Overview

Dataset statistics

Number of variables17
Number of observations51
Missing cells156
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory138.6 B

Variable types

Categorical11
Text6

Dataset

Description국외인적자원관리시스템 기업전용서비스로 정부초청장학생을 대상으로 하는 직원 채용 시 희망채용조건을 등록하는 메뉴(현재 서비스 미오픈, 오픈 시기 미정)
Author교육부 국립국제교육원
URLhttps://www.data.go.kr/data/15069779/fileData.do

Alerts

근무국가1 is highly overall correlated with 2차선택(1차업종) and 4 other fieldsHigh correlation
지역1 is highly overall correlated with 근무국가1 and 1 other fieldsHigh correlation
3차선택(1차업종) is highly overall correlated with 1차선택(1차업종) and 3 other fieldsHigh correlation
2차선택(1차업종) is highly overall correlated with 1차선택(1차업종) and 2 other fieldsHigh correlation
근무국가2 is highly overall correlated with 3차선택(1차업종) and 1 other fieldsHigh correlation
고용형태아르바이트 is highly overall correlated with 근무국가1 and 2 other fieldsHigh correlation
고용형태프리랜서 is highly overall correlated with 근무국가1 and 3 other fieldsHigh correlation
고용형태정규직 is highly overall correlated with 2차선택(1차업종) and 7 other fieldsHigh correlation
1차선택(1차업종) is highly overall correlated with 1차선택(2차업종) and 2 other fieldsHigh correlation
1차선택(2차업종) is highly overall correlated with 1차선택(1차업종) and 1 other fieldsHigh correlation
회사규모 is highly overall correlated with 고용형태정규직 and 1 other fieldsHigh correlation
1차선택(1차업종) is highly imbalanced (56.1%)Imbalance
고용형태정규직 is highly imbalanced (86.1%)Imbalance
2차선택(2차업종) has 12 (23.5%) missing valuesMissing
3선택(2차업종) has 20 (39.2%) missing valuesMissing
지역2 has 33 (64.7%) missing valuesMissing
근무국가3 has 19 (37.3%) missing valuesMissing
지역3 has 38 (74.5%) missing valuesMissing
급여수준(만원) has 34 (66.7%) missing valuesMissing

Reproduction

Analysis started2023-12-12 17:40:18.686617
Analysis finished2023-12-12 17:40:20.840294
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1차선택(1차업종)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
제조/건설/기계/전기/섬유/제약
44 
미디어/방송/신문/출판/광고
5 
인터넷/IT/정보통신
 
2

Length

Max length17
Median length17
Mean length16.568627
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미디어/방송/신문/출판/광고
2nd row제조/건설/기계/전기/섬유/제약
3rd row제조/건설/기계/전기/섬유/제약
4th row제조/건설/기계/전기/섬유/제약
5th row제조/건설/기계/전기/섬유/제약

Common Values

ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 44
86.3%
미디어/방송/신문/출판/광고 5
 
9.8%
인터넷/IT/정보통신 2
 
3.9%

Length

2023-12-13T02:40:20.920884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:21.028264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 44
86.3%
미디어/방송/신문/출판/광고 5
 
9.8%
인터넷/it/정보통신 2
 
3.9%

1차선택(2차업종)
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)35.3%
Missing0
Missing (%)0.0%
Memory size540.0 B
공공기관/공사/공기업/협회/(대)학교
11 
연구/조사/리서치/컨설팅
9 
무역/상사
6 
연예/엔터테인먼트/문화/공연
4 
특허/법률/세뮤/회계/노무
4 
Other values (13)
17 

Length

Max length20
Median length14
Mean length13.098039
Min length5

Unique

Unique10 ?
Unique (%)19.6%

Sample

1st row연예/엔터테인먼트/문화/공연
2nd row공공기관/공사/공기업/협회/(대)학교
3rd row공공기관/공사/공기업/협회/(대)학교
4th row건축/설비/설계/감리/ENG
5th row연구/조사/리서치/컨설팅

Common Values

ValueCountFrequency (%)
공공기관/공사/공기업/협회/(대)학교 11
21.6%
연구/조사/리서치/컨설팅 9
17.6%
무역/상사 6
11.8%
연예/엔터테인먼트/문화/공연 4
 
7.8%
특허/법률/세뮤/회계/노무 4
 
7.8%
건설/토목/시공/조경 3
 
5.9%
의료/제약/바이오 2
 
3.9%
금융(은행/증권/보험/카드) 2
 
3.9%
석유/화학/에너지/환경 1
 
2.0%
전기/전자/반도체/광학 1
 
2.0%
Other values (8) 8
15.7%

Length

2023-12-13T02:40:21.160016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공공기관/공사/공기업/협회/(대)학교 11
21.6%
연구/조사/리서치/컨설팅 9
17.6%
무역/상사 6
11.8%
연예/엔터테인먼트/문화/공연 4
 
7.8%
특허/법률/세뮤/회계/노무 4
 
7.8%
건설/토목/시공/조경 3
 
5.9%
의료/제약/바이오 2
 
3.9%
금융(은행/증권/보험/카드 2
 
3.9%
기계/자동차/조선/항공 1
 
2.0%
금속/철강/재료 1
 
2.0%
Other values (8) 8
15.7%

2차선택(1차업종)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
제조/건설/기계/전기/섬유/제약
30 
<NA>
12 
미디어/방송/신문/출판/광고
6 
인터넷/IT/정보통신
 
3

Length

Max length17
Median length17
Mean length13.352941
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row미디어/방송/신문/출판/광고
4th row<NA>
5th row제조/건설/기계/전기/섬유/제약

Common Values

ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 30
58.8%
<NA> 12
 
23.5%
미디어/방송/신문/출판/광고 6
 
11.8%
인터넷/IT/정보통신 3
 
5.9%

Length

2023-12-13T02:40:21.613001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:21.736424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 30
58.8%
na 12
 
23.5%
미디어/방송/신문/출판/광고 6
 
11.8%
인터넷/it/정보통신 3
 
5.9%

2차선택(2차업종)
Text

MISSING 

Distinct21
Distinct (%)53.8%
Missing12
Missing (%)23.5%
Memory size540.0 B
2023-12-13T02:40:21.971277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length13
Mean length13.051282
Min length5

Characters and Unicode

Total characters509
Distinct characters133
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)28.2%

Sample

1st row신문/잡지/출판/인쇄
2nd row교육/유학/학원/학습지
3rd row연구/조사/리서치/컨설팅
4th row공공기관/공사/공기업/협회/(대)학교
5th rowSI/SM/ERP/CRM
ValueCountFrequency (%)
공공기관/공사/공기업/협회/(대)학교 7
17.9%
무역/상사 4
 
10.3%
방송/영상/케이블/프로덕션 3
 
7.7%
의료/건강/보건/복지 2
 
5.1%
연구/조사/리서치/컨설팅 2
 
5.1%
석유/화학/에너지/환경 2
 
5.1%
특허/법률/세뮤/회계/노무 2
 
5.1%
신문/잡지/출판/인쇄 2
 
5.1%
전기/전자/반도체/광학 2
 
5.1%
건축/설비/설계/감리/eng 2
 
5.1%
Other values (11) 11
28.2%
2023-12-13T02:40:22.401060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 116
22.8%
공 31
 
6.1%
기 17
 
3.3%
학 14
 
2.8%
사 13
 
2.6%
회 9
 
1.8%
( 9
 
1.8%
) 9
 
1.8%
교 8
 
1.6%
관 8
 
1.6%
Other values (123) 275
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 359
70.5%
Other Punctuation 116
 
22.8%
Uppercase Letter 16
 
3.1%
Open Punctuation 9
 
1.8%
Close Punctuation 9
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 31
 
8.6%
기 17
 
4.7%
학 14
 
3.9%
사 13
 
3.6%
회 9
 
2.5%
교 8
 
2.2%
관 8
 
2.2%
업 8
 
2.2%
상 7
 
1.9%
무 7
 
1.9%
Other values (111) 237
66.0%
Uppercase Letter
ValueCountFrequency (%)
E 3
18.8%
M 2
12.5%
N 2
12.5%
G 2
12.5%
S 2
12.5%
R 2
12.5%
I 1
 
6.2%
P 1
 
6.2%
C 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 116
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 359
70.5%
Common 134
 
26.3%
Latin 16
 
3.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 31
 
8.6%
기 17
 
4.7%
학 14
 
3.9%
사 13
 
3.6%
회 9
 
2.5%
교 8
 
2.2%
관 8
 
2.2%
업 8
 
2.2%
상 7
 
1.9%
무 7
 
1.9%
Other values (111) 237
66.0%
Latin
ValueCountFrequency (%)
E 3
18.8%
M 2
12.5%
N 2
12.5%
G 2
12.5%
S 2
12.5%
R 2
12.5%
I 1
 
6.2%
P 1
 
6.2%
C 1
 
6.2%
Common
ValueCountFrequency (%)
/ 116
86.6%
( 9
 
6.7%
) 9
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 359
70.5%
ASCII 150
29.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 116
77.3%
( 9
 
6.0%
) 9
 
6.0%
E 3
 
2.0%
M 2
 
1.3%
N 2
 
1.3%
G 2
 
1.3%
S 2
 
1.3%
R 2
 
1.3%
I 1
 
0.7%
Other values (2) 2
 
1.3%
Hangul
ValueCountFrequency (%)
공 31
 
8.6%
기 17
 
4.7%
학 14
 
3.9%
사 13
 
3.6%
회 9
 
2.5%
교 8
 
2.2%
관 8
 
2.2%
업 8
 
2.2%
상 7
 
1.9%
무 7
 
1.9%
Other values (111) 237
66.0%

3차선택(1차업종)
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
제조/건설/기계/전기/섬유/제약
25 
<NA>
20 
미디어/방송/신문/출판/광고
4 
인터넷/IT/정보통신
 
2

Length

Max length17
Median length15
Mean length11.509804
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row제조/건설/기계/전기/섬유/제약

Common Values

ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 25
49.0%
<NA> 20
39.2%
미디어/방송/신문/출판/광고 4
 
7.8%
인터넷/IT/정보통신 2
 
3.9%

Length

2023-12-13T02:40:22.582090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:22.709076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조/건설/기계/전기/섬유/제약 25
49.0%
na 20
39.2%
미디어/방송/신문/출판/광고 4
 
7.8%
인터넷/it/정보통신 2
 
3.9%

3선택(2차업종)
Text

MISSING 

Distinct20
Distinct (%)64.5%
Missing20
Missing (%)39.2%
Memory size540.0 B
2023-12-13T02:40:22.954501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length12.096774
Min length5

Characters and Unicode

Total characters375
Distinct characters119
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)38.7%

Sample

1st row호텔/여행/관광/항공
2nd row외식/프랜차이즈
3rd rowSI/SM/ERP/CRM
4th row컴퓨터/하드웨어/시스템
5th row연구/조사/리서치/컨설팅
ValueCountFrequency (%)
연구/조사/리서치/컨설팅 3
 
9.7%
의료/제약/바이오 3
 
9.7%
특허/법률/세뮤/회계/노무 3
 
9.7%
기계/자동차/조선/항공 2
 
6.5%
전기/전자/반도체/광학 2
 
6.5%
금융(은행/증권/보험/카드 2
 
6.5%
호텔/여행/관광/항공 2
 
6.5%
연예/엔터테인먼트/문화/공연 2
 
6.5%
공공기관/공사/공기업/협회/(대)학교 1
 
3.2%
무역/상사 1
 
3.2%
Other values (10) 10
32.3%
2023-12-13T02:40:23.345151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 88
 
23.5%
공 11
 
2.9%
연 7
 
1.9%
기 6
 
1.6%
설 6
 
1.6%
조 6
 
1.6%
계 6
 
1.6%
전 5
 
1.3%
광 5
 
1.3%
무 5
 
1.3%
Other values (109) 230
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 268
71.5%
Other Punctuation 88
 
23.5%
Uppercase Letter 13
 
3.5%
Close Punctuation 3
 
0.8%
Open Punctuation 3
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 11
 
4.1%
연 7
 
2.6%
기 6
 
2.2%
설 6
 
2.2%
조 6
 
2.2%
계 6
 
2.2%
전 5
 
1.9%
광 5
 
1.9%
무 5
 
1.9%
사 5
 
1.9%
Other values (97) 206
76.9%
Uppercase Letter
ValueCountFrequency (%)
S 2
15.4%
M 2
15.4%
R 2
15.4%
E 2
15.4%
N 1
7.7%
G 1
7.7%
I 1
7.7%
P 1
7.7%
C 1
7.7%
Other Punctuation
ValueCountFrequency (%)
/ 88
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 268
71.5%
Common 94
 
25.1%
Latin 13
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 11
 
4.1%
연 7
 
2.6%
기 6
 
2.2%
설 6
 
2.2%
조 6
 
2.2%
계 6
 
2.2%
전 5
 
1.9%
광 5
 
1.9%
무 5
 
1.9%
사 5
 
1.9%
Other values (97) 206
76.9%
Latin
ValueCountFrequency (%)
S 2
15.4%
M 2
15.4%
R 2
15.4%
E 2
15.4%
N 1
7.7%
G 1
7.7%
I 1
7.7%
P 1
7.7%
C 1
7.7%
Common
ValueCountFrequency (%)
/ 88
93.6%
) 3
 
3.2%
( 3
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 268
71.5%
ASCII 107
 
28.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 88
82.2%
) 3
 
2.8%
( 3
 
2.8%
S 2
 
1.9%
M 2
 
1.9%
R 2
 
1.9%
E 2
 
1.9%
N 1
 
0.9%
G 1
 
0.9%
I 1
 
0.9%
Other values (2) 2
 
1.9%
Hangul
ValueCountFrequency (%)
공 11
 
4.1%
연 7
 
2.6%
기 6
 
2.2%
설 6
 
2.2%
조 6
 
2.2%
계 6
 
2.2%
전 5
 
1.9%
광 5
 
1.9%
무 5
 
1.9%
사 5
 
1.9%
Other values (97) 206
76.9%

근무국가1
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)37.3%
Missing0
Missing (%)0.0%
Memory size540.0 B
한국
30 
말레이지아
 
2
일본
 
2
중국
 
2
가나
 
1
Other values (14)
14 

Length

Max length6
Median length2
Mean length2.5294118
Min length2

Unique

Unique15 ?
Unique (%)29.4%

Sample

1st row중국
2nd row미국
3rd row중국
4th row말레이지아
5th row한국

Common Values

ValueCountFrequency (%)
한국 30
58.8%
말레이지아 2
 
3.9%
일본 2
 
3.9%
중국 2
 
3.9%
가나 1
 
2.0%
스리랑카 1
 
2.0%
프랑스 1
 
2.0%
우즈베키스탄 1
 
2.0%
미얀마 1
 
2.0%
말라위 1
 
2.0%
Other values (9) 9
 
17.6%

Length

2023-12-13T02:40:23.489802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한국 30
58.8%
일본 2
 
3.9%
중국 2
 
3.9%
말레이지아 2
 
3.9%
세네갈 1
 
2.0%
캄보디아 1
 
2.0%
몽골 1
 
2.0%
싱가포르 1
 
2.0%
카자흐스탄 1
 
2.0%
스위스 1
 
2.0%
Other values (9) 9
 
17.6%

지역1
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
서울
26 
<NA>
22 
대전
 
1
충남
 
1
충북
 
1

Length

Max length4
Median length2
Mean length2.8627451
Min length2

Unique

Unique3 ?
Unique (%)5.9%

Sample

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

Common Values

ValueCountFrequency (%)
서울 26
51.0%
<NA> 22
43.1%
대전 1
 
2.0%
충남 1
 
2.0%
충북 1
 
2.0%

Length

2023-12-13T02:40:23.666128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:23.803761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 26
51.0%
na 22
43.1%
대전 1
 
2.0%
충남 1
 
2.0%
충북 1
 
2.0%

근무국가2
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size540.0 B
한국
22 
<NA>
10 
미국
3 
영국
 
2
싱가포르
 
2
Other values (9)
12 

Length

Max length6
Median length2
Mean length2.7058824
Min length2

Unique

Unique6 ?
Unique (%)11.8%

Sample

1st row한국
2nd row영국
3rd row홍콩
4th row싱가포르
5th row<NA>

Common Values

ValueCountFrequency (%)
한국 22
43.1%
<NA> 10
19.6%
미국 3
 
5.9%
영국 2
 
3.9%
싱가포르 2
 
3.9%
몽골 2
 
3.9%
대만 2
 
3.9%
일본 2
 
3.9%
홍콩 1
 
2.0%
아랍에미리트 1
 
2.0%
Other values (4) 4
 
7.8%

Length

2023-12-13T02:40:23.953862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
한국 22
43.1%
na 10
19.6%
미국 3
 
5.9%
영국 2
 
3.9%
싱가포르 2
 
3.9%
몽골 2
 
3.9%
대만 2
 
3.9%
일본 2
 
3.9%
홍콩 1
 
2.0%
아랍에미리트 1
 
2.0%
Other values (4) 4
 
7.8%

지역2
Text

MISSING 

Distinct9
Distinct (%)50.0%
Missing33
Missing (%)64.7%
Memory size540.0 B
2023-12-13T02:40:24.106384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters36
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)27.8%

Sample

1st row부산
2nd row인천
3rd row경기
4th row전북
5th row부산
ValueCountFrequency (%)
부산 5
27.8%
인천 4
22.2%
대구 2
 
11.1%
서울 2
 
11.1%
경기 1
 
5.6%
전북 1
 
5.6%
충남 1
 
5.6%
대전 1
 
5.6%
경남 1
 
5.6%
2023-12-13T02:40:24.417700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
부 5
13.9%
산 5
13.9%
인 4
11.1%
천 4
11.1%
대 3
8.3%
구 2
 
5.6%
서 2
 
5.6%
울 2
 
5.6%
경 2
 
5.6%
전 2
 
5.6%
Other values (4) 5
13.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 5
13.9%
산 5
13.9%
인 4
11.1%
천 4
11.1%
대 3
8.3%
구 2
 
5.6%
서 2
 
5.6%
울 2
 
5.6%
경 2
 
5.6%
전 2
 
5.6%
Other values (4) 5
13.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 5
13.9%
산 5
13.9%
인 4
11.1%
천 4
11.1%
대 3
8.3%
구 2
 
5.6%
서 2
 
5.6%
울 2
 
5.6%
경 2
 
5.6%
전 2
 
5.6%
Other values (4) 5
13.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
부 5
13.9%
산 5
13.9%
인 4
11.1%
천 4
11.1%
대 3
8.3%
구 2
 
5.6%
서 2
 
5.6%
울 2
 
5.6%
경 2
 
5.6%
전 2
 
5.6%
Other values (4) 5
13.9%

근무국가3
Text

MISSING 

Distinct16
Distinct (%)50.0%
Missing19
Missing (%)37.3%
Memory size540.0 B
2023-12-13T02:40:24.570658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.96875
Min length2

Characters and Unicode

Total characters95
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)40.6%

Sample

1st row독일
2nd row중국
3rd row한국
4th row한국
5th row요르단
ValueCountFrequency (%)
한국 15
46.9%
오스트레일리아 2
 
6.2%
미국 2
 
6.2%
사우디아라비아 1
 
3.1%
중국 1
 
3.1%
요르단 1
 
3.1%
우크라이나 1
 
3.1%
우즈베키스탄 1
 
3.1%
독일 1
 
3.1%
기니비사우 1
 
3.1%
Other values (6) 6
 
18.8%
2023-12-13T02:40:24.835262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 19
20.0%
한 15
15.8%
우 4
 
4.2%
스 4
 
4.2%
리 4
 
4.2%
아 4
 
4.2%
일 3
 
3.2%
나 2
 
2.1%
르 2
 
2.1%
비 2
 
2.1%
Other values (30) 36
37.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 95
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 19
20.0%
한 15
15.8%
우 4
 
4.2%
스 4
 
4.2%
리 4
 
4.2%
아 4
 
4.2%
일 3
 
3.2%
나 2
 
2.1%
르 2
 
2.1%
비 2
 
2.1%
Other values (30) 36
37.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 19
20.0%
한 15
15.8%
우 4
 
4.2%
스 4
 
4.2%
리 4
 
4.2%
아 4
 
4.2%
일 3
 
3.2%
나 2
 
2.1%
르 2
 
2.1%
비 2
 
2.1%
Other values (30) 36
37.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 95
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 19
20.0%
한 15
15.8%
우 4
 
4.2%
스 4
 
4.2%
리 4
 
4.2%
아 4
 
4.2%
일 3
 
3.2%
나 2
 
2.1%
르 2
 
2.1%
비 2
 
2.1%
Other values (30) 36
37.9%

지역3
Text

MISSING 

Distinct7
Distinct (%)53.8%
Missing38
Missing (%)74.5%
Memory size540.0 B
2023-12-13T02:40:24.986528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters26
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)30.8%

Sample

1st row서울
2nd row제주
3rd row부산
4th row서울
5th row울산
ValueCountFrequency (%)
부산 4
30.8%
서울 3
23.1%
경기 2
15.4%
제주 1
 
7.7%
울산 1
 
7.7%
대전 1
 
7.7%
충북 1
 
7.7%
2023-12-13T02:40:25.282499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 5
19.2%
부 4
15.4%
울 4
15.4%
서 3
11.5%
경 2
 
7.7%
기 2
 
7.7%
제 1
 
3.8%
주 1
 
3.8%
대 1
 
3.8%
전 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 5
19.2%
부 4
15.4%
울 4
15.4%
서 3
11.5%
경 2
 
7.7%
기 2
 
7.7%
제 1
 
3.8%
주 1
 
3.8%
대 1
 
3.8%
전 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 5
19.2%
부 4
15.4%
울 4
15.4%
서 3
11.5%
경 2
 
7.7%
기 2
 
7.7%
제 1
 
3.8%
주 1
 
3.8%
대 1
 
3.8%
전 1
 
3.8%
Other values (2) 2
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 5
19.2%
부 4
15.4%
울 4
15.4%
서 3
11.5%
경 2
 
7.7%
기 2
 
7.7%
제 1
 
3.8%
주 1
 
3.8%
대 1
 
3.8%
전 1
 
3.8%
Other values (2) 2
 
7.7%

고용형태정규직
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Memory size540.0 B
정규직
50 
아르바이트
 
1

Length

Max length5
Median length3
Mean length3.0392157
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
정규직 50
98.0%
아르바이트 1
 
2.0%

Length

2023-12-13T02:40:25.419262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:25.526227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정규직 50
98.0%
아르바이트 1
 
2.0%

고용형태프리랜서
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
<NA>
21 
아르바이트
20 
인턴직
4 
프리랜서
3 
계약직
3 

Length

Max length5
Median length4
Mean length4.254902
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 21
41.2%
아르바이트 20
39.2%
인턴직 4
 
7.8%
프리랜서 3
 
5.9%
계약직 3
 
5.9%

Length

2023-12-13T02:40:25.664852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:25.796889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
41.2%
아르바이트 20
39.2%
인턴직 4
 
7.8%
프리랜서 3
 
5.9%
계약직 3
 
5.9%

고용형태아르바이트
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size540.0 B
<NA>
33 
계약직
14 
아르바이트
 
3
인턴직
 
1

Length

Max length5
Median length4
Mean length3.7647059
Min length3

Unique

Unique1 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 33
64.7%
계약직 14
27.5%
아르바이트 3
 
5.9%
인턴직 1
 
2.0%

Length

2023-12-13T02:40:25.948107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:26.077444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 33
64.7%
계약직 14
27.5%
아르바이트 3
 
5.9%
인턴직 1
 
2.0%

급여수준(만원)
Text

MISSING 

Distinct12
Distinct (%)70.6%
Missing34
Missing (%)66.7%
Memory size540.0 B
2023-12-13T02:40:26.262179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.4117647
Min length4

Characters and Unicode

Total characters109
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)52.9%

Sample

1st row480~500
2nd row300~320
3rd row50이하
4th row260~280
5th row160~180
ValueCountFrequency (%)
50이하 3
17.6%
200~220 3
17.6%
280~300 2
11.8%
480~500 1
 
5.9%
300~320 1
 
5.9%
260~280 1
 
5.9%
160~180 1
 
5.9%
1000이상 1
 
5.9%
420~440 1
 
5.9%
320~340 1
 
5.9%
Other values (2) 2
11.8%
2023-12-13T02:40:26.664312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
35.8%
2 21
19.3%
~ 13
 
11.9%
4 7
 
6.4%
3 6
 
5.5%
8 5
 
4.6%
5 4
 
3.7%
이 4
 
3.7%
하 3
 
2.8%
6 3
 
2.8%
Other values (2) 4
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
80.7%
Math Symbol 13
 
11.9%
Other Letter 8
 
7.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
44.3%
2 21
23.9%
4 7
 
8.0%
3 6
 
6.8%
8 5
 
5.7%
5 4
 
4.5%
6 3
 
3.4%
1 3
 
3.4%
Other Letter
ValueCountFrequency (%)
이 4
50.0%
하 3
37.5%
상 1
 
12.5%
Math Symbol
ValueCountFrequency (%)
~ 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101
92.7%
Hangul 8
 
7.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39
38.6%
2 21
20.8%
~ 13
 
12.9%
4 7
 
6.9%
3 6
 
5.9%
8 5
 
5.0%
5 4
 
4.0%
6 3
 
3.0%
1 3
 
3.0%
Hangul
ValueCountFrequency (%)
이 4
50.0%
하 3
37.5%
상 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
92.7%
Hangul 8
 
7.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
38.6%
2 21
20.8%
~ 13
 
12.9%
4 7
 
6.9%
3 6
 
5.9%
8 5
 
5.0%
5 4
 
4.0%
6 3
 
3.0%
1 3
 
3.0%
Hangul
ValueCountFrequency (%)
이 4
50.0%
하 3
37.5%
상 1
 
12.5%

회사규모
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Memory size540.0 B
기업규모무관
14 
<NA>
6 
대기업(1000명 이상)
6 
비영리단체,협회,재단
6 
일반기업
4 
Other values (5)
15 

Length

Max length13
Median length11
Mean length8.4117647
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
기업규모무관 14
27.5%
<NA> 6
11.8%
대기업(1000명 이상) 6
11.8%
비영리단체,협회,재단 6
11.8%
일반기업 4
 
7.8%
중소기업(300명 미만) 4
 
7.8%
중견기업(300명 이상) 4
 
7.8%
외국계투자기업 3
 
5.9%
외국계법인기업 2
 
3.9%
공기업,공공기관,공사 2
 
3.9%

Length

2023-12-13T02:40:26.839388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:40:27.009290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
기업규모무관 14
21.5%
이상 10
15.4%
na 6
9.2%
대기업(1000명 6
9.2%
비영리단체,협회,재단 6
9.2%
일반기업 4
 
6.2%
중소기업(300명 4
 
6.2%
미만 4
 
6.2%
중견기업(300명 4
 
6.2%
외국계투자기업 3
 
4.6%
Other values (2) 4
 
6.2%

Correlations

2023-12-13T02:40:27.154858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차선택(1차업종)1차선택(2차업종)2차선택(1차업종)2차선택(2차업종)3차선택(1차업종)3선택(2차업종)근무국가1지역1근무국가2지역2근무국가3지역3고용형태정규직고용형태프리랜서고용형태아르바이트급여수준(만원)회사규모
1차선택(1차업종)1.0001.0000.9250.9640.8561.0000.5380.0000.423NaN0.693NaN0.0000.0000.0001.0000.000
1차선택(2차업종)1.0001.0000.7800.7920.8830.9050.0000.8510.6360.0000.5980.7210.0000.0000.0000.0000.000
2차선택(1차업종)0.9250.7801.0001.0000.7250.8890.9310.0000.3760.0000.000NaNNaN0.0000.0000.0000.354
2차선택(2차업종)0.9640.7921.0001.0000.6780.8620.9040.4690.7160.0000.0000.928NaN0.8441.0000.0000.604
3차선택(1차업종)0.8560.8830.7250.6781.0001.0000.3980.0000.9071.0000.0000.000NaN0.0000.0001.0000.000
3선택(2차업종)1.0000.9050.8890.8621.0001.0000.9170.9240.8280.6140.8570.000NaN0.8730.4340.9480.803
근무국가10.5380.0000.9310.9040.3980.9171.000NaN0.4440.0000.6460.0001.0000.8380.9780.7910.696
지역10.0000.8510.0000.4690.0000.924NaN1.0000.0001.0000.0000.715NaN0.0000.0001.0000.000
근무국가20.4230.6360.3760.7160.9070.8280.4440.0001.000NaN0.7910.000NaN0.0000.0000.7180.000
지역2NaN0.0000.0000.0001.0000.6140.0001.000NaN1.0000.0000.000NaN0.0000.0000.8910.000
근무국가30.6930.5980.0000.0000.0000.8570.6460.0000.7910.0001.000NaNNaN0.2430.1780.8620.692
지역3NaN0.721NaN0.9280.0000.0000.0000.7150.0000.000NaN1.000NaN0.6881.0000.9130.687
고용형태정규직0.0000.000NaNNaNNaNNaN1.000NaNNaNNaNNaNNaN1.000NaNNaNNaN0.610
고용형태프리랜서0.0000.0000.0000.8440.0000.8730.8380.0000.0000.0000.2430.688NaN1.0000.9350.6070.796
고용형태아르바이트0.0000.0000.0001.0000.0000.4340.9780.0000.0000.0000.1781.000NaN0.9351.0000.0000.313
급여수준(만원)1.0000.0000.0000.0001.0000.9480.7911.0000.7180.8910.8620.913NaN0.6070.0001.0000.000
회사규모0.0000.0000.3540.6040.0000.8030.6960.0000.0000.0000.6920.6870.6100.7960.3130.0001.000
2023-12-13T02:40:27.332050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
근무국가1회사규모1차선택(1차업종)지역11차선택(2차업종)3차선택(1차업종)2차선택(1차업종)근무국가2고용형태아르바이트고용형태프리랜서고용형태정규직
근무국가11.0000.3080.2651.0000.0000.1180.5800.1390.6270.5340.808
회사규모0.3081.0000.0000.0000.0000.0000.2070.0000.1050.6430.560
1차선택(1차업종)0.2650.0001.0000.0000.8290.5410.6650.3250.0000.0000.000
지역11.0000.0000.0001.0000.4420.0000.0000.0000.0000.0001.000
1차선택(2차업종)0.0000.0000.8290.4421.0000.5290.4640.2460.0000.0000.000
3차선택(1차업종)0.1180.0000.5410.0000.5291.0000.3790.5500.0000.0001.000
2차선택(1차업종)0.5800.2070.6650.0000.4640.3791.0000.1900.0000.0001.000
근무국가20.1390.0000.3250.0000.2460.5500.1901.0000.0000.0001.000
고용형태아르바이트0.6270.1050.0000.0000.0000.0000.0000.0001.0000.6851.000
고용형태프리랜서0.5340.6430.0000.0000.0000.0000.0000.0000.6851.0001.000
고용형태정규직0.8080.5600.0001.0000.0001.0001.0001.0001.0001.0001.000
2023-12-13T02:40:27.479734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차선택(1차업종)1차선택(2차업종)2차선택(1차업종)3차선택(1차업종)근무국가1지역1근무국가2고용형태정규직고용형태프리랜서고용형태아르바이트회사규모
1차선택(1차업종)1.0000.8290.6650.5410.2650.0000.3250.0000.0000.0000.000
1차선택(2차업종)0.8291.0000.4640.5290.0000.4420.2460.0000.0000.0000.000
2차선택(1차업종)0.6650.4641.0000.3790.5800.0000.1901.0000.0000.0000.207
3차선택(1차업종)0.5410.5290.3791.0000.1180.0000.5501.0000.0000.0000.000
근무국가10.2650.0000.5800.1181.0001.0000.1390.8080.5340.6270.308
지역10.0000.4420.0000.0001.0001.0000.0001.0000.0000.0000.000
근무국가20.3250.2460.1900.5500.1390.0001.0001.0000.0000.0000.000
고용형태정규직0.0000.0001.0001.0000.8081.0001.0001.0001.0001.0000.560
고용형태프리랜서0.0000.0000.0000.0000.5340.0000.0001.0001.0000.6850.643
고용형태아르바이트0.0000.0000.0000.0000.6270.0000.0001.0000.6851.0000.105
회사규모0.0000.0000.2070.0000.3080.0000.0000.5600.6430.1051.000

Missing values

2023-12-13T02:40:20.118142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:40:20.401209image/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.
2023-12-13T02:40:20.650338image/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

1차선택(1차업종)1차선택(2차업종)2차선택(1차업종)2차선택(2차업종)3차선택(1차업종)3선택(2차업종)근무국가1지역1근무국가2지역2근무국가3지역3고용형태정규직고용형태프리랜서고용형태아르바이트급여수준(만원)회사규모
0미디어/방송/신문/출판/광고연예/엔터테인먼트/문화/공연<NA><NA><NA><NA>중국<NA>한국<NA><NA><NA>정규직아르바이트계약직<NA>일반기업
1제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교<NA><NA><NA><NA>미국<NA>영국<NA>독일<NA>정규직<NA><NA>480~500외국계법인기업
2제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교미디어/방송/신문/출판/광고신문/잡지/출판/인쇄<NA><NA>중국<NA>홍콩<NA><NA><NA>정규직<NA><NA><NA><NA>
3제조/건설/기계/전기/섬유/제약건축/설비/설계/감리/ENG<NA><NA><NA><NA>말레이지아<NA>싱가포르<NA>중국<NA>정규직<NA><NA><NA><NA>
4제조/건설/기계/전기/섬유/제약연구/조사/리서치/컨설팅제조/건설/기계/전기/섬유/제약교육/유학/학원/학습지제조/건설/기계/전기/섬유/제약호텔/여행/관광/항공한국서울<NA><NA><NA><NA>정규직<NA><NA><NA><NA>
5제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교제조/건설/기계/전기/섬유/제약연구/조사/리서치/컨설팅제조/건설/기계/전기/섬유/제약외식/프랜차이즈스리랑카<NA>한국부산한국서울정규직인턴직<NA><NA>중소기업(300명 미만)
6제조/건설/기계/전기/섬유/제약기계/자동차/조선/항공제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교인터넷/IT/정보통신SI/SM/ERP/CRM한국서울몽골<NA><NA><NA>정규직아르바이트<NA><NA><NA>
7인터넷/IT/정보통신모바일/유무선인터넷/IT/정보통신SI/SM/ERP/CRM제조/건설/기계/전기/섬유/제약컴퓨터/하드웨어/시스템말레이지아<NA><NA><NA><NA><NA>정규직아르바이트계약직<NA>기업규모무관
8제조/건설/기계/전기/섬유/제약무역/상사제조/건설/기계/전기/섬유/제약운송/운수/물류제조/건설/기계/전기/섬유/제약연구/조사/리서치/컨설팅한국서울한국인천한국제주정규직<NA><NA><NA>대기업(1000명 이상)
9미디어/방송/신문/출판/광고연예/엔터테인먼트/문화/공연미디어/방송/신문/출판/광고방송/영상/케이블/프로덕션미디어/방송/신문/출판/광고신문/잡지/출판/인쇄한국서울대만<NA><NA><NA>정규직<NA><NA><NA>대기업(1000명 이상)
1차선택(1차업종)1차선택(2차업종)2차선택(1차업종)2차선택(2차업종)3차선택(1차업종)3선택(2차업종)근무국가1지역1근무국가2지역2근무국가3지역3고용형태정규직고용형태프리랜서고용형태아르바이트급여수준(만원)회사규모
41제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교미디어/방송/신문/출판/광고광고/홍보/전시/이벤트제조/건설/기계/전기/섬유/제약기계/자동차/조선/항공카자흐스탄<NA>한국서울싱가포르<NA>정규직<NA><NA><NA>외국계투자기업
42제조/건설/기계/전기/섬유/제약전기/전자/반도체/광학제조/건설/기계/전기/섬유/제약전기/전자/반도체/광학제조/건설/기계/전기/섬유/제약전기/전자/반도체/광학한국<NA>몽골<NA>오스트레일리아<NA>정규직<NA><NA><NA>대기업(1000명 이상)
43제조/건설/기계/전기/섬유/제약무역/상사제조/건설/기계/전기/섬유/제약금융(은행/증권/보험/카드)제조/건설/기계/전기/섬유/제약연구/조사/리서치/컨설팅한국서울우즈베키스탄<NA>한국부산정규직아르바이트계약직<NA>기업규모무관
44미디어/방송/신문/출판/광고연예/엔터테인먼트/문화/공연<NA><NA><NA><NA>한국서울미국<NA>영국<NA>정규직<NA><NA><NA>공기업,공공기관,공사
45제조/건설/기계/전기/섬유/제약특허/법률/세뮤/회계/노무제조/건설/기계/전기/섬유/제약연구/조사/리서치/컨설팅제조/건설/기계/전기/섬유/제약특허/법률/세뮤/회계/노무한국서울한국인천한국경기정규직<NA><NA><NA>일반기업
46제조/건설/기계/전기/섬유/제약무역/상사제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교<NA><NA>싱가포르<NA><NA><NA><NA><NA>정규직<NA><NA><NA>중소기업(300명 미만)
47제조/건설/기계/전기/섬유/제약호텔/여행/관광/항공제조/건설/기계/전기/섬유/제약특허/법률/세뮤/회계/노무제조/건설/기계/전기/섬유/제약의료/건강/보건/복지한국충북한국서울필리핀<NA>정규직<NA><NA>220~240기업규모무관
48제조/건설/기계/전기/섬유/제약특허/법률/세뮤/회계/노무인터넷/IT/정보통신모바일/유무선제조/건설/기계/전기/섬유/제약기계/자동차/조선/항공몽골<NA>한국<NA>한국<NA>정규직인턴직아르바이트200~220외국계투자기업
49제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교<NA><NA><NA><NA>탄자니아<NA><NA><NA><NA><NA>아르바이트<NA><NA><NA>외국계법인기업
50제조/건설/기계/전기/섬유/제약공공기관/공사/공기업/협회/(대)학교미디어/방송/신문/출판/광고방송/영상/케이블/프로덕션<NA><NA>한국서울한국인천<NA><NA>정규직계약직<NA>240~260중견기업(300명 이상)