Overview

Dataset statistics

Number of variables8
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory73.3 B

Variable types

Numeric3
Text1
DateTime3
Categorical1

Dataset

Description한국기술교육대학교 온라인평생교육원 스마트 직업훈련 플랫폼 (STEP)에 대한 LMS 과정 등록 제한 이력 내용을 제공합니다.
Author한국기술교육대학교
URLhttps://www.data.go.kr/data/15091001/fileData.do

Alerts

등록 국가 has constant value ""Constant
아이디 is highly overall correlated with 과정 아이디 and 1 other fieldsHigh correlation
과정 아이디 is highly overall correlated with 아이디 and 1 other fieldsHigh correlation
과정 아이템 아이디 is highly overall correlated with 아이디 and 1 other fieldsHigh correlation
아이디 has unique valuesUnique
과정 아이디 has unique valuesUnique
과정 아이템 아이디 has unique valuesUnique
등록 시작 일시 has unique valuesUnique
등록 종료 일시 has unique valuesUnique
등록 일시 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:43:43.056144
Analysis finished2023-12-12 10:43:45.083141
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.714286
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T19:43:45.157400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q112
median24
Q330
95-th percentile52
Maximum57
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.815002
Coefficient of variation (CV)0.66689767
Kurtosis-0.31995315
Mean23.714286
Median Absolute Deviation (MAD)11
Skewness0.52430652
Sum498
Variance250.11429
MonotonicityStrictly increasing
2023-12-12T19:43:45.324508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
57 1
 
4.8%
52 1
 
4.8%
46 1
 
4.8%
40 1
 
4.8%
34 1
 
4.8%
30 1
 
4.8%
29 1
 
4.8%
28 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
5 1
4.8%
8 1
4.8%
9 1
4.8%
12 1
4.8%
13 1
4.8%
16 1
4.8%
20 1
4.8%
21 1
4.8%
ValueCountFrequency (%)
57 1
4.8%
52 1
4.8%
46 1
4.8%
40 1
4.8%
34 1
4.8%
30 1
4.8%
29 1
4.8%
28 1
4.8%
26 1
4.8%
25 1
4.8%

제목
Text

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T19:43:45.602297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length20
Mean length17.47619
Min length1

Characters and Unicode

Total characters367
Distinct characters100
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row빅데이터 과정
2nd row빅데이터 분석 실무 활용 중급 과정
3rd row빅데이터 분석 실무 활용 중급 온라인 기초
4th row(test)HTML5 기반 스마트 앱 개발하기
5th rowHTML5 기반 스마트 웹 페이지 제작하기_1
ValueCountFrequency (%)
과정 6
 
6.5%
빅데이터 5
 
5.4%
5
 
5.4%
기반 5
 
5.4%
5
 
5.4%
분석 4
 
4.3%
스마트 4
 
4.3%
이해 3
 
3.2%
메이머스트 3
 
3.2%
가상화 3
 
3.2%
Other values (32) 50
53.8%
2023-12-12T19:43:46.083521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
72
 
19.6%
18
 
4.9%
14
 
3.8%
13
 
3.5%
11
 
3.0%
8
 
2.2%
8
 
2.2%
t 6
 
1.6%
6
 
1.6%
6
 
1.6%
Other values (90) 205
55.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 237
64.6%
Space Separator 72
 
19.6%
Uppercase Letter 19
 
5.2%
Lowercase Letter 18
 
4.9%
Decimal Number 6
 
1.6%
Open Punctuation 6
 
1.6%
Close Punctuation 6
 
1.6%
Connector Punctuation 2
 
0.5%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
18
 
7.6%
14
 
5.9%
13
 
5.5%
11
 
4.6%
8
 
3.4%
8
 
3.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
Other values (65) 143
60.3%
Lowercase Letter
ValueCountFrequency (%)
t 6
33.3%
e 3
16.7%
s 3
16.7%
n 1
 
5.6%
a 1
 
5.6%
j 1
 
5.6%
g 1
 
5.6%
o 1
 
5.6%
d 1
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
T 5
26.3%
L 4
21.1%
H 4
21.1%
M 4
21.1%
D 1
 
5.3%
I 1
 
5.3%
Decimal Number
ValueCountFrequency (%)
5 4
66.7%
2 1
 
16.7%
1 1
 
16.7%
Open Punctuation
ValueCountFrequency (%)
[ 4
66.7%
( 2
33.3%
Close Punctuation
ValueCountFrequency (%)
] 4
66.7%
) 2
33.3%
Space Separator
ValueCountFrequency (%)
72
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 237
64.6%
Common 93
 
25.3%
Latin 37
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
18
 
7.6%
14
 
5.9%
13
 
5.5%
11
 
4.6%
8
 
3.4%
8
 
3.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
Other values (65) 143
60.3%
Latin
ValueCountFrequency (%)
t 6
16.2%
T 5
13.5%
L 4
10.8%
H 4
10.8%
M 4
10.8%
e 3
8.1%
s 3
8.1%
n 1
 
2.7%
a 1
 
2.7%
D 1
 
2.7%
Other values (5) 5
13.5%
Common
ValueCountFrequency (%)
72
77.4%
5 4
 
4.3%
[ 4
 
4.3%
] 4
 
4.3%
_ 2
 
2.2%
( 2
 
2.2%
) 2
 
2.2%
2 1
 
1.1%
- 1
 
1.1%
1 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 237
64.6%
ASCII 130
35.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72
55.4%
t 6
 
4.6%
T 5
 
3.8%
5 4
 
3.1%
[ 4
 
3.1%
] 4
 
3.1%
L 4
 
3.1%
H 4
 
3.1%
M 4
 
3.1%
e 3
 
2.3%
Other values (15) 20
 
15.4%
Hangul
ValueCountFrequency (%)
18
 
7.6%
14
 
5.9%
13
 
5.5%
11
 
4.6%
8
 
3.4%
8
 
3.4%
6
 
2.5%
6
 
2.5%
5
 
2.1%
5
 
2.1%
Other values (65) 143
60.3%

과정 아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean237.14286
Minimum10
Maximum570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T19:43:46.251373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile20
Q1120
median240
Q3300
95-th percentile520
Maximum570
Range560
Interquartile range (IQR)180

Descriptive statistics

Standard deviation158.15002
Coefficient of variation (CV)0.66689767
Kurtosis-0.31995315
Mean237.14286
Median Absolute Deviation (MAD)110
Skewness0.52430652
Sum4980
Variance25011.429
MonotonicityStrictly increasing
2023-12-12T19:43:46.426400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
10 1
 
4.8%
20 1
 
4.8%
570 1
 
4.8%
520 1
 
4.8%
460 1
 
4.8%
400 1
 
4.8%
340 1
 
4.8%
300 1
 
4.8%
290 1
 
4.8%
280 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
10 1
4.8%
20 1
4.8%
50 1
4.8%
80 1
4.8%
90 1
4.8%
120 1
4.8%
130 1
4.8%
160 1
4.8%
200 1
4.8%
210 1
4.8%
ValueCountFrequency (%)
570 1
4.8%
520 1
4.8%
460 1
4.8%
400 1
4.8%
340 1
4.8%
300 1
4.8%
290 1
4.8%
280 1
4.8%
260 1
4.8%
250 1
4.8%

과정 아이템 아이디
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2371.4286
Minimum100
Maximum5700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T19:43:46.587613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q11200
median2400
Q33000
95-th percentile5200
Maximum5700
Range5600
Interquartile range (IQR)1800

Descriptive statistics

Standard deviation1581.5002
Coefficient of variation (CV)0.66689767
Kurtosis-0.31995315
Mean2371.4286
Median Absolute Deviation (MAD)1100
Skewness0.52430652
Sum49800
Variance2501142.9
MonotonicityStrictly increasing
2023-12-12T19:43:46.746995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
100 1
 
4.8%
200 1
 
4.8%
5700 1
 
4.8%
5200 1
 
4.8%
4600 1
 
4.8%
4000 1
 
4.8%
3400 1
 
4.8%
3000 1
 
4.8%
2900 1
 
4.8%
2800 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
100 1
4.8%
200 1
4.8%
500 1
4.8%
800 1
4.8%
900 1
4.8%
1200 1
4.8%
1300 1
4.8%
1600 1
4.8%
2000 1
4.8%
2100 1
4.8%
ValueCountFrequency (%)
5700 1
4.8%
5200 1
4.8%
4600 1
4.8%
4000 1
4.8%
3400 1
4.8%
3000 1
4.8%
2900 1
4.8%
2800 1
4.8%
2600 1
4.8%
2500 1
4.8%
Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2017-07-21 13:10:00
Maximum2020-02-17 11:01:00
2023-12-12T19:43:46.931861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:47.140309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2017-07-28 13:10:00
Maximum2020-02-24 11:01:00
2023-12-12T19:43:47.330312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:47.516148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

등록 국가
Categorical

CONSTANT 

Distinct1
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
KR
21 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KR 21
100.0%

Length

2023-12-12T19:43:47.722659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:43:47.863821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kr 21
100.0%

등록 일시
Date

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
Minimum2017-07-21 13:10:00
Maximum2020-02-17 11:01:00
2023-12-12T19:43:48.004730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:48.198073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)

Interactions

2023-12-12T19:43:44.479016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:43.335584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:43.669206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:44.573962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:43.450325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:44.158275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:44.675579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:43.555856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:44.265599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:43:48.311829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디제목과정 아이디과정 아이템 아이디등록 시작 일시등록 종료 일시등록 일시
아이디1.0000.6890.9990.9991.0001.0001.000
제목0.6891.0000.6890.6891.0001.0001.000
과정 아이디0.9990.6891.0001.0001.0001.0001.000
과정 아이템 아이디0.9990.6891.0001.0001.0001.0001.000
등록 시작 일시1.0001.0001.0001.0001.0001.0001.000
등록 종료 일시1.0001.0001.0001.0001.0001.0001.000
등록 일시1.0001.0001.0001.0001.0001.0001.000
2023-12-12T19:43:48.462461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아이디과정 아이디과정 아이템 아이디
아이디1.0001.0001.000
과정 아이디1.0001.0001.000
과정 아이템 아이디1.0001.0001.000

Missing values

2023-12-12T19:43:44.858779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:43:45.020590image/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

아이디제목과정 아이디과정 아이템 아이디등록 시작 일시등록 종료 일시등록 국가등록 일시
01빅데이터 과정101002017-07-21 13:102017-07-28 13:10KR2017-07-21 13:10
12빅데이터 분석 실무 활용 중급 과정202002017-12-04 19:202017-12-11 19:20KR2017-12-04 19:20
25빅데이터 분석 실무 활용 중급 온라인 기초505002017-12-07 17:072017-12-14 17:07KR2017-12-07 17:07
38(test)HTML5 기반 스마트 앱 개발하기808002018-04-16 21:512018-04-23 21:51KR2018-04-16 21:51
49HTML5 기반 스마트 웹 페이지 제작하기_1909002018-05-04 18:412018-05-11 18:41KR2018-05-04 18:41
512Django 기반 웹 사이트 개발하기12012002018-05-04 18:422018-05-11 18:42KR2018-05-04 18:42
613HTML5 기반 스마트 웹 페이지 제작하기13013002018-05-24 16:372018-05-31 16:37KR2018-05-24 16:37
716HTML5 기반 스마트 웹 페이지 제작하기16016002018-05-24 16:552018-05-31 16:55KR2018-05-24 16:55
820(test) 테스트 과정20020002018-05-25 17:002018-06-01 17:00KR2018-05-25 17:00
921[팀 프로젝트] IT기반 파이썬 메이커 과정21021002018-05-31 09:342018-06-07 09:34KR2018-05-31 09:34
아이디제목과정 아이디과정 아이템 아이디등록 시작 일시등록 종료 일시등록 국가등록 일시
1125[메이머스트] 가상화 솔루션 이해 및 관리 채용연계형 과정25025002018-07-19 17:112018-07-26 17:11KR2018-07-19 17:11
1226test26026002018-07-20 18:012018-07-27 18:01KR2018-07-20 18:01
1328[메이머스트] 가상화 솔루션 이해 및 관리 재직자교육28028002018-08-01 15:242018-08-08 15:24KR2018-08-01 15:24
1429[메이머스트] 가상화 솔루션 이해 및 관리 채용연계형 과정 2차29029002018-10-19 16:582018-10-26 16:58KR2018-10-19 16:58
1530테스트과정_구본철30030002018-11-24 20:452018-12-01 20:45KR2018-11-24 20:45
1634패키지 테스트-사전등록학습자용34034002019-11-01 14:562019-11-08 14:56KR2019-11-01 14:56
1740빅데이터 수집 및 분석40040002019-11-14 13:422019-11-21 13:42KR2019-11-14 13:42
1846빅데이터 수집 및 분석46046002019-11-14 13:442019-11-21 13:44KR2019-11-14 13:44
1952d52052002019-12-11 22:512019-12-18 22:51KR2019-12-11 22:51
2057테스트과정57057002020-02-17 11:012020-02-24 11:01KR2020-02-17 11:01