1
 
12.5%
Space Separator
ValueCountFrequency (%)
301
98.7%
　 4
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 19
82.6%
] 4
 
17.4%
Open Punctuation
ValueCountFrequency (%)
( 19
82.6%
[ 4
 
17.4%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1718
73.8%
Common 510
 
21.9%
Latin 99
 
4.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 95
 
5.5%
의 94
 
5.5%
대 72
 
4.2%
소 56
 
3.3%
실 49
 
2.9%
강 45
 
2.6%
공 43
 
2.5%
관 40
 
2.3%
층 39
 
2.3%
상 36
 
2.1%
Other values (152) 1149
66.9%
Latin
ValueCountFrequency (%)
l 18
18.2%
h 12
12.1%
C 10
10.1%
a 9
 
9.1%
B 6
 
6.1%
T 6
 
6.1%
N 6
 
6.1%
z 3
 
3.0%
K 3
 
3.0%
D 3
 
3.0%
Other values (10) 23
23.2%
Common
ValueCountFrequency (%)
301
59.0%
2 33
 
6.5%
1 33
 
6.5%
5 25
 
4.9%
3 19
 
3.7%
) 19
 
3.7%
( 19
 
3.7%
0 19
 
3.7%
7 9
 
1.8%
& 5
 
1.0%
Other values (9) 28
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1718
73.8%
ASCII 605
 
26.0%
None 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301
49.8%
2 33
 
5.5%
1 33
 
5.5%
5 25
 
4.1%
3 19
 
3.1%
) 19
 
3.1%
( 19
 
3.1%
0 19
 
3.1%
l 18
 
3.0%
h 12
 
2.0%
Other values (28) 107
 
17.7%
Hangul
ValueCountFrequency (%)
회 95
 
5.5%
의 94
 
5.5%
대 72
 
4.2%
소 56
 
3.3%
실 49
 
2.9%
강 45
 
2.6%
공 43
 
2.5%
관 40
 
2.3%
층 39
 
2.3%
상 36
 
2.1%
Other values (152) 1149
66.9%
None
ValueCountFrequency (%)
　 4
100.0%

교육일자
Date

Distinct153
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum2014-10-24 00:00:00
Maximum2020-09-23 00:00:00
2023-12-12T12:26:41.000521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:26:41.236463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

교육시작시간
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
14:00
109 
09:00
28 
13:00
12 
10:00
 
7
15:00
 
6
Other values (4)
 
8

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique3 ?
Unique (%)1.8%

Sample

1st row14:00
2nd row13:30
3rd row14:00
4th row13:30
5th row14:00

Common Values

ValueCountFrequency (%)
14:00 109
64.1%
09:00 28
 
16.5%
13:00 12
 
7.1%
10:00 7
 
4.1%
15:00 6
 
3.5%
13:30 5
 
2.9%
09:30 1
 
0.6%
13:10 1
 
0.6%
15:30 1
 
0.6%

Length

2023-12-12T12:26:41.477236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T12:26:41.676848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14:00 109
64.1%
09:00 28
 
16.5%
13:00 12
 
7.1%
10:00 7
 
4.1%
15:00 6
 
3.5%
13:30 5
 
2.9%
09:30 1
 
0.6%
13:10 1
 
0.6%
15:30 1
 
0.6%

교육종료시간
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
17:00
94 
18:00
37 
16:00
16 
15:30
 
7
12:00
 
4
Other values (7)
12 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique4 ?
Unique (%)2.4%

Sample

1st row16:00
2nd row17:15
3rd row17:00
4th row17:00
5th row16:00

Common Values

ValueCountFrequency (%)
17:00 94
55.3%
18:00 37
 
21.8%
16:00 16
 
9.4%
15:30 7
 
4.1%
12:00 4
 
2.4%
17:30 4
 
2.4%
17:15 2
 
1.2%
16:30 2
 
1.2%
11:30 1
 
0.6%
16:40 1
 
0.6%
Other values (2) 2
 
1.2%

Length

2023-12-12T12:26:41.887003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17:00 94
55.3%
18:00 37
 
21.8%
16:00 16
 
9.4%
15:30 7
 
4.1%
12:00 4
 
2.4%
17:30 4
 
2.4%
17:15 2
 
1.2%
16:30 2
 
1.2%
11:30 1
 
0.6%
16:40 1
 
0.6%
Other values (2) 2
 
1.2%

참석
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)47.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.094118
Minimum0
Maximum533
Zeros61
Zeros (%)35.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T12:26:42.099051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32.5
Q397.5
95-th percentile287.75
Maximum533
Range533
Interquartile range (IQR)97.5

Descriptive statistics

Standard deviation100.04586
Coefficient of variation (CV)1.3687266
Kurtosis4.0388968
Mean73.094118
Median Absolute Deviation (MAD)32.5
Skewness1.9218439
Sum12426
Variance10009.175
MonotonicityNot monotonic
2023-12-12T12:26:42.324352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 61
35.9%
150 6
 
3.5%
80 5
 
2.9%
45 3
 
1.8%
29 3
 
1.8%
39 3
 
1.8%
21 3
 
1.8%
2 2
 
1.2%
24 2
 
1.2%
175 2
 
1.2%
Other values (70) 80
47.1%
ValueCountFrequency (%)
0 61
35.9%
1 1
 
0.6%
2 2
 
1.2%
3 1
 
0.6%
7 1
 
0.6%
12 1
 
0.6%
13 1
 
0.6%
16 1
 
0.6%
19 1
 
0.6%
21 3
 
1.8%
ValueCountFrequency (%)
533 1
0.6%
440 1
0.6%
407 1
0.6%
394 1
0.6%
360 1
0.6%
340 1
0.6%
300 1
0.6%
298 1
0.6%
290 1
0.6%
285 1
0.6%

정원
Real number (ℝ)

Distinct48
Distinct (%)28.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.34706
Minimum0
Maximum660
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2023-12-12T12:26:42.515467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q180
median135
Q3237.5
95-th percentile416.5
Maximum660
Range660
Interquartile range (IQR)157.5

Descriptive statistics

Standard deviation122.76572
Coefficient of variation (CV)0.73800959
Kurtosis1.4093644
Mean166.34706
Median Absolute Deviation (MAD)65
Skewness1.2441453
Sum28279
Variance15071.423
MonotonicityNot monotonic
2023-12-12T12:26:42.689104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
150 14
 
8.2%
80 14
 
8.2%
100 12
 
7.1%
250 10
 
5.9%
200 10
 
5.9%
30 9
 
5.3%
90 9
 
5.3%
70 7
 
4.1%
300 7
 
4.1%
35 6
 
3.5%
Other values (38) 72
42.4%
ValueCountFrequency (%)
0 1
 
0.6%
28 1
 
0.6%
30 9
5.3%
35 6
3.5%
39 1
 
0.6%
40 2
 
1.2%
50 5
2.9%
55 4
2.4%
60 1
 
0.6%
62 1
 
0.6%
ValueCountFrequency (%)
660 1
 
0.6%
550 1
 
0.6%
480 2
1.2%
460 1
 
0.6%
450 1
 
0.6%
440 1
 
0.6%
430 2
1.2%
400 4
2.4%
380 1
 
0.6%
370 1
 
0.6%

Interactions

2023-12-12T12:26:37.725831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:26:37.415038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:26:38.293918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:26:37.577915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:26:42.813443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육지역교육시작시간교육종료시간참석정원
교육지역1.0000.0000.0000.0000.562
교육시작시간0.0001.0000.9010.4490.405
교육종료시간0.0000.9011.0000.2630.000
참석0.0000.4490.2631.0000.909
정원0.5620.4050.0000.9091.000
2023-12-12T12:26:42.956434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
교육종료시간교육시작시간교육지역
교육종료시간1.0000.6690.000
교육시작시간0.6691.0000.000
교육지역0.0000.0001.000
2023-12-12T12:26:43.109919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
참석정원교육지역교육시작시간교육종료시간
참석1.0000.0790.0000.2210.111
정원0.0791.0000.2410.1960.000
교육지역0.0000.2411.0000.0000.000
교육시작시간0.2210.1960.0001.0000.669
교육종료시간0.1110.0000.0000.6691.000

Missing values

2023-12-12T12:26:38.524300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:26:38.753930image/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

교육명교육지역교육장소교육일자교육시작시간교육종료시간참석정원
0[온라인교육]화평법 공동등록 전문교육(기본과정)<NA>온라인교육2020-09-2314:0016:00236250
1[온라인교육]위해성자료 작성 실무과정<NA>온라인교육2020-09-2213:3017:15171250
2[온라인교육]고분자 화합물 등록과정<NA>온라인교육2020-09-1714:0017:00145250
3[온라인교육]화평법 공동등록 전문교육(실무과정)<NA>온라인교육2020-09-1513:3017:00245250
4[온라인교육]화평법 공동등록 전문교육(기본과정)<NA>온라인교육2020-09-1014:0016:00244250
5[온라인교육]위해성자료 작성 실무과정<NA>온라인교육2020-09-0813:3017:15176250
6[온라인교육]화평법 공동등록 전문교육(실무과정)<NA>온라인교육2020-09-0413:3017:00167250
7[온라인교육]고분자 화합물 등록과정<NA>온라인교육2020-09-0214:0017:00169250
8화평법 공동등록 전문교육(실무과정)<NA>온라인교육2020-07-3114:0016:30150150
9위해성자료 작성 실무과정<NA>온라인교육2020-07-2814:0016:00150150
교육명교육지역교육장소교육일자교육시작시간교육종료시간참석정원
160화평법·화관법 제도설명회_익산전북익산 국민생활관 소극장2014-12-1613:0018:000180
161화평법·화관법 제도설명회_광주전남광주 김대중컨벤션센터 중소회의실2014-12-1513:0018:000180
162화평법·화관법 제도설명회_대구경북대구 교통연수원 대강당2014-12-1213:0018:000480
163화평법·화관법 제도설명회_서울서울종로 구민회관 대강당2014-12-0813:0018:000370
164화평법·화관법 제도설명회_울산울산울산 상공회의소 7층 대회의실2014-12-0513:0018:000430
165화평법·화관법 제도설명회_창원경남창원_경남도청 대강당2014-12-0413:0018:000480
166화평법·화관법 제도설명회_화성경기화성상공회의소 4층 컨벤션홀2014-12-0113:0018:000230
167화평법·화관법 제도설명회_인천인천인천 상공회의소 대강당2014-11-2813:0018:000280
168화평법·화관법 제도설명회_시흥경기시흥평생학습센터 대공연장2014-11-2413:0018:000300
169충청북도 유해화학물질 안전관리 정책 토론회충북충청북도 도의회 회의실2014-10-2414:0016:0000