Overview
Brought to you by YData
Dataset statistics
| Number of variables | 13 |
|---|---|
| Number of observations | 119 |
| Missing cells | 0 |
| Missing cells (%) | 0.0% |
| Duplicate rows | 17 |
| Duplicate rows (%) | 14.3% |
| Total size in memory | 113.1 KiB |
| Average record size in memory | 973.6 B |
Variable types
| Numeric | 11 |
|---|---|
| Text | 2 |
| Dataset has 17 (14.3%) duplicate rows | Duplicates |
Follows is highly overall correlated with From Hashtags and 4 other fields | High correlation |
From Explore is highly overall correlated with Impressions and 2 other fields | High correlation |
From Hashtags is highly overall correlated with Follows and 3 other fields | High correlation |
From Home is highly overall correlated with Impressions and 3 other fields | High correlation |
From Other is highly overall correlated with Follows and 1 other fields | High correlation |
Impressions is highly overall correlated with Follows and 6 other fields | High correlation |
Likes is highly overall correlated with Follows and 6 other fields | High correlation |
Profile Visits is highly overall correlated with Follows and 3 other fields | High correlation |
Saves is highly overall correlated with From Explore and 4 other fields | High correlation |
Shares is highly overall correlated with From Home and 2 other fields | High correlation |
Comments has 3 (2.5%) zeros | Zeros |
Shares has 5 (4.2%) zeros | Zeros |
Follows has 9 (7.6%) zeros | Zeros |
Reproduction
| Analysis started | 2025-09-15 13:58:20.959084 |
|---|---|
| Analysis finished | 2025-09-15 13:58:37.365022 |
| Duration | 16.41 seconds |
| Software version | ydata-profiling vv4.16.1 |
| Download configuration | config.json |
Variables
Impressions
Real number (ℝ)
High correlation
| Distinct | 101 |
|---|---|
| Distinct (%) | 84.9% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 5703.9916 |
| Minimum | 1941 |
|---|---|
| Maximum | 36919 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 1941 |
|---|---|
| 5-th percentile | 2407 |
| Q1 | 3467 |
| median | 4289 |
| Q3 | 6138 |
| 95-th percentile | 11404.1 |
| Maximum | 36919 |
| Range | 34978 |
| Interquartile range (IQR) | 2671 |
Descriptive statistics
| Standard deviation | 4843.7801 |
|---|---|
| Coefficient of variation (CV) | 0.84919131 |
| Kurtosis | 21.918792 |
| Mean | 5703.9916 |
| Median Absolute Deviation (MAD) | 1120 |
| Skewness | 4.1819648 |
| Sum | 678775 |
| Variance | 23462206 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 5394 | 3 | 2.5% |
| 2407 | 2 | 1.7% |
| 5055 | 2 | 1.7% |
| 6168 | 2 | 1.7% |
| 3169 | 2 | 1.7% |
| 4082 | 2 | 1.7% |
| 3924 | 2 | 1.7% |
| 3015 | 2 | 1.7% |
| 4628 | 2 | 1.7% |
| 4002 | 2 | 1.7% |
| Other values (91) | 98 |
| Value | Count | Frequency (%) |
| 1941 | 1 | |
| 2064 | 1 | |
| 2191 | 1 | |
| 2218 | 1 | |
| 2327 | 1 | |
| 2407 | 2 | |
| 2518 | 1 | |
| 2523 | 1 | |
| 2621 | 1 | |
| 2766 | 2 |
| Value | Count | Frequency (%) |
| 36919 | 1 | |
| 32695 | 1 | |
| 17713 | 1 | |
| 17396 | 1 | |
| 16062 | 1 | |
| 13700 | 1 | |
| 11149 | 1 | |
| 11068 | 1 | |
| 10933 | 1 | |
| 10667 | 1 |
From Home
Real number (ℝ)
High correlation
| Distinct | 97 |
|---|---|
| Distinct (%) | 81.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 2475.7899 |
| Minimum | 1133 |
|---|---|
| Maximum | 13473 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 1133 |
|---|---|
| 5-th percentile | 1338 |
| Q1 | 1945 |
| median | 2207 |
| Q3 | 2602.5 |
| 95-th percentile | 3845.4 |
| Maximum | 13473 |
| Range | 12340 |
| Interquartile range (IQR) | 657.5 |
Descriptive statistics
| Standard deviation | 1489.3863 |
|---|---|
| Coefficient of variation (CV) | 0.60158026 |
| Kurtosis | 37.42173 |
| Mean | 2475.7899 |
| Median Absolute Deviation (MAD) | 334 |
| Skewness | 5.6448226 |
| Sum | 294619 |
| Variance | 2218271.7 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 1975 | 3 | 2.5% |
| 2415 | 2 | 1.7% |
| 3401 | 2 | 1.7% |
| 2608 | 2 | 1.7% |
| 2017 | 2 | 1.7% |
| 2275 | 2 | 1.7% |
| 2541 | 2 | 1.7% |
| 2034 | 2 | 1.7% |
| 2406 | 2 | 1.7% |
| 2177 | 2 | 1.7% |
| Other values (87) | 98 |
| Value | Count | Frequency (%) |
| 1133 | 1 | |
| 1179 | 1 | |
| 1304 | 1 | |
| 1308 | 1 | |
| 1323 | 1 | |
| 1338 | 2 | |
| 1466 | 1 | |
| 1502 | 1 | |
| 1543 | 1 | |
| 1570 | 1 |
| Value | Count | Frequency (%) |
| 13473 | 1 | |
| 11815 | 1 | |
| 5185 | 1 | |
| 4439 | 1 | |
| 4137 | 2 | |
| 3813 | 1 | |
| 3717 | 1 | |
| 3401 | 2 | |
| 3152 | 2 | |
| 3144 | 1 |
From Hashtags
Real number (ℝ)
High correlation
| Distinct | 100 |
|---|---|
| Distinct (%) | 84.0% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1887.5126 |
| Minimum | 116 |
|---|---|
| Maximum | 11817 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 116 |
|---|---|
| 5-th percentile | 201 |
| Q1 | 726 |
| median | 1278 |
| Q3 | 2363.5 |
| 95-th percentile | 5129.4 |
| Maximum | 11817 |
| Range | 11701 |
| Interquartile range (IQR) | 1637.5 |
Descriptive statistics
| Standard deviation | 1884.3614 |
|---|---|
| Coefficient of variation (CV) | 0.99833052 |
| Kurtosis | 8.9271402 |
| Mean | 1887.5126 |
| Median Absolute Deviation (MAD) | 695 |
| Skewness | 2.5753493 |
| Sum | 224614 |
| Variance | 3550818 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 655 | 2 | 1.7% |
| 3450 | 2 | 1.7% |
| 707 | 2 | 1.7% |
| 411 | 2 | 1.7% |
| 1278 | 2 | 1.7% |
| 771 | 2 | 1.7% |
| 116 | 2 | 1.7% |
| 2975 | 2 | 1.7% |
| 278 | 2 | 1.7% |
| 2351 | 2 | 1.7% |
| Other values (90) | 99 |
| Value | Count | Frequency (%) |
| 116 | 2 | |
| 139 | 1 | |
| 166 | 1 | |
| 183 | 1 | |
| 201 | 2 | |
| 212 | 1 | |
| 255 | 1 | |
| 278 | 2 | |
| 349 | 1 | |
| 362 | 2 |
| Value | Count | Frequency (%) |
| 11817 | 1 | |
| 10008 | 1 | |
| 7761 | 1 | |
| 6610 | 1 | |
| 6564 | 1 | |
| 5799 | 1 | |
| 5055 | 1 | |
| 4604 | 1 | |
| 4221 | 1 | |
| 4176 | 1 |
From Explore
Real number (ℝ)
High correlation
| Distinct | 95 |
|---|---|
| Distinct (%) | 79.8% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 1078.1008 |
| Minimum | 0 |
|---|---|
| Maximum | 17414 |
| Zeros | 1 |
| Zeros (%) | 0.8% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 45 |
| Q1 | 157.5 |
| median | 326 |
| Q3 | 689.5 |
| 95-th percentile | 5380.2 |
| Maximum | 17414 |
| Range | 17414 |
| Interquartile range (IQR) | 532 |
Descriptive statistics
| Standard deviation | 2613.0261 |
|---|---|
| Coefficient of variation (CV) | 2.4237307 |
| Kurtosis | 24.753334 |
| Mean | 1078.1008 |
| Median Absolute Deviation (MAD) | 219 |
| Skewness | 4.7608149 |
| Sum | 128294 |
| Variance | 6827905.6 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 45 | 3 | 2.5% |
| 84 | 3 | 2.5% |
| 861 | 2 | 1.7% |
| 541 | 2 | 1.7% |
| 115 | 2 | 1.7% |
| 326 | 2 | 1.7% |
| 248 | 2 | 1.7% |
| 153 | 2 | 1.7% |
| 121 | 2 | 1.7% |
| 298 | 2 | 1.7% |
| Other values (85) | 97 |
| Value | Count | Frequency (%) |
| 0 | 1 | 0.8% |
| 29 | 1 | 0.8% |
| 36 | 1 | 0.8% |
| 37 | 1 | 0.8% |
| 45 | 3 | |
| 48 | 2 | |
| 51 | 2 | |
| 59 | 1 | 0.8% |
| 60 | 1 | 0.8% |
| 69 | 1 | 0.8% |
| Value | Count | Frequency (%) |
| 17414 | 1 | |
| 16444 | 1 | |
| 12389 | 1 | |
| 6000 | 1 | |
| 5762 | 1 | |
| 5634 | 1 | |
| 5352 | 1 | |
| 5192 | 1 | |
| 2355 | 2 | |
| 2266 | 1 |
From Other
Real number (ℝ)
High correlation
| Distinct | 84 |
|---|---|
| Distinct (%) | 70.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 171.09244 |
| Minimum | 9 |
|---|---|
| Maximum | 2547 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 9 |
|---|---|
| 5-th percentile | 20.7 |
| Q1 | 38 |
| median | 74 |
| Q3 | 196 |
| 95-th percentile | 570.4 |
| Maximum | 2547 |
| Range | 2538 |
| Interquartile range (IQR) | 158 |
Descriptive statistics
| Standard deviation | 289.43103 |
|---|---|
| Coefficient of variation (CV) | 1.6916647 |
| Kurtosis | 39.146608 |
| Mean | 171.09244 |
| Median Absolute Deviation (MAD) | 42 |
| Skewness | 5.3874322 |
| Sum | 20360 |
| Variance | 83770.322 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 34 | 4 | 3.4% |
| 73 | 3 | 2.5% |
| 36 | 3 | 2.5% |
| 32 | 3 | 2.5% |
| 26 | 3 | 2.5% |
| 65 | 3 | 2.5% |
| 72 | 3 | 2.5% |
| 139 | 3 | 2.5% |
| 18 | 2 | 1.7% |
| 39 | 2 | 1.7% |
| Other values (74) | 90 |
| Value | Count | Frequency (%) |
| 9 | 2 | |
| 15 | 1 | 0.8% |
| 17 | 1 | 0.8% |
| 18 | 2 | |
| 21 | 1 | 0.8% |
| 23 | 1 | 0.8% |
| 24 | 1 | 0.8% |
| 25 | 2 | |
| 26 | 3 | |
| 27 | 2 |
| Value | Count | Frequency (%) |
| 2547 | 1 | |
| 1115 | 1 | |
| 794 | 1 | |
| 792 | 1 | |
| 748 | 1 | |
| 655 | 1 | |
| 561 | 1 | |
| 536 | 1 | |
| 533 | 1 | |
| 532 | 1 |
Saves
Real number (ℝ)
High correlation
| Distinct | 84 |
|---|---|
| Distinct (%) | 70.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 153.31092 |
| Minimum | 22 |
|---|---|
| Maximum | 1095 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 22 |
|---|---|
| 5-th percentile | 35.9 |
| Q1 | 65 |
| median | 109 |
| Q3 | 169 |
| 95-th percentile | 472.5 |
| Maximum | 1095 |
| Range | 1073 |
| Interquartile range (IQR) | 104 |
Descriptive statistics
| Standard deviation | 156.31773 |
|---|---|
| Coefficient of variation (CV) | 1.0196125 |
| Kurtosis | 12.786458 |
| Mean | 153.31092 |
| Median Absolute Deviation (MAD) | 48 |
| Skewness | 3.1341324 |
| Sum | 18244 |
| Variance | 24435.233 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 40 | 4 | 3.4% |
| 135 | 4 | 3.4% |
| 144 | 4 | 3.4% |
| 111 | 3 | 2.5% |
| 42 | 3 | 2.5% |
| 106 | 2 | 1.7% |
| 49 | 2 | 1.7% |
| 74 | 2 | 1.7% |
| 101 | 2 | 1.7% |
| 82 | 2 | 1.7% |
| Other values (74) | 91 |
| Value | Count | Frequency (%) |
| 22 | 1 | 0.8% |
| 28 | 1 | 0.8% |
| 33 | 1 | 0.8% |
| 34 | 2 | |
| 35 | 1 | 0.8% |
| 36 | 1 | 0.8% |
| 38 | 2 | |
| 40 | 4 | |
| 41 | 1 | 0.8% |
| 42 | 3 |
| Value | Count | Frequency (%) |
| 1095 | 1 | |
| 668 | 2 | |
| 653 | 1 | |
| 573 | 1 | |
| 504 | 1 | |
| 469 | 1 | |
| 421 | 1 | |
| 393 | 1 | |
| 342 | 1 | |
| 318 | 1 |
Comments
Real number (ℝ)
Zeros
| Distinct | 15 |
|---|---|
| Distinct (%) | 12.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 6.6638655 |
| Minimum | 0 |
|---|---|
| Maximum | 19 |
| Zeros | 3 |
| Zeros (%) | 2.5% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 1.9 |
| Q1 | 4 |
| median | 6 |
| Q3 | 8 |
| 95-th percentile | 11.2 |
| Maximum | 19 |
| Range | 19 |
| Interquartile range (IQR) | 4 |
Descriptive statistics
| Standard deviation | 3.5445765 |
|---|---|
| Coefficient of variation (CV) | 0.53190996 |
| Kurtosis | 2.0099397 |
| Mean | 6.6638655 |
| Median Absolute Deviation (MAD) | 2 |
| Skewness | 0.94325674 |
| Sum | 793 |
| Variance | 12.564022 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=15)
| Value | Count | Frequency (%) |
| 6 | 17 | |
| 8 | 16 | |
| 4 | 14 | |
| 7 | 13 | |
| 5 | 12 | |
| 11 | 9 | |
| 9 | 8 | |
| 3 | 8 | |
| 10 | 5 | 4.2% |
| 2 | 5 | 4.2% |
| Other values (5) | 12 |
| Value | Count | Frequency (%) |
| 0 | 3 | 2.5% |
| 1 | 3 | 2.5% |
| 2 | 5 | 4.2% |
| 3 | 8 | |
| 4 | 14 | |
| 5 | 12 | |
| 6 | 17 | |
| 7 | 13 | |
| 8 | 16 | |
| 9 | 8 |
| Value | Count | Frequency (%) |
| 19 | 2 | 1.7% |
| 17 | 2 | 1.7% |
| 13 | 2 | 1.7% |
| 11 | 9 | |
| 10 | 5 | 4.2% |
| 9 | 8 | |
| 8 | 16 | |
| 7 | 13 | |
| 6 | 17 | |
| 5 | 12 |
Shares
Real number (ℝ)
High correlation Zeros
| Distinct | 28 |
|---|---|
| Distinct (%) | 23.5% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 9.3613445 |
| Minimum | 0 |
|---|---|
| Maximum | 75 |
| Zeros | 5 |
| Zeros (%) | 4.2% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 1 |
| Q1 | 3 |
| median | 6 |
| Q3 | 13.5 |
| 95-th percentile | 23.3 |
| Maximum | 75 |
| Range | 75 |
| Interquartile range (IQR) | 10.5 |
Descriptive statistics
| Standard deviation | 10.089205 |
|---|---|
| Coefficient of variation (CV) | 1.0777517 |
| Kurtosis | 15.613397 |
| Mean | 9.3613445 |
| Median Absolute Deviation (MAD) | 4 |
| Skewness | 3.1553217 |
| Sum | 1114 |
| Variance | 101.79205 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=28)
| Value | Count | Frequency (%) |
| 3 | 14 | 11.8% |
| 1 | 12 | 10.1% |
| 4 | 9 | 7.6% |
| 5 | 8 | 6.7% |
| 6 | 8 | 6.7% |
| 15 | 7 | 5.9% |
| 7 | 5 | 4.2% |
| 14 | 5 | 4.2% |
| 11 | 5 | 4.2% |
| 2 | 5 | 4.2% |
| Other values (18) | 41 |
| Value | Count | Frequency (%) |
| 0 | 5 | 4.2% |
| 1 | 12 | |
| 2 | 5 | 4.2% |
| 3 | 14 | |
| 4 | 9 | |
| 5 | 8 | |
| 6 | 8 | |
| 7 | 5 | 4.2% |
| 8 | 5 | 4.2% |
| 9 | 3 | 2.5% |
| Value | Count | Frequency (%) |
| 75 | 1 | 0.8% |
| 41 | 2 | |
| 38 | 1 | 0.8% |
| 27 | 1 | 0.8% |
| 26 | 1 | 0.8% |
| 23 | 1 | 0.8% |
| 22 | 2 | |
| 20 | 3 | |
| 19 | 1 | 0.8% |
| 18 | 2 |
Likes
Real number (ℝ)
High correlation
| Distinct | 85 |
|---|---|
| Distinct (%) | 71.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 173.78151 |
| Minimum | 72 |
|---|---|
| Maximum | 549 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 72 |
|---|---|
| 5-th percentile | 81.9 |
| Q1 | 121.5 |
| median | 151 |
| Q3 | 204 |
| 95-th percentile | 328 |
| Maximum | 549 |
| Range | 477 |
| Interquartile range (IQR) | 82.5 |
Descriptive statistics
| Standard deviation | 82.378947 |
|---|---|
| Coefficient of variation (CV) | 0.47403746 |
| Kurtosis | 4.2120269 |
| Mean | 173.78151 |
| Median Absolute Deviation (MAD) | 37 |
| Skewness | 1.7533936 |
| Sum | 20680 |
| Variance | 6786.2908 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 151 | 5 | 4.2% |
| 114 | 5 | 4.2% |
| 159 | 3 | 2.5% |
| 92 | 3 | 2.5% |
| 72 | 3 | 2.5% |
| 129 | 2 | 1.7% |
| 121 | 2 | 1.7% |
| 205 | 2 | 1.7% |
| 76 | 2 | 1.7% |
| 160 | 2 | 1.7% |
| Other values (75) | 90 |
| Value | Count | Frequency (%) |
| 72 | 3 | |
| 76 | 2 | |
| 81 | 1 | 0.8% |
| 82 | 1 | 0.8% |
| 85 | 1 | 0.8% |
| 86 | 2 | |
| 91 | 1 | 0.8% |
| 92 | 3 | |
| 94 | 1 | 0.8% |
| 95 | 1 | 0.8% |
| Value | Count | Frequency (%) |
| 549 | 1 | |
| 443 | 1 | |
| 416 | 2 | |
| 373 | 1 | |
| 328 | 2 | |
| 308 | 1 | |
| 301 | 1 | |
| 297 | 1 | |
| 294 | 1 | |
| 275 | 1 |
Profile Visits
Real number (ℝ)
High correlation
| Distinct | 59 |
|---|---|
| Distinct (%) | 49.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 50.621849 |
| Minimum | 4 |
|---|---|
| Maximum | 611 |
| Zeros | 0 |
| Zeros (%) | 0.0% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 4 |
|---|---|
| 5-th percentile | 8.9 |
| Q1 | 15 |
| median | 23 |
| Q3 | 42 |
| 95-th percentile | 186.6 |
| Maximum | 611 |
| Range | 607 |
| Interquartile range (IQR) | 27 |
Descriptive statistics
| Standard deviation | 87.088402 |
|---|---|
| Coefficient of variation (CV) | 1.7203718 |
| Kurtosis | 19.961194 |
| Mean | 50.621849 |
| Median Absolute Deviation (MAD) | 11 |
| Skewness | 4.1930725 |
| Sum | 6024 |
| Variance | 7584.3897 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=50)
| Value | Count | Frequency (%) |
| 21 | 6 | 5.0% |
| 19 | 6 | 5.0% |
| 26 | 5 | 4.2% |
| 14 | 5 | 4.2% |
| 20 | 4 | 3.4% |
| 11 | 4 | 3.4% |
| 16 | 4 | 3.4% |
| 8 | 4 | 3.4% |
| 10 | 4 | 3.4% |
| 9 | 4 | 3.4% |
| Other values (49) | 73 |
| Value | Count | Frequency (%) |
| 4 | 1 | 0.8% |
| 7 | 1 | 0.8% |
| 8 | 4 | |
| 9 | 4 | |
| 10 | 4 | |
| 11 | 4 | |
| 12 | 3 | |
| 13 | 2 | 1.7% |
| 14 | 5 | |
| 15 | 3 |
| Value | Count | Frequency (%) |
| 611 | 1 | |
| 467 | 1 | |
| 347 | 1 | |
| 330 | 1 | |
| 306 | 1 | |
| 237 | 1 | |
| 181 | 1 | |
| 155 | 1 | |
| 148 | 1 | |
| 144 | 1 |
Follows
Real number (ℝ)
High correlation Zeros
| Distinct | 29 |
|---|---|
| Distinct (%) | 24.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Infinite | 0 |
| Infinite (%) | 0.0% |
| Mean | 20.756303 |
| Minimum | 0 |
|---|---|
| Maximum | 260 |
| Zeros | 9 |
| Zeros (%) | 7.6% |
| Negative | 0 |
| Negative (%) | 0.0% |
| Memory size | 1.1 KiB |
Quantile statistics
| Minimum | 0 |
|---|---|
| 5-th percentile | 0 |
| Q1 | 4 |
| median | 8 |
| Q3 | 18 |
| 95-th percentile | 94.2 |
| Maximum | 260 |
| Range | 260 |
| Interquartile range (IQR) | 14 |
Descriptive statistics
| Standard deviation | 40.92158 |
|---|---|
| Coefficient of variation (CV) | 1.9715255 |
| Kurtosis | 18.268318 |
| Mean | 20.756303 |
| Median Absolute Deviation (MAD) | 6 |
| Skewness | 4.0398202 |
| Sum | 2470 |
| Variance | 1674.5757 |
| Monotonicity | Not monotonic |
Histogram with fixed size bins (bins=29)
| Value | Count | Frequency (%) |
| 2 | 17 | |
| 4 | 16 | |
| 6 | 16 | |
| 10 | 10 | 8.4% |
| 0 | 9 | 7.6% |
| 8 | 8 | 6.7% |
| 12 | 7 | 5.9% |
| 18 | 5 | 4.2% |
| 30 | 3 | 2.5% |
| 16 | 3 | 2.5% |
| Other values (19) | 25 |
| Value | Count | Frequency (%) |
| 0 | 9 | |
| 2 | 17 | |
| 4 | 16 | |
| 6 | 16 | |
| 8 | 8 | |
| 10 | 10 | |
| 12 | 7 | |
| 14 | 2 | 1.7% |
| 16 | 3 | 2.5% |
| 18 | 5 | 4.2% |
| Value | Count | Frequency (%) |
| 260 | 1 | |
| 228 | 1 | |
| 214 | 1 | |
| 100 | 2 | |
| 96 | 1 | |
| 94 | 2 | |
| 80 | 1 | |
| 74 | 1 | |
| 58 | 1 | |
| 46 | 1 |
Caption
Text
| Distinct | 90 |
|---|---|
| Distinct (%) | 75.6% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 31.6 KiB |
Length
| Max length | 784 |
|---|---|
| Median length | 217 |
| Mean length | 192.46218 |
| Min length | 44 |
Unique
| Unique | 63 ? |
|---|---|
| Unique (%) | 52.9% |
Sample
| 1st row | Here are some of the most important data visualizations that every Financial Data Analyst/Scientist should know. |
|---|---|
| 2nd row | Here are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects. |
| 3rd row | Learn how to train a machine learning model and giving inputs to your trained model to make predictions using Python. |
| 4th row | Heres how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence. |
| 5th row | Plotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python. |
| Value | Count | Frequency (%) |
| the | 192 | 4.9% |
| of | 163 | 4.1% |
| to | 141 | 3.6% |
| data | 125 | 3.2% |
| you | 120 | 3.0% |
| a | 96 | 2.4% |
| here | 86 | 2.2% |
| are | 85 | 2.2% |
| in | 76 | 1.9% |
| and | 64 | 1.6% |
| Other values (549) | 2795 |
Most occurring characters
| Value | Count | Frequency (%) |
| 3822 | ||
| e | 2350 | 10.3% |
| t | 1625 | 7.1% |
| a | 1607 | 7.0% |
| o | 1503 | 6.6% |
| n | 1394 | 6.1% |
| i | 1277 | 5.6% |
| s | 1234 | 5.4% |
| r | 1149 | 5.0% |
| l | 740 | 3.2% |
| Other values (63) | 6202 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 22903 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| 3822 | ||
| e | 2350 | 10.3% |
| t | 1625 | 7.1% |
| a | 1607 | 7.0% |
| o | 1503 | 6.6% |
| n | 1394 | 6.1% |
| i | 1277 | 5.6% |
| s | 1234 | 5.4% |
| r | 1149 | 5.0% |
| l | 740 | 3.2% |
| Other values (63) | 6202 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 22903 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| 3822 | ||
| e | 2350 | 10.3% |
| t | 1625 | 7.1% |
| a | 1607 | 7.0% |
| o | 1503 | 6.6% |
| n | 1394 | 6.1% |
| i | 1277 | 5.6% |
| s | 1234 | 5.4% |
| r | 1149 | 5.0% |
| l | 740 | 3.2% |
| Other values (63) | 6202 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 22903 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| 3822 | ||
| e | 2350 | 10.3% |
| t | 1625 | 7.1% |
| a | 1607 | 7.0% |
| o | 1503 | 6.6% |
| n | 1394 | 6.1% |
| i | 1277 | 5.6% |
| s | 1234 | 5.4% |
| r | 1149 | 5.0% |
| l | 740 | 3.2% |
| Other values (63) | 6202 |
Hashtags
Text
| Distinct | 54 |
|---|---|
| Distinct (%) | 45.4% |
| Missing | 0 |
| Missing (%) | 0.0% |
| Memory size | 71.4 KiB |
Length
| Max length | 406 |
|---|---|
| Median length | 328 |
| Mean length | 264.80672 |
| Min length | 153 |
Unique
| Unique | 30 ? |
|---|---|
| Unique (%) | 25.2% |
Sample
| 1st row | #finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
|---|---|
| 2nd row | #healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
| 3rd row | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels |
| 4th row | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects |
| 5th row | #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal |
| Value | Count | Frequency (%) |
| amankharwal | 117 | 5.2% |
| thecleverprogrammer | 117 | 5.2% |
| python | 109 | 4.8% |
| machinelearning | 97 | 4.3% |
| pythonprogramming | 95 | 4.2% |
| datascience | 94 | 4.2% |
| ai | 91 | 4.0% |
| pythonprojects | 90 | 4.0% |
| artificialintelligence | 89 | 3.9% |
| data | 88 | 3.9% |
| Other values (154) | 1270 |
Most occurring characters
| Value | Count | Frequency (%) |
| a | 3348 | 10.6% |
| e | 2732 | 8.7% |
| n | 2429 | 7.7% |
| # | 2256 | 7.2% |
| t | 2216 | 7.0% |
| i | 2144 | 6.8% |
| 2138 | 6.8% | |
| r | 1774 | 5.6% |
| c | 1564 | 5.0% |
| o | 1416 | 4.5% |
| Other values (22) | 9495 |
Most occurring categories
| Value | Count | Frequency (%) |
| (unknown) | 31512 |
Most frequent character per category
(unknown)
| Value | Count | Frequency (%) |
| a | 3348 | 10.6% |
| e | 2732 | 8.7% |
| n | 2429 | 7.7% |
| # | 2256 | 7.2% |
| t | 2216 | 7.0% |
| i | 2144 | 6.8% |
| 2138 | 6.8% | |
| r | 1774 | 5.6% |
| c | 1564 | 5.0% |
| o | 1416 | 4.5% |
| Other values (22) | 9495 |
Most occurring scripts
| Value | Count | Frequency (%) |
| (unknown) | 31512 |
Most frequent character per script
(unknown)
| Value | Count | Frequency (%) |
| a | 3348 | 10.6% |
| e | 2732 | 8.7% |
| n | 2429 | 7.7% |
| # | 2256 | 7.2% |
| t | 2216 | 7.0% |
| i | 2144 | 6.8% |
| 2138 | 6.8% | |
| r | 1774 | 5.6% |
| c | 1564 | 5.0% |
| o | 1416 | 4.5% |
| Other values (22) | 9495 |
Most occurring blocks
| Value | Count | Frequency (%) |
| (unknown) | 31512 |
Most frequent character per block
(unknown)
| Value | Count | Frequency (%) |
| a | 3348 | 10.6% |
| e | 2732 | 8.7% |
| n | 2429 | 7.7% |
| # | 2256 | 7.2% |
| t | 2216 | 7.0% |
| i | 2144 | 6.8% |
| 2138 | 6.8% | |
| r | 1774 | 5.6% |
| c | 1564 | 5.0% |
| o | 1416 | 4.5% |
| Other values (22) | 9495 |
Interactions
Correlations
| Comments | Follows | From Explore | From Hashtags | From Home | From Other | Impressions | Likes | Profile Visits | Saves | Shares | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Comments | 1.000 | -0.079 | 0.015 | 0.155 | 0.331 | -0.134 | 0.225 | 0.306 | 0.029 | 0.183 | 0.134 |
| Follows | -0.079 | 1.000 | 0.482 | 0.562 | 0.350 | 0.617 | 0.762 | 0.568 | 0.758 | 0.435 | 0.228 |
| From Explore | 0.015 | 0.482 | 1.000 | 0.236 | 0.457 | 0.259 | 0.609 | 0.531 | 0.325 | 0.647 | 0.422 |
| From Hashtags | 0.155 | 0.562 | 0.236 | 1.000 | 0.118 | 0.377 | 0.783 | 0.625 | 0.584 | 0.395 | 0.251 |
| From Home | 0.331 | 0.350 | 0.457 | 0.118 | 1.000 | 0.177 | 0.541 | 0.705 | 0.245 | 0.705 | 0.576 |
| From Other | -0.134 | 0.617 | 0.259 | 0.377 | 0.177 | 1.000 | 0.473 | 0.384 | 0.605 | 0.300 | 0.283 |
| Impressions | 0.225 | 0.762 | 0.609 | 0.783 | 0.541 | 0.473 | 1.000 | 0.854 | 0.654 | 0.688 | 0.465 |
| Likes | 0.306 | 0.568 | 0.531 | 0.625 | 0.705 | 0.384 | 0.854 | 1.000 | 0.485 | 0.850 | 0.569 |
| Profile Visits | 0.029 | 0.758 | 0.325 | 0.584 | 0.245 | 0.605 | 0.654 | 0.485 | 1.000 | 0.258 | 0.113 |
| Saves | 0.183 | 0.435 | 0.647 | 0.395 | 0.705 | 0.300 | 0.688 | 0.850 | 0.258 | 1.000 | 0.618 |
| Shares | 0.134 | 0.228 | 0.422 | 0.251 | 0.576 | 0.283 | 0.465 | 0.569 | 0.113 | 0.618 | 1.000 |
Missing values
A simple visualization of nullity by column.
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
Sample
| Impressions | From Home | From Hashtags | From Explore | From Other | Saves | Comments | Shares | Likes | Profile Visits | Follows | Caption | Hashtags | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 3920 | 2586 | 1028 | 619 | 56 | 98 | 9 | 5 | 162 | 35 | 2 | Here are some of the most important data visualizations that every Financial Data Analyst/Scientist should know. | #finance #money #business #investing #investment #trading #stockmarket #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
| 1 | 5394 | 2727 | 1838 | 1174 | 78 | 194 | 7 | 14 | 224 | 48 | 10 | Here are some of the best data science project ideas on healthcare. If you want to become a data science professional in the healthcare domain then you must try to work on these projects. | #healthcare #health #covid #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #dataanalyst #amankharwal #thecleverprogrammer |
| 2 | 4021 | 2085 | 1188 | 0 | 533 | 41 | 11 | 1 | 131 | 62 | 12 | Learn how to train a machine learning model and giving inputs to your trained model to make predictions using Python. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels |
| 3 | 4528 | 2700 | 621 | 932 | 73 | 172 | 10 | 7 | 213 | 23 | 8 | Heres how you can write a Python program to detect whether a sentence is a question or not. The idea here is to find the words that we see in the beginning of a question in the beginning of a sentence. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects |
| 4 | 2518 | 1704 | 255 | 279 | 37 | 96 | 5 | 4 | 123 | 8 | 0 | Plotting annotations while visualizing your data is considered good practice to make the graphs self-explanatory. Here is an example of how you can annotate a graph using Python. | #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #artificialintelligence #python #datascientist #bigdata #deeplearning #dataviz #ai #analytics #technology #dataanalyst #programming #pythonprogramming #statistics #coding #businessintelligence #datamining #tech #business #computerscience #tableau #database #thecleverprogrammer #amankharwal |
| 5 | 3884 | 2046 | 1214 | 329 | 43 | 74 | 7 | 10 | 144 | 9 | 2 | Here are some of the most important soft skills that every data scientist should have. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #machinelearningalgorithms #ml #amankharwal #thecleverprogrammer #softskills |
| 6 | 2621 | 1543 | 599 | 333 | 25 | 22 | 5 | 1 | 76 | 26 | 0 | Learn how to analyze a candlestick chart as a data scientist or a financial analyst. I hope this resource will help you to invest and analyze stock markets. | #stockmarket #investing #stocks #trading #money #investment #finance #forex #datavisualization #datascience #data #dataanalytics #machinelearning #dataanalysis #ai #candlestick #candlestickcharts |
| 7 | 3541 | 2071 | 628 | 500 | 60 | 135 | 4 | 9 | 124 | 12 | 6 | Here are some of the best books that you can follow to learn Python from scratch. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects #pythonbooks #bookstagram |
| 8 | 3749 | 2384 | 857 | 248 | 49 | 155 | 6 | 8 | 159 | 36 | 4 | Here are some of the best data analysis project ideas that you should try and show on your resume. These projects will help you to show your data analysis skills. | #dataanalytics #datascience #data #machinelearning #datavisualization #bigdata #artificialintelligence #datascientist #python #analytics #ai #dataanalysis #deeplearning #technology #programming #coding #dataanalyst #business #pythonprogramming #datamining #tech #businessintelligence #database #computerscience #statistics #powerbi #dataanalysisprojects #businessanalytics #thecleverprogrammer #amankharwal |
| 9 | 4115 | 2609 | 1104 | 178 | 46 | 122 | 6 | 3 | 191 | 31 | 6 | Here are two best ways to count the number of letters in a string using Python. | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects |
| Impressions | From Home | From Hashtags | From Explore | From Other | Saves | Comments | Shares | Likes | Profile Visits | Follows | Caption | Hashtags | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 109 | 17713 | 2449 | 2141 | 12389 | 561 | 504 | 3 | 23 | 308 | 70 | 96 | Here are some of the best resources to learn SQL for data science. | #sql #mysql #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer |
| 110 | 5563 | 3813 | 362 | 1135 | 76 | 149 | 5 | 8 | 163 | 22 | 20 | Here are the best Python libraries for data visualization that you should learn for data science. | #datavisualization #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer |
| 111 | 4842 | 1658 | 694 | 2036 | 310 | 55 | 6 | 4 | 86 | 46 | 30 | Learn how to create an interactive language translator using the Python programming language. | #python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer #nlp #naturallanguageprocessing |
| 112 | 11149 | 4439 | 747 | 5762 | 53 | 273 | 4 | 13 | 210 | 61 | 58 | Python is one of the best programming languages for numerical calculations. So you should know how to calculate mean, median and mode using Python without using any built-in Python library or module. Heres how to calculate mean, median, and mode using Python. | #python #pythonprogramming #pythoncode #pythonlearning #pythondeveloper #pythonprogrammer #pythonprojects #python3 #pythoncoding #pythonprogramminglanguage #amankharwal #thecleverprogrammer |
| 113 | 10206 | 2371 | 1624 | 6000 | 117 | 182 | 10 | 17 | 172 | 237 | 100 | Practice these 90+ Data Science Projects For Beginners Solved & Explained using Python. Find all these projects from the link in bio. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer |
| 114 | 13700 | 5185 | 3041 | 5352 | 77 | 573 | 2 | 38 | 373 | 73 | 80 | Here are some of the best data science certifications that you can choose from in 2022. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer |
| 115 | 5731 | 1923 | 1368 | 2266 | 65 | 135 | 4 | 1 | 148 | 20 | 18 | Clustering is a machine learning technique used to classify data points, charaterized by some specific features into groups. It is an unsupervised machine learning method where the data we deal with is not labelled. Here are some of the best Machine Learning project ideas on Clustering that you should try. | #machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering |
| 116 | 4139 | 1133 | 1538 | 1367 | 33 | 36 | 0 | 1 | 92 | 34 | 10 | Clustering music genres is a task of grouping music based on the similarities in their audio characteristics. Here you will learn how to do clustering analysis of music genres with Machine Learning using Python. | #machinelearning #machinelearningalgorithms #datascience #dataanalysis #dataanalytics #datascientist #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #amankharwal #thecleverprogrammer #clustering |
| 117 | 32695 | 11815 | 3147 | 17414 | 170 | 1095 | 2 | 75 | 549 | 148 | 214 | Here are some of the best data science certifications that you can choose from in 2022. | #datascience #datasciencejobs #datasciencetraining #datascienceeducation #datasciencecourse #data #dataanalysis #dataanalytics #datascientist #machinelearning #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer |
| 118 | 36919 | 13473 | 4176 | 16444 | 2547 | 653 | 5 | 26 | 443 | 611 | 228 | 175 Python Projects with Source Code solved and explained for free: Link in Bio | #python #pythonprogramming #pythonprojects #pythoncode #pythonlearning #pythondeveloper #pythoncoding #pythonprogrammer #amankharwal #thecleverprogrammer #pythonprojects |
Duplicate rows
Most frequently occurring
| Impressions | From Home | From Hashtags | From Explore | From Other | Saves | Comments | Shares | Likes | Profile Visits | Follows | Caption | Hashtags | # duplicates | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2407 | 1338 | 655 | 276 | 39 | 40 | 8 | 20 | 72 | 10 | 0 | Data Science Use Cases: Heres how Zomato is using your data for its future business model. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels #zomato #business #casestudy #businessmodel | 2 |
| 1 | 2766 | 2541 | 116 | 51 | 9 | 40 | 10 | 4 | 114 | 11 | 6 | Here are all the programming languages that Facebook uses in the Front-end and the back-end of Facebook. | #programming #coding #programmer #python #developer #javascript #technology #code #coder #java #html #computerscience #tech #css #webdeveloper #software #webdevelopment #codinglife #softwaredeveloper #linux #programmingmemes #webdesign #programmers #php #programminglife #machinelearning #hacking #pythonprogramming #thecleverprogrammer #amankharwal | 2 |
| 2 | 2826 | 2108 | 583 | 76 | 34 | 67 | 3 | 3 | 114 | 30 | 4 | Visualizing data is one of the most valuable skills every Data Scientist and Analyst should have. There are a lot of data visualizations to learn in data science. Here you will find a list of all data visualizations for data science explained using Python. | #dataanalytics #datascience #data #machinelearning #datavisualization #bigdata #artificialintelligence #datascientist #python #analytics #ai #dataanalysis #deeplearning #technology #programming #coding #dataanalyst #business #pythonprogramming #datamining #tech #businessintelligence #database #computerscience #statistics #powerbi #dataanalysisprojects #businessanalytics #thecleverprogrammer #amankharwal | 2 |
| 3 | 2998 | 1945 | 794 | 84 | 139 | 42 | 4 | 1 | 126 | 31 | 10 | Time series analysis means analyzing and finding patterns in a time series dataset. A time-series dataset is a sequence of data collected over an interval of time. Here you will learn how to do Time Series Analysis using Python. | #timeseries #time #statistics #datascience #bigdata #machinelearning #python #ai #timeseriesanalysis #datavisualization #dataanalytics #data #iot #analysis #timeseriesmalaysia #artificialintelligence #analytics #amankharwal #thecleverprogrammer | 2 |
| 4 | 3015 | 2034 | 771 | 115 | 41 | 52 | 11 | 4 | 92 | 9 | 2 | Heres how Amazon uses your data as an e-commerce platform. I hope this post will help you to understand how Amazon is using data science to improve the quality of its services. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer #machinelearningmodels #zomato #business #casestudy #businessmodel #amazonfinds | 2 |
| 5 | 3052 | 2608 | 201 | 121 | 87 | 63 | 5 | 14 | 129 | 14 | 2 | Here are all the programming languages that Google uses in the Front-end and the back-end of Google. | #programming #coding #programmer #python #developer #javascript #technology #code #coder #java #html #computerscience #tech #css #webdeveloper #software #webdevelopment #codinglife #softwaredeveloper #linux #programmingmemes #webdesign #programmers #php #programminglife #machinelearning #hacking #pythonprogramming #thecleverprogrammer #amankharwal | 2 |
| 6 | 3169 | 1979 | 707 | 341 | 32 | 106 | 8 | 1 | 121 | 21 | 2 | In Data Science, Time Series Analysis is a method of analyzing data collected over an interval of time. Stock price data and covid-19 cases data are examples of time-series data. Time Series Analysis helps understand the underlying causes of trends and patterns at particular time intervals. So it is one of the topics that every data scientist should know perfectly. So if you are looking for some of the best data science project ideas on Time Series Analysis, you should try to work on these projects. | #timeseries #time #statistics #datascience #bigdata #machinelearning #python #ai #timeseriesanalysis #datavisualization #dataanalytics #data #iot #analysis #timeseriesmalaysia #artificialintelligence #analytics #amankharwal #thecleverprogrammer | 2 |
| 7 | 3630 | 1747 | 1693 | 72 | 86 | 137 | 4 | 10 | 137 | 14 | 4 | Here are some of the best data analysis project ideas for resume that you should try. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #machinelearningprojects #datascienceprojects #amankharwal #thecleverprogrammer | 2 |
| 8 | 3924 | 2244 | 1278 | 326 | 34 | 139 | 11 | 3 | 151 | 19 | 2 | Here are some of the most popular data science case studies and projects that every data science beginner should try. You can find these case studies and projects with solutions at Kaggle. | #data #datascience #dataanalysis #dataanalytics #datascientist #machinelearning #python #pythonprogramming #pythonprojects #pythoncode #artificialintelligence #ai #deeplearning #algorithm #algorithms #machinelearningalgorithms #ml #amankharwal #thecleverprogrammer #projects #casestudies | 2 |
| 9 | 4002 | 3401 | 278 | 128 | 73 | 111 | 17 | 18 | 205 | 16 | 2 | Here are some of the highest paying skills in 2022 that you should start learning today. These skills do not require you to come from a specific education background. If you are passionate about learning any of these skills, then you can easily find so many free resources on the internet. If you dont know how to find free resources to learn these skills, feel free to reach me @aman.kharwal, I will be happy to guide you. | #career #job #jobs #jobsearch #education #business #success #careergoals #motivation #work #careerdevelopment #careers #goals #resume #students #careeradvice #datascience #marketing #digitalmarketing #media #socialmedia #IT #webdevelopment #amankharwal #thecleverprogrammer | 2 |