Data analysis. Without the pain.
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60+ Methods
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Chapter 1 Your data, your way.
SPREADSHEET EDITOR
customers.csv 100 rows · 4 cols
Name
Age #
City
Purchase
Alice Johnson
25
New York
$1,234
Bob Smith
30
Los Angeles
$2,567
Charlie Brown
28
San Francisco
$890
Diana Prince
35
New York
$3,421
Eve Wilson
42
Chicago
$1,876
But your data is messy. Missing values. Fixed in seconds.
SMART CLEANING
Missing Values 3 columns affected
Age
Number3 missing
Mean fill
Income
Number5 missing
Median fill
City
Text2 missing
Mode fill
Clean. Now transform. Normalize · Encode · Export.
TRANSFORM & EXPORT
Transform Columns Select method
㏑
Log
ln(x)
42→3.74
√
Sqrt
√x
42→6.48
Z
Z-Score
(x-μ)/σ
42→1.24
⇅
Min-Max
[0,1]
42→0.87
{}
One-Hot
encode
[1,0,0]
|x|
Abs
|x|
-3→3
↓ CSV
↓ Excel
↓ JSON
↩ 50-level undo
Chapter 2 60+ analyses. Every method your team will ever need.
STATISTICAL METHODS
Select Analysis 60+ methods
Descriptive Stats
Frequency Analysis
T-Test
ANOVA
Chi-Square
Correlation
Regression
Random Forest
Decision Tree
K-Means
ARIMA
PCA
Factor Analysis
Gradient Boosting
Logistic Regression
DBSCAN
Don't understand your results? Just ask.
AI ANALYSIS ASSISTANT
AI
Analysis Assistant
Online · responds instantly
Two-Way ANOVA
VISUAL SUMMARY
Visual Summary Elbow · Silhouette · PCA · Distribution
Elbow Method for Optimal k
150 100 50 20 0 2 3 4 5 6 7 8 9 Number of Clusters (k) Elbow k=3
Silhouette Scores by k
0.7 0.5 0.3 0.1 0.0 2 3 4 5 6 7 8 9 Number of Clusters (k)
Clusters (PCA projection — 94.8% variance explained)
PC1 (59.1%) PC2 (35.7%) 3 1 0 -1 -3 -1 1 3 Cluster 1 Cluster 2 Cluster 3 Centroid
Cluster Distribution
n=48 Cluster 1 Cluster 2 Cluster 3 50% 25% 25%
ANALYSIS COMPLETE
Result Summary
Key findings from K-Means clustering
Key Findings
Successfully segmented 48 data points into 3 distinct groups.
Largest segment: Cluster 1 — 24 members (50.0% of total).
Cluster quality: Good (Silhouette = 0.658) — distinct with minor boundary overlap.
💡 Suggestion: Try k = 4 for comparison.
Good Clustering Quality
Clusters are reasonably well-separated with minor boundary overlap.
Supporting Evidence
Silhouette 0.658 — good: most points are closer to their own cluster
Calinski-Harabász 98 (~33 per cluster) — moderate density, acceptable
Davies-Bouldin 0.498 — excellent: clusters compact and clearly separated
Quality: ☆☆☆☆☆ (Good)
AUTOMATIC ASSUMPTION CHECKS
Validation Results Scanning...
↻
Normality Test
Running Shapiro-Wilk...
↻
Homogeneity of Variance
Running Levene's test...
↻
Outlier Detection
Scanning for outliers...
↻
Sample Size
Checking adequacy...
↻
Missing Data
Scanning dataset...
Results. Publication-ready. APA format, automatically.
APA FORMAT · AUTO-GENERATED
Two-Way ANOVA
APA Result
A two-way ANOVA revealed a statistically significant main effect of Group, F(2, 94) = 8.43, p = .001, η² = .15, indicating a large effect.
Effect Size
η² = .15
Large effect
p = .001
Significant
95% CI
[2.14, 8.76]
Interpretation
Group differences are statistically significant and practically meaningful. Post-hoc tests recommended.
Analysis done. Download it. PDF · Word · Python — in under 2 minutes.
EXPORT REPORT
Analysis Report Building...
01 Executive Summary
85%
02 Methodology
65%
03 Statistical Results
95%
04 Interpretation
75%
📄
PDF
Publication-ready
📝
Word
Fully editable
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Python
Reproducible code
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R
Reproducible code
Trust the results. Not just the numbers.
One platform. Every analysis.
SKARI