Overview

Dataset statistics

Number of variables4
Number of observations641
Missing cells285
Missing cells (%)11.1%
Duplicate rows24
Duplicate rows (%)3.7%
Total size in memory20.2 KiB
Average record size in memory32.2 B

Variable types

Categorical1
Text3

Dataset

Description한국보건의료인국가시험에서 사용하는 용어에 대한 영어표기 및 정의를 제공(대분류,용어,영어표기,정의)합니다.
URLhttps://www.data.go.kr/data/15054624/fileData.do

Alerts

Dataset has 24 (3.7%) duplicate rowsDuplicates
정의 has 283 (44.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:02:51.977439
Analysis finished2023-12-12 22:02:52.830199
Duration0.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대분류
Categorical

Distinct6
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
직종별 국가시험 및 과목
236 
교육평가
117 
시험
113 
문항
78 
위원회 등 조직
50 

Length

Max length13
Median length8
Mean length6.8829953
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
직종별 국가시험 및 과목 236
36.8%
교육평가 117
18.3%
시험 113
17.6%
문항 78
 
12.2%
위원회 등 조직 50
 
7.8%
연구 47
 
7.3%

Length

2023-12-13T07:02:52.913155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:02:53.035160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직종별 236
16.3%
국가시험 236
16.3%
및 236
16.3%
과목 236
16.3%
교육평가 117
8.1%
시험 113
7.8%
문항 78
 
5.4%
위원회 50
 
3.5%
등 50
 
3.5%
조직 50
 
3.5%

용어
Text

Distinct593
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Memory size5.1 KiB
2023-12-13T07:02:53.370985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length18
Mean length6.0904836
Min length1

Characters and Unicode

Total characters3904
Distinct characters308
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique566 ?
Unique (%)88.3%

Sample

1st row「간호조무사 및 의료유사업자에 관한 규칙」
2nd row감독관
3rd row감독관 교육
4th row감독관 근무지침
5th row검사
ValueCountFrequency (%)
국가시험 35
 
3.9%
문항 31
 
3.5%
실기시험 18
 
2.0%
및 11
 
1.2%
10
 
1.1%
1급 8
 
0.9%
2급 8
 
0.9%
의료관계법규 8
 
0.9%
보건교육사 6
 
0.7%
예비시험 6
 
0.7%
Other values (604) 749
84.2%
2023-12-13T07:02:53.879242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
267
 
6.8%
사 130
 
3.3%
시 124
 
3.2%
험 95
 
2.4%
학 90
 
2.3%
기 85
 
2.2%
의 84
 
2.2%
문 77
 
2.0%
항 73
 
1.9%
가 69
 
1.8%
Other values (298) 2810
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3481
89.2%
Space Separator 267
 
6.8%
Uppercase Letter 44
 
1.1%
Lowercase Letter 32
 
0.8%
Decimal Number 23
 
0.6%
Open Punctuation 19
 
0.5%
Close Punctuation 19
 
0.5%
Other Punctuation 15
 
0.4%
Dash Punctuation 2
 
0.1%
Letter Number 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 130
 
3.7%
시 124
 
3.6%
험 95
 
2.7%
학 90
 
2.6%
기 85
 
2.4%
의 84
 
2.4%
문 77
 
2.2%
항 73
 
2.1%
가 69
 
2.0%
보 68
 
2.0%
Other values (252) 2586
74.3%
Uppercase Letter
ValueCountFrequency (%)
C 6
13.6%
B 6
13.6%
A 4
9.1%
T 4
9.1%
P 4
9.1%
I 3
6.8%
S 3
6.8%
D 3
6.8%
E 2
 
4.5%
R 2
 
4.5%
Other values (7) 7
15.9%
Lowercase Letter
ValueCountFrequency (%)
o 5
15.6%
f 4
12.5%
r 3
9.4%
n 3
9.4%
k 3
9.4%
a 2
 
6.2%
b 2
 
6.2%
g 2
 
6.2%
e 2
 
6.2%
c 1
 
3.1%
Other values (5) 5
15.6%
Decimal Number
ValueCountFrequency (%)
1 10
43.5%
2 10
43.5%
3 3
 
13.0%
Other Punctuation
ValueCountFrequency (%)
/ 10
66.7%
, 4
 
26.7%
· 1
 
6.7%
Open Punctuation
ValueCountFrequency (%)
「 12
63.2%
( 7
36.8%
Close Punctuation
ValueCountFrequency (%)
」 12
63.2%
) 7
36.8%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%
Space Separator
ValueCountFrequency (%)
267
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3481
89.2%
Common 345
 
8.8%
Latin 78
 
2.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 130
 
3.7%
시 124
 
3.6%
험 95
 
2.7%
학 90
 
2.6%
기 85
 
2.4%
의 84
 
2.4%
문 77
 
2.2%
항 73
 
2.1%
가 69
 
2.0%
보 68
 
2.0%
Other values (252) 2586
74.3%
Latin
ValueCountFrequency (%)
C 6
 
7.7%
B 6
 
7.7%
o 5
 
6.4%
A 4
 
5.1%
T 4
 
5.1%
P 4
 
5.1%
f 4
 
5.1%
r 3
 
3.8%
n 3
 
3.8%
k 3
 
3.8%
Other values (24) 36
46.2%
Common
ValueCountFrequency (%)
267
77.4%
「 12
 
3.5%
」 12
 
3.5%
1 10
 
2.9%
2 10
 
2.9%
/ 10
 
2.9%
( 7
 
2.0%
) 7
 
2.0%
, 4
 
1.2%
3 3
 
0.9%
Other values (2) 3
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3481
89.2%
ASCII 396
 
10.1%
None 25
 
0.6%
Number Forms 2
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
267
67.4%
1 10
 
2.5%
2 10
 
2.5%
/ 10
 
2.5%
( 7
 
1.8%
) 7
 
1.8%
C 6
 
1.5%
B 6
 
1.5%
o 5
 
1.3%
A 4
 
1.0%
Other values (31) 64
 
16.2%
Hangul
ValueCountFrequency (%)
사 130
 
3.7%
시 124
 
3.6%
험 95
 
2.7%
학 90
 
2.6%
기 85
 
2.4%
의 84
 
2.4%
문 77
 
2.2%
항 73
 
2.1%
가 69
 
2.0%
보 68
 
2.0%
Other values (252) 2586
74.3%
None
ValueCountFrequency (%)
「 12
48.0%
」 12
48.0%
· 1
 
4.0%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
50.0%
Ⅰ 1
50.0%

영어 표기
Text

Distinct593
Distinct (%)92.8%
Missing2
Missing (%)0.3%
Memory size5.1 KiB
2023-12-13T07:02:54.228960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length360
Median length57
Mean length24.510172
Min length3

Characters and Unicode

Total characters15662
Distinct characters68
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

Unique564 ?
Unique (%)88.3%

Sample

1st rowRegulations on Licensed Practical Nurse and Quasi-Medical Personnel
2nd rowproctor
3rd rowproctor training
4th rowproctor's manual
5th rowExamination
ValueCountFrequency (%)
of 57
 
2.9%
examination 54
 
2.8%
test 52
 
2.7%
health 46
 
2.4%
item 46
 
2.4%
medical 40
 
2.1%
and 40
 
2.1%
38
 
2.0%
research 33
 
1.7%
licensing 31
 
1.6%
Other values (634) 1496
77.4%
2023-12-13T07:02:54.713691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1609
 
10.3%
i 1424
 
9.1%
1305
 
8.3%
t 1234
 
7.9%
a 1226
 
7.8%
n 1134
 
7.2%
o 849
 
5.4%
r 839
 
5.4%
c 709
 
4.5%
s 704
 
4.5%
Other values (58) 4629
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13054
83.3%
Space Separator 1305
 
8.3%
Uppercase Letter 1118
 
7.1%
Dash Punctuation 64
 
0.4%
Other Punctuation 48
 
0.3%
Decimal Number 25
 
0.2%
Open Punctuation 21
 
0.1%
Close Punctuation 21
 
0.1%
Letter Number 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1609
12.3%
i 1424
10.9%
t 1234
9.5%
a 1226
9.4%
n 1134
8.7%
o 849
 
6.5%
r 839
 
6.4%
c 709
 
5.4%
s 704
 
5.4%
l 690
 
5.3%
Other values (17) 2636
20.2%
Uppercase Letter
ValueCountFrequency (%)
P 141
12.6%
E 104
9.3%
M 102
9.1%
C 85
 
7.6%
D 80
 
7.2%
L 78
 
7.0%
T 76
 
6.8%
S 75
 
6.7%
H 69
 
6.2%
A 65
 
5.8%
Other values (15) 243
21.7%
Other Punctuation
ValueCountFrequency (%)
/ 15
31.2%
, 13
27.1%
: 11
22.9%
& 6
 
12.5%
' 3
 
6.2%
Decimal Number
ValueCountFrequency (%)
2 10
40.0%
1 9
36.0%
3 4
 
16.0%
5 1
 
4.0%
0 1
 
4.0%
Letter Number
ValueCountFrequency (%)
Ⅰ 3
50.0%
Ⅱ 3
50.0%
Space Separator
ValueCountFrequency (%)
1305
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14177
90.5%
Common 1484
 
9.5%
Greek 1
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1609
11.3%
i 1424
 
10.0%
t 1234
 
8.7%
a 1226
 
8.6%
n 1134
 
8.0%
o 849
 
6.0%
r 839
 
5.9%
c 709
 
5.0%
s 704
 
5.0%
l 690
 
4.9%
Other values (43) 3759
26.5%
Common
ValueCountFrequency (%)
1305
87.9%
- 64
 
4.3%
( 21
 
1.4%
) 21
 
1.4%
/ 15
 
1.0%
, 13
 
0.9%
: 11
 
0.7%
2 10
 
0.7%
1 9
 
0.6%
& 6
 
0.4%
Other values (4) 9
 
0.6%
Greek
ValueCountFrequency (%)
α 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15655
> 99.9%
Number Forms 6
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1609
 
10.3%
i 1424
 
9.1%
1305
 
8.3%
t 1234
 
7.9%
a 1226
 
7.8%
n 1134
 
7.2%
o 849
 
5.4%
r 839
 
5.4%
c 709
 
4.5%
s 704
 
4.5%
Other values (55) 4622
29.5%
Number Forms
ValueCountFrequency (%)
Ⅰ 3
50.0%
Ⅱ 3
50.0%
None
ValueCountFrequency (%)
α 1
100.0%

정의
Text

MISSING 

Distinct358
Distinct (%)100.0%
Missing283
Missing (%)44.1%
Memory size5.1 KiB
2023-12-13T07:02:55.076219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length354
Median length156
Mean length82.963687
Min length9

Characters and Unicode

Total characters29701
Distinct characters586
Distinct categories12 ?
Distinct scripts4 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique358 ?
Unique (%)100.0%

Sample

1st row간호조무사 관련 법령
2nd row시험실 내에서 응시자를 확인하고, 문제지와 답안지를 관리하며, 응시자의 부정행위를 감독하도록 위촉한 인력
3rd row감독관이 시험을 감독하면서 숙지해야 하는 행동 수칙에 대한 교육
4th row감독관이 반드시 숙지해야 하는 행동수칙을 수록한 지침서
5th row환자의 문제해결을 위해 구강 내외부 진찰 및 X-ray를 포함한 기타 방법을 사용하여 병력을 파악하는 과정 (치과의사 실기시험에 한함)
ValueCountFrequency (%)
문항 85
 
1.2%
및 61
 
0.9%
위해 53
 
0.8%
수 50
 
0.7%
또는 48
 
0.7%
하는 33
 
0.5%
대한 31
 
0.4%
등을 31
 
0.4%
문항의 31
 
0.4%
검사 30
 
0.4%
Other values (3456) 6446
93.4%
2023-12-13T07:02:55.612732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6570
 
22.1%
의 663
 
2.2%
하 632
 
2.1%
는 586
 
2.0%
, 468
 
1.6%
을 431
 
1.5%
에 404
 
1.4%
문 367
 
1.2%
가 363
 
1.2%
수 361
 
1.2%
Other values (576) 18856
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21098
71.0%
Space Separator 6570
 
22.1%
Other Punctuation 763
 
2.6%
Lowercase Letter 625
 
2.1%
Decimal Number 156
 
0.5%
Open Punctuation 134
 
0.5%
Close Punctuation 134
 
0.5%
Uppercase Letter 109
 
0.4%
Dash Punctuation 51
 
0.2%
Other Number 25
 
0.1%
Other values (2) 36
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 663
 
3.1%
하 632
 
3.0%
는 586
 
2.8%
을 431
 
2.0%
에 404
 
1.9%
문 367
 
1.7%
가 363
 
1.7%
수 361
 
1.7%
이 360
 
1.7%
로 345
 
1.6%
Other values (485) 16586
78.6%
Lowercase Letter
ValueCountFrequency (%)
o 61
 
9.8%
e 61
 
9.8%
i 49
 
7.8%
a 47
 
7.5%
t 45
 
7.2%
r 44
 
7.0%
n 41
 
6.6%
l 40
 
6.4%
m 33
 
5.3%
c 33
 
5.3%
Other values (23) 171
27.4%
Uppercase Letter
ValueCountFrequency (%)
B 14
12.8%
T 13
11.9%
C 13
11.9%
A 10
9.2%
S 8
 
7.3%
H 8
 
7.3%
D 5
 
4.6%
P 5
 
4.6%
R 5
 
4.6%
X 5
 
4.6%
Other values (10) 23
21.1%
Decimal Number
ValueCountFrequency (%)
1 38
24.4%
0 22
14.1%
6 19
12.2%
5 16
10.3%
9 16
10.3%
2 14
 
9.0%
8 10
 
6.4%
4 9
 
5.8%
7 7
 
4.5%
3 5
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 468
61.3%
. 170
 
22.3%
' 46
 
6.0%
: 26
 
3.4%
* 18
 
2.4%
/ 16
 
2.1%
※ 10
 
1.3%
% 5
 
0.7%
· 4
 
0.5%
Other Number
ValueCountFrequency (%)
② 6
24.0%
① 6
24.0%
² 4
16.0%
④ 3
12.0%
③ 3
12.0%
⑤ 3
12.0%
Math Symbol
ValueCountFrequency (%)
← 6
33.3%
~ 4
22.2%
+ 3
16.7%
↓ 3
16.7%
= 2
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 130
97.0%
「 4
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 130
97.0%
」 4
 
3.0%
Other Symbol
ValueCountFrequency (%)
☞ 15
83.3%
□ 3
 
16.7%
Space Separator
ValueCountFrequency (%)
6570
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21098
71.0%
Common 7869
 
26.5%
Latin 722
 
2.4%
Greek 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 663
 
3.1%
하 632
 
3.0%
는 586
 
2.8%
을 431
 
2.0%
에 404
 
1.9%
문 367
 
1.7%
가 363
 
1.7%
수 361
 
1.7%
이 360
 
1.7%
로 345
 
1.6%
Other values (485) 16586
78.6%
Latin
ValueCountFrequency (%)
o 61
 
8.4%
e 61
 
8.4%
i 49
 
6.8%
a 47
 
6.5%
t 45
 
6.2%
r 44
 
6.1%
n 41
 
5.7%
l 40
 
5.5%
m 33
 
4.6%
c 33
 
4.6%
Other values (36) 268
37.1%
Common
ValueCountFrequency (%)
6570
83.5%
, 468
 
5.9%
. 170
 
2.2%
( 130
 
1.7%
) 130
 
1.7%
- 51
 
0.6%
' 46
 
0.6%
1 38
 
0.5%
: 26
 
0.3%
0 22
 
0.3%
Other values (28) 218
 
2.8%
Greek
ValueCountFrequency (%)
θ 3
25.0%
α 3
25.0%
β 2
16.7%
γ 1
 
8.3%
ρ 1
 
8.3%
σ 1
 
8.3%
μ 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21098
71.0%
ASCII 8517
28.7%
None 28
 
0.1%
Enclosed Alphanum 21
 
0.1%
Misc Symbols 15
 
0.1%
Punctuation 10
 
< 0.1%
Arrows 9
 
< 0.1%
Geometric Shapes 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6570
77.1%
, 468
 
5.5%
. 170
 
2.0%
( 130
 
1.5%
) 130
 
1.5%
o 61
 
0.7%
e 61
 
0.7%
- 51
 
0.6%
i 49
 
0.6%
a 47
 
0.6%
Other values (60) 780
 
9.2%
Hangul
ValueCountFrequency (%)
의 663
 
3.1%
하 632
 
3.0%
는 586
 
2.8%
을 431
 
2.0%
에 404
 
1.9%
문 367
 
1.7%
가 363
 
1.7%
수 361
 
1.7%
이 360
 
1.7%
로 345
 
1.6%
Other values (485) 16586
78.6%
Misc Symbols
ValueCountFrequency (%)
☞ 15
100.0%
Punctuation
ValueCountFrequency (%)
※ 10
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
② 6
28.6%
① 6
28.6%
④ 3
14.3%
③ 3
14.3%
⑤ 3
14.3%
Arrows
ValueCountFrequency (%)
← 6
66.7%
↓ 3
33.3%
None
ValueCountFrequency (%)
² 4
14.3%
」 4
14.3%
· 4
14.3%
「 4
14.3%
θ 3
10.7%
α 3
10.7%
β 2
7.1%
γ 1
 
3.6%
ρ 1
 
3.6%
σ 1
 
3.6%
Geometric Shapes
ValueCountFrequency (%)
□ 3
100.0%

Missing values

2023-12-13T07:02:52.572473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:02:52.684213image/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.
2023-12-13T07:02:52.779823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

대분류용어영어 표기정의
0시험「간호조무사 및 의료유사업자에 관한 규칙」Regulations on Licensed Practical Nurse and Quasi-Medical Personnel간호조무사 관련 법령
1시험감독관proctor시험실 내에서 응시자를 확인하고, 문제지와 답안지를 관리하며, 응시자의 부정행위를 감독하도록 위촉한 인력
2시험감독관 교육proctor training감독관이 시험을 감독하면서 숙지해야 하는 행동 수칙에 대한 교육
3시험감독관 근무지침proctor's manual감독관이 반드시 숙지해야 하는 행동수칙을 수록한 지침서
4시험검사Examination환자의 문제해결을 위해 구강 내외부 진찰 및 X-ray를 포함한 기타 방법을 사용하여 병력을 파악하는 과정 (치과의사 실기시험에 한함)
5시험결과평가Outcome EvaluationSimulator mannequin을 활용한 수기능력평가 (치과의사 실기시험에 한함)
6시험결격기준ineligibility criteria응시자가 해당 시험에 응시할 수 없는 기준 또는 면허를 받을 수 없는 기준(해당 시험의 근거 법령에 명시되어 있음)
7시험「공중위생관리법」Public Health Control Act위생사 관련 법령
8시험과락과목failed subject성적이 합격기준에 못미치는 과목으로서, 특정과목에서 40점 미만인 경우에 과락이 됨. 과락과목이 발생하면 총점에 관계없이 불합격이 됨
9시험과정평가Process Evaluation표준화환자, 모의환자, 장비 등을 활용한 진료수행 및 기본 술기능력 평가(치과의사 실기시험에 한함)
대분류용어영어 표기정의
631직종별 국가시험 및 과목한국어Proficiency in Korean<NA>
632직종별 국가시험 및 과목약사 예비시험Preliminary examination for pharmacist<NA>
633직종별 국가시험 및 과목약학기초Essential pharmacy<NA>
634직종별 국가시험 및 과목한국어Proficiency in Korean<NA>
635직종별 국가시험 및 과목치과의사 예비시험Preliminary examination for DLE<NA>
636직종별 국가시험 및 과목치의학의 기초Essential Dental Science<NA>
637직종별 국가시험 및 과목한국어Proficiency in Korean<NA>
638직종별 국가시험 및 과목한의사 예비시험Preliminary examination for OMDLE<NA>
639직종별 국가시험 및 과목한국어Proficiency in Korean<NA>
640직종별 국가시험 및 과목특례시험Exceptionally-Certified Examination<NA>

Duplicate rows

Most frequently occurring

대분류용어영어 표기정의# duplicates
15직종별 국가시험 및 과목의료관계법규Medical Service Legislation<NA>7
4직종별 국가시험 및 과목보건의약관계법규Medical Health Legislation<NA>5
9직종별 국가시험 및 과목실기시험Practical Skill (written exam)<NA>5
10직종별 국가시험 및 과목실기시험Practical Skill(written exam)<NA>4
23직종별 국가시험 및 과목한국어Proficiency in Korean<NA>4
5직종별 국가시험 및 과목보건프로그램개발 및 평가Health Program Planning and Evaluation<NA>3
0직종별 국가시험 및 과목보건교육방법론Health Education Methods<NA>2
1직종별 국가시험 및 과목보건교육학Introduction to Health Education and Promotion<NA>2
2직종별 국가시험 및 과목보건사업관리Health Program Management<NA>2
3직종별 국가시험 및 과목보건의료법규Health and Medical Law<NA>2