Our products

Comparison table

Platform

DataStories Platform

SDK

DataStories Library for Jupyter and Python users

Target usersBusiness users, scientists and engineersData scientists and data engineers
ApplicationsExploration, research and data discovery, business decision supportProgrammatic automation and industrialization

Deployment

Cloud – public and private
Local/user laptop
Server or HPC

Data Input

Data Input FormatExcel, CSV, TXT
Minimum Data Requirements30 rows
Maximum File Size For Uploads40 MB

Data diagnostics

Input Data VisualizationHistogram and Box Plots
Missing Data ResolutionAutomatic
ScalingAutomatic
BalancingAutomatic

Modeling Settings and Processing

KPI data type supportedRegression, Classification
Modeling ProblemsSelf-adjusting algorithm applied automatically
Algorithm Selection
Outlier detection and elimination
Set modeling iterations
Extrapolation
Auto Data Compression

Output

Linear Correlations
Non-linear relationships
Set correlation strength threshold for visualization of relationships
What-if scenarios
Individual Run Inspection
Model validation
Model exportC/C++, Excel, Matlab, Python or R
DataStory exportPowerPoint or Matlab Toolbox
Generate reports
Story Sharing
Integration
Deployment
Operationalization
Platform

DataStories Platform

SDK

DataStories Library for Jupyter and Python users

Target usersBusiness users, scientists and engineersData scientists and data engineers
ApplicationsExploration, research and data discovery, business decision supportProgrammatic automation and industrialization

Deployment

Cloud – public and private
Local/user laptop
Server or HPC

Data Input

Data Input FormatExcel, CSV, TXT
Minimum Data Requirements30 rows
Maximum File Size For Uploads40 MB

Data diagnostics

Input Data VisualizationHistogram and Box Plots
Missing Data ResolutionAutomatic
ScalingAutomatic
BalancingAutomatic

Modeling Settings and Processing

KPI data type supportedRegression, Classification
Modeling ProblemsSelf-adjusting algorithm applied automatically
Algorithm Selection
Outlier detection and elimination
Set modeling iterations
Extrapolation
Auto Data Compression

Output

Linear Correlations
Non-linear relationships
Set correlation strength threshold for visualization of relationships
What-if scenarios
Individual Run Inspection
Model validation
Model exportC/C++, Excel, Matlab, Python or R
DataStory exportPowerPoint or Matlab Toolbox
Generate reports
Story Sharing
Integration
Deployment
Operationalization