Overview

Dataset statistics

Number of variables14
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory131.3 B

Variable types

Text1
Numeric12
Categorical1

Dataset

Description대전광역시 2019년 시민정보화교육 현황입니다.2022년 공공데이터 기업매칭지원사업으로 수행되었습니다
Author대전광역시
URLhttps://www.data.go.kr/data/15111015/fileData.do

Alerts

수강인원 is highly overall correlated with 설문인원 and 2 other fieldsHigh correlation
설문인원 is highly overall correlated with 수강인원 and 3 other fieldsHigh correlation
is highly overall correlated with 설문인원 and 3 other fieldsHigh correlation
is highly overall correlated with 40대이하 and 1 other fieldsHigh correlation
40대이하 is highly overall correlated with and 1 other fieldsHigh correlation
50대 is highly overall correlated with 수강인원 and 1 other fieldsHigh correlation
60대 is highly overall correlated with High correlation
70대이상 is highly overall correlated with High correlation
전업주부 is highly overall correlated with and 1 other fieldsHigh correlation
자영업 is highly overall correlated with 농어민High correlation
무직 is highly overall correlated with 수강인원 and 2 other fieldsHigh correlation
농어민 is highly overall correlated with 자영업High correlation
농어민 is highly imbalanced (72.4%)Imbalance
40대이하 has 3 (14.3%) zerosZeros
직장인 has 2 (9.5%) zerosZeros
자영업 has 1 (4.8%) zerosZeros

Reproduction

Analysis started2023-12-12 16:55:44.463268
Analysis finished2023-12-12 16:55:57.919588
Duration13.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-13T01:55:58.071176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length15
Mean length13.142857
Min length7

Characters and Unicode

Total characters276
Distinct characters81
Distinct categories7 ?
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 row컴퓨터 및 인터넷기초반(1기)
2nd row생활문서작성 한글2010(1기)
3rd rowITQ한글2010자격증반
4th row스마트폰활용반
5th row국민행복IT경진대회예선대비반
ValueCountFrequency (%)
6
 
14.6%
이미지편집 2
 
4.9%
활용반 2
 
4.9%
동영상제작 2
 
4.9%
블로그제작 2
 
4.9%
운영 2
 
4.9%
컴퓨터 2
 
4.9%
생활문서작성 2
 
4.9%
쉽게배우는 1
 
2.4%
스크래치 1
 
2.4%
Other values (19) 19
46.3%
2023-12-13T01:55:58.436541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20
 
7.2%
0 16
 
5.8%
2 11
 
4.0%
1 10
 
3.6%
10
 
3.6%
9
 
3.3%
8
 
2.9%
7
 
2.5%
7
 
2.5%
6
 
2.2%
Other values (71) 172
62.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 190
68.8%
Decimal Number 37
 
13.4%
Space Separator 20
 
7.2%
Uppercase Letter 16
 
5.8%
Open Punctuation 6
 
2.2%
Close Punctuation 6
 
2.2%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10
 
5.3%
9
 
4.7%
8
 
4.2%
7
 
3.7%
7
 
3.7%
6
 
3.2%
6
 
3.2%
5
 
2.6%
5
 
2.6%
4
 
2.1%
Other values (59) 123
64.7%
Uppercase Letter
ValueCountFrequency (%)
I 5
31.2%
T 5
31.2%
Q 3
18.8%
S 2
 
12.5%
N 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
0 16
43.2%
2 11
29.7%
1 10
27.0%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 190
68.8%
Common 70
 
25.4%
Latin 16
 
5.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10
 
5.3%
9
 
4.7%
8
 
4.2%
7
 
3.7%
7
 
3.7%
6
 
3.2%
6
 
3.2%
5
 
2.6%
5
 
2.6%
4
 
2.1%
Other values (59) 123
64.7%
Common
ValueCountFrequency (%)
20
28.6%
0 16
22.9%
2 11
15.7%
1 10
14.3%
( 6
 
8.6%
) 6
 
8.6%
& 1
 
1.4%
Latin
ValueCountFrequency (%)
I 5
31.2%
T 5
31.2%
Q 3
18.8%
S 2
 
12.5%
N 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 190
68.8%
ASCII 86
31.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20
23.3%
0 16
18.6%
2 11
12.8%
1 10
11.6%
( 6
 
7.0%
) 6
 
7.0%
I 5
 
5.8%
T 5
 
5.8%
Q 3
 
3.5%
S 2
 
2.3%
Other values (2) 2
 
2.3%
Hangul
ValueCountFrequency (%)
10
 
5.3%
9
 
4.7%
8
 
4.2%
7
 
3.7%
7
 
3.7%
6
 
3.2%
6
 
3.2%
5
 
2.6%
5
 
2.6%
4
 
2.1%
Other values (59) 123
64.7%

수강인원
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.238095
Minimum7
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:58.591545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q130
median30
Q330
95-th percentile32
Maximum34
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.3710655
Coefficient of variation (CV)0.22561952
Kurtosis7.7418113
Mean28.238095
Median Absolute Deviation (MAD)0
Skewness-2.8911452
Sum593
Variance40.590476
MonotonicityNot monotonic
2023-12-13T01:55:58.744646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 16
76.2%
32 1
 
4.8%
28 1
 
4.8%
12 1
 
4.8%
34 1
 
4.8%
7 1
 
4.8%
ValueCountFrequency (%)
7 1
 
4.8%
12 1
 
4.8%
28 1
 
4.8%
30 16
76.2%
32 1
 
4.8%
34 1
 
4.8%
ValueCountFrequency (%)
34 1
 
4.8%
32 1
 
4.8%
30 16
76.2%
28 1
 
4.8%
12 1
 
4.8%
7 1
 
4.8%

설문인원
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.904762
Minimum7
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:58.889819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q130
median30
Q330
95-th percentile32
Maximum34
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.3789087
Coefficient of variation (CV)0.2285957
Kurtosis6.9741173
Mean27.904762
Median Absolute Deviation (MAD)0
Skewness-2.7166207
Sum586
Variance40.690476
MonotonicityNot monotonic
2023-12-13T01:55:59.021119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
30 14
66.7%
28 2
 
9.5%
32 1
 
4.8%
12 1
 
4.8%
34 1
 
4.8%
7 1
 
4.8%
25 1
 
4.8%
ValueCountFrequency (%)
7 1
 
4.8%
12 1
 
4.8%
25 1
 
4.8%
28 2
 
9.5%
30 14
66.7%
32 1
 
4.8%
34 1
 
4.8%
ValueCountFrequency (%)
34 1
 
4.8%
32 1
 
4.8%
30 14
66.7%
28 2
 
9.5%
25 1
 
4.8%
12 1
 
4.8%
7 1
 
4.8%


Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.095238
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:59.178738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q19
median10
Q313
95-th percentile14
Maximum20
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0976184
Coefficient of variation (CV)0.40589616
Kurtosis0.70787936
Mean10.095238
Median Absolute Deviation (MAD)3
Skewness0.12212046
Sum212
Variance16.790476
MonotonicityNot monotonic
2023-12-13T01:55:59.328542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
9 4
19.0%
14 3
14.3%
11 3
14.3%
5 2
9.5%
13 2
9.5%
10 2
9.5%
20 1
 
4.8%
12 1
 
4.8%
4 1
 
4.8%
2 1
 
4.8%
ValueCountFrequency (%)
2 1
 
4.8%
4 1
 
4.8%
5 2
9.5%
7 1
 
4.8%
9 4
19.0%
10 2
9.5%
11 3
14.3%
12 1
 
4.8%
13 2
9.5%
14 3
14.3%
ValueCountFrequency (%)
20 1
 
4.8%
14 3
14.3%
13 2
9.5%
12 1
 
4.8%
11 3
14.3%
10 2
9.5%
9 4
19.0%
7 1
 
4.8%
5 2
9.5%
4 1
 
4.8%


Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.142857
Minimum5
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:59.479506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q117
median20
Q321
95-th percentile25
Maximum26
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.2942017
Coefficient of variation (CV)0.29180639
Kurtosis1.3614713
Mean18.142857
Median Absolute Deviation (MAD)3
Skewness-1.1765922
Sum381
Variance28.028571
MonotonicityNot monotonic
2023-12-13T01:55:59.601625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
21 5
23.8%
17 3
14.3%
20 3
14.3%
16 2
 
9.5%
7 1
 
4.8%
10 1
 
4.8%
18 1
 
4.8%
26 1
 
4.8%
5 1
 
4.8%
23 1
 
4.8%
Other values (2) 2
 
9.5%
ValueCountFrequency (%)
5 1
 
4.8%
7 1
 
4.8%
10 1
 
4.8%
16 2
 
9.5%
17 3
14.3%
18 1
 
4.8%
19 1
 
4.8%
20 3
14.3%
21 5
23.8%
23 1
 
4.8%
ValueCountFrequency (%)
26 1
 
4.8%
25 1
 
4.8%
23 1
 
4.8%
21 5
23.8%
20 3
14.3%
19 1
 
4.8%
18 1
 
4.8%
17 3
14.3%
16 2
 
9.5%
10 1
 
4.8%

40대이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6666667
Minimum0
Maximum14
Zeros3
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:59.723103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q310
95-th percentile13
Maximum14
Range14
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.9631979
Coefficient of variation (CV)0.87585845
Kurtosis-1.5364582
Mean5.6666667
Median Absolute Deviation (MAD)4
Skewness0.3446348
Sum119
Variance24.633333
MonotonicityNot monotonic
2023-12-13T01:55:59.845888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 4
19.0%
0 3
14.3%
1 3
14.3%
10 2
9.5%
12 2
9.5%
6 1
 
4.8%
13 1
 
4.8%
7 1
 
4.8%
8 1
 
4.8%
11 1
 
4.8%
Other values (2) 2
9.5%
ValueCountFrequency (%)
0 3
14.3%
1 3
14.3%
2 4
19.0%
5 1
 
4.8%
6 1
 
4.8%
7 1
 
4.8%
8 1
 
4.8%
10 2
9.5%
11 1
 
4.8%
12 2
9.5%
ValueCountFrequency (%)
14 1
 
4.8%
13 1
 
4.8%
12 2
9.5%
11 1
 
4.8%
10 2
9.5%
8 1
 
4.8%
7 1
 
4.8%
6 1
 
4.8%
5 1
 
4.8%
2 4
19.0%

50대
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9047619
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:55:59.982054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8413246
Coefficient of variation (CV)0.37541569
Kurtosis-0.21117873
Mean4.9047619
Median Absolute Deviation (MAD)1
Skewness-0.16471668
Sum103
Variance3.3904762
MonotonicityNot monotonic
2023-12-13T01:56:00.121527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 5
23.8%
6 4
19.0%
5 4
19.0%
3 2
 
9.5%
7 2
 
9.5%
8 2
 
9.5%
2 1
 
4.8%
1 1
 
4.8%
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
3 2
 
9.5%
4 5
23.8%
5 4
19.0%
6 4
19.0%
7 2
 
9.5%
8 2
 
9.5%
ValueCountFrequency (%)
8 2
 
9.5%
7 2
 
9.5%
6 4
19.0%
5 4
19.0%
4 5
23.8%
3 2
 
9.5%
2 1
 
4.8%
1 1
 
4.8%

60대
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.857143
Minimum3
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:00.254884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q18
median11
Q315
95-th percentile16
Maximum20
Range17
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.3044827
Coefficient of variation (CV)0.39646551
Kurtosis-0.57720377
Mean10.857143
Median Absolute Deviation (MAD)4
Skewness0.1828417
Sum228
Variance18.528571
MonotonicityNot monotonic
2023-12-13T01:56:00.392159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
15 4
19.0%
6 3
14.3%
8 3
14.3%
11 2
9.5%
14 1
 
4.8%
16 1
 
4.8%
7 1
 
4.8%
12 1
 
4.8%
10 1
 
4.8%
13 1
 
4.8%
Other values (3) 3
14.3%
ValueCountFrequency (%)
3 1
 
4.8%
6 3
14.3%
7 1
 
4.8%
8 3
14.3%
9 1
 
4.8%
10 1
 
4.8%
11 2
9.5%
12 1
 
4.8%
13 1
 
4.8%
14 1
 
4.8%
ValueCountFrequency (%)
20 1
 
4.8%
16 1
 
4.8%
15 4
19.0%
14 1
 
4.8%
13 1
 
4.8%
12 1
 
4.8%
11 2
9.5%
10 1
 
4.8%
9 1
 
4.8%
8 3
14.3%

70대이상
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.8095238
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:00.862476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median7
Q38
95-th percentile12
Maximum13
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8743529
Coefficient of variation (CV)0.42210777
Kurtosis-0.15273155
Mean6.8095238
Median Absolute Deviation (MAD)2
Skewness0.61567415
Sum143
Variance8.2619048
MonotonicityNot monotonic
2023-12-13T01:56:00.983850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7 4
19.0%
6 3
14.3%
8 3
14.3%
3 3
14.3%
5 2
9.5%
4 2
9.5%
10 1
 
4.8%
13 1
 
4.8%
12 1
 
4.8%
11 1
 
4.8%
ValueCountFrequency (%)
3 3
14.3%
4 2
9.5%
5 2
9.5%
6 3
14.3%
7 4
19.0%
8 3
14.3%
10 1
 
4.8%
11 1
 
4.8%
12 1
 
4.8%
13 1
 
4.8%
ValueCountFrequency (%)
13 1
 
4.8%
12 1
 
4.8%
11 1
 
4.8%
10 1
 
4.8%
8 3
14.3%
7 4
19.0%
6 3
14.3%
5 2
9.5%
4 2
9.5%
3 3
14.3%

전업주부
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)71.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.238095
Minimum3
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:01.111163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median14
Q317
95-th percentile19
Maximum21
Range18
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6249839
Coefficient of variation (CV)0.34936929
Kurtosis-0.1797808
Mean13.238095
Median Absolute Deviation (MAD)3
Skewness-0.41273202
Sum278
Variance21.390476
MonotonicityNot monotonic
2023-12-13T01:56:01.253335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
14 3
14.3%
17 2
 
9.5%
11 2
 
9.5%
12 2
 
9.5%
19 2
 
9.5%
13 1
 
4.8%
16 1
 
4.8%
7 1
 
4.8%
6 1
 
4.8%
18 1
 
4.8%
Other values (5) 5
23.8%
ValueCountFrequency (%)
3 1
 
4.8%
6 1
 
4.8%
7 1
 
4.8%
9 1
 
4.8%
10 1
 
4.8%
11 2
9.5%
12 2
9.5%
13 1
 
4.8%
14 3
14.3%
15 1
 
4.8%
ValueCountFrequency (%)
21 1
 
4.8%
19 2
9.5%
18 1
 
4.8%
17 2
9.5%
16 1
 
4.8%
15 1
 
4.8%
14 3
14.3%
13 1
 
4.8%
12 2
9.5%
11 2
9.5%

직장인
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7142857
Minimum0
Maximum8
Zeros2
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:01.397317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0770859
Coefficient of variation (CV)0.76524216
Kurtosis1.2018671
Mean2.7142857
Median Absolute Deviation (MAD)1
Skewness1.1251587
Sum57
Variance4.3142857
MonotonicityNot monotonic
2023-12-13T01:56:01.530365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6
28.6%
1 4
19.0%
4 3
14.3%
3 3
14.3%
0 2
 
9.5%
5 1
 
4.8%
8 1
 
4.8%
7 1
 
4.8%
ValueCountFrequency (%)
0 2
 
9.5%
1 4
19.0%
2 6
28.6%
3 3
14.3%
4 3
14.3%
5 1
 
4.8%
7 1
 
4.8%
8 1
 
4.8%
ValueCountFrequency (%)
8 1
 
4.8%
7 1
 
4.8%
5 1
 
4.8%
4 3
14.3%
3 3
14.3%
2 6
28.6%
1 4
19.0%
0 2
 
9.5%

농어민
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
20 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
95.2%
1 1
 
4.8%

Length

2023-12-13T01:56:01.692006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:56:01.818086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
95.2%
1 1
 
4.8%

자영업
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6190476
Minimum0
Maximum9
Zeros1
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:01.921542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2017309
Coefficient of variation (CV)0.8406609
Kurtosis2.3148877
Mean2.6190476
Median Absolute Deviation (MAD)1
Skewness1.5324328
Sum55
Variance4.847619
MonotonicityNot monotonic
2023-12-13T01:56:02.062761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 7
33.3%
2 6
28.6%
4 3
14.3%
6 2
 
9.5%
3 1
 
4.8%
0 1
 
4.8%
9 1
 
4.8%
ValueCountFrequency (%)
0 1
 
4.8%
1 7
33.3%
2 6
28.6%
3 1
 
4.8%
4 3
14.3%
6 2
 
9.5%
9 1
 
4.8%
ValueCountFrequency (%)
9 1
 
4.8%
6 2
 
9.5%
4 3
14.3%
3 1
 
4.8%
2 6
28.6%
1 7
33.3%
0 1
 
4.8%

무직
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6190476
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-13T01:56:02.220161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median9
Q312
95-th percentile16
Maximum20
Range19
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5877684
Coefficient of variation (CV)0.47694622
Kurtosis0.089521315
Mean9.6190476
Median Absolute Deviation (MAD)3
Skewness0.47744919
Sum202
Variance21.047619
MonotonicityNot monotonic
2023-12-13T01:56:02.409332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
8 3
14.3%
10 2
9.5%
6 2
9.5%
7 2
9.5%
16 2
9.5%
9 2
9.5%
11 1
 
4.8%
4 1
 
4.8%
20 1
 
4.8%
15 1
 
4.8%
Other values (4) 4
19.0%
ValueCountFrequency (%)
1 1
 
4.8%
4 1
 
4.8%
5 1
 
4.8%
6 2
9.5%
7 2
9.5%
8 3
14.3%
9 2
9.5%
10 2
9.5%
11 1
 
4.8%
12 1
 
4.8%
ValueCountFrequency (%)
20 1
 
4.8%
16 2
9.5%
15 1
 
4.8%
14 1
 
4.8%
12 1
 
4.8%
11 1
 
4.8%
10 2
9.5%
9 2
9.5%
8 3
14.3%
7 2
9.5%

Interactions

2023-12-13T01:55:56.319710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:44.917891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.772900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.573614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.490772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.498656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.975605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.079341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.170863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.076902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.228410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.280896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.413510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.004247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.836919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.636326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.571488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.905784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.056280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.165293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.257896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.152480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.303376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.364927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.496912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.072704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.897249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.703178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.642845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.986061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.139115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.265827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.332818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.225714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.387336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.442536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.589010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.149175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.974460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.779921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.722521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.088558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.227125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.356929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.426584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.304335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.475676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.518100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.670329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.217531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.031916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.844289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.791487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.203865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.308325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.434655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.499720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.371972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.554506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.584896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.773056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.287137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.101439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.916183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.868954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.304029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.399443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.518235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.569622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.449139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.636068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.666023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.873409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.356802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.170925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.990866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.943405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.396122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.488115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.605679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.640376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.530231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.719070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.753521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:57.030802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.424121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.238041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.059674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.018837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.496100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.586024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.700959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.716497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.603180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.811411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.856035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:57.120192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.488391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.300739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.123122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.092039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.586812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.666214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.798431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.778515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.671689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:54.926074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.927265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:57.254980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.558248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.371438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.204157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.212252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.686692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.782668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:51.893050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.862774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.748260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.018362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.024673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:57.386830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.639231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.442639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.282483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.318748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.792265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.881351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.007165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.940899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.828238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.110394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.155240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:57.480261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:45.705789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:46.503215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:47.386143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:48.401597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:49.886768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:50.960854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:52.078410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.003865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:53.891982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:55.185782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:55:56.235848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:56:02.540381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시민_정보화_교육_과정명수강인원설문인원40대이하50대60대70대이상전업주부직장인농어민자영업무직
시민_정보화_교육_과정명1.0000.8970.9340.9220.8020.8880.7020.7980.8550.8710.8720.0000.0000.935
수강인원0.8971.0000.9360.5130.3590.0000.7920.1870.0000.8970.0000.0000.1290.800
설문인원0.9340.9361.0000.7330.6610.0000.7810.0000.0000.7930.5920.0000.0000.773
0.9220.5130.7331.0000.9020.0000.7460.6540.4980.7960.6660.0000.0000.730
0.8020.3590.6610.9021.0000.0000.5310.5180.7320.7670.4220.0000.0000.667
40대이하0.8880.0000.0000.0000.0001.0000.0000.0000.0000.0000.7850.0000.2670.000
50대0.7020.7920.7810.7460.5310.0001.0000.5510.2760.8920.0000.6090.3910.719
60대0.7980.1870.0000.6540.5180.0000.5511.0000.8430.0000.0000.0000.0000.690
70대이상0.8550.0000.0000.4980.7320.0000.2760.8431.0000.4030.0000.3900.0000.692
전업주부0.8710.8970.7930.7960.7670.0000.8920.0000.4031.0000.3440.3710.4020.785
직장인0.8720.0000.5920.6660.4220.7850.0000.0000.0000.3441.0000.0000.0000.000
농어민0.0000.0000.0000.0000.0000.0000.6090.0000.3900.3710.0001.0001.0000.000
자영업0.0000.1290.0000.0000.0000.2670.3910.0000.0000.4020.0001.0001.0000.000
무직0.9350.8000.7730.7300.6670.0000.7190.6900.6920.7850.0000.0000.0001.000
2023-12-13T01:56:02.717212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수강인원설문인원40대이하50대60대70대이상전업주부직장인자영업무직농어민
수강인원1.0000.8640.4410.4850.4940.5290.2300.2880.2580.3590.1610.5990.000
설문인원0.8641.0000.5390.2680.3280.5950.2810.2460.0670.1830.3090.6520.000
0.4410.5391.000-0.445-0.2730.2570.5610.604-0.2220.0540.2550.7990.000
0.4850.268-0.4451.0000.8270.324-0.155-0.1380.6700.363-0.073-0.1630.000
40대이하0.4940.328-0.2730.8271.0000.148-0.402-0.0490.6020.243-0.157-0.0050.000
50대0.5290.5950.2570.3240.1481.0000.1980.0890.1550.3620.3570.2520.363
60대0.2300.2810.561-0.155-0.4020.1981.0000.142-0.0200.0800.2090.4180.000
70대이상0.2880.2460.604-0.138-0.0490.0890.1421.000-0.0160.1130.2670.4690.281
전업주부0.2580.067-0.2220.6700.6020.155-0.020-0.0161.0000.059-0.146-0.2260.162
직장인0.3590.1830.0540.3630.2430.3620.0800.1130.0591.000-0.0850.1030.000
자영업0.1610.3090.255-0.073-0.1570.3570.2090.267-0.146-0.0851.000-0.0890.858
무직0.5990.6520.799-0.163-0.0050.2520.4180.469-0.2260.103-0.0891.0000.000
농어민0.0000.0000.0000.0000.0000.3630.0000.2810.1620.0000.8580.0001.000

Missing values

2023-12-13T01:55:57.634645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:55:57.850416image/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

시민_정보화_교육_과정명수강인원설문인원40대이하50대60대70대이상전업주부직장인농어민자영업무직
0컴퓨터 및 인터넷기초반(1기)303014160614101440210
1생활문서작성 한글2010(1기)32321121651561340411
2ITQ한글2010자격증반303092113368171066
3스마트폰활용반2828111714167161047
4국민행복IT경진대회예선대비반121257027370014
5SNS 활용반30302010231213620220
6이미지편집 및 동영상제작343414201071071410316
7블로그제작 및 운영30301218104881120116
8엑셀2010 활용반303042612765184026
9ITQ엑셀2010 자격증반3030131776134153048
시민_정보화_교육_과정명수강인원설문인원40대이하50대60대70대이상전업주부직장인농어민자영업무직
11생활문서작성 한글2010(2기)303014161611121220214
12국민행복IT경진대회본선대비반(모바일)7725013331021
13스마트폰활용반(2기)3030102025158190029
14엑셀2010활용3030921126661430112
15쉽게배우는 스크래치 코딩302892184117173019
16파워포인트2010활용302572311487195015
17ITQ파워포인트2010자격증303052514583212007
18블로그제작 및 운영303092128155102198
19스마트폰을활용한 이미지제작&동영상제작30301020541561180110
20이미지편집 및 동영상제작303011192891197068