Coheris Spad by ChapsVision is a powerful, self-service data analysis studio designed for data scientists across all sectors. It supports a wide range of data mining and machine learning tasks including segmentation, prediction, anomaly detection, recommendation, and text analysis. With an intuitive graphical interface and scalable infrastructure, Coheris Spad enables fast, agile, and comprehensive data exploration and modeling.

Features

• Graphical Interface:
  • Fully visual data processing chain.
  • Intuitive ergonomics for quick autonomy without coding.
  • Default settings aligned with common usage, easily modifiable.
• Data Preparation:
  • Connects to multiple data sources.
  • Rich library of functions: filtering, stacking, aggregation, transposition, joins, missing data handling, statistical recoding, formatting.
• Descriptive Statistics:
  • Univariate and bivariate analysis.
  • Graphical visualizations and statistical tests.
  • Automatic variable characterization.
  • Factor analysis (PCA, AFC, ACM, AFM).
  • Classifications (hierarchical, K-Means, mixed).
• Statistical Modeling:
  • Regressions (simple, multiple, logistic, PLS).
  • Discriminant analysis, time series.
  • Machine learning algorithms: decision trees, Bayesian networks, neural networks, SVM, random forests, collaborative filtering.
• Text Mining:
  • Language detection, lemmatization, vocabulary building.
  • Word clouds, lexical contingency tables.
  • Automatic text grouping and survey response analysis.
• Scripting Integration:
  • Supports R and Python for model extension and custom analytics.
• Output & Visualization:
  • Results formatted for spreadsheets or web browsers.
  • Interactive graphs: factorial plans, decision trees, image balances, scores.
  • Guides for result interpretation.

Benefits

• Scalable & Agile:
  • Handles large data volumes with high performance.
• Modular & Extensible:
  • Adapts to diverse analytical needs and integrates with other ChapsVision or external applications.
• User-Friendly:
  • No programming required; ideal for rapid deployment and use.
• Comprehensive Methodology:
  • Covers full spectrum of data analysis from preparation to modeling.
• Educational Recognition:
  • Taught in major universities and schools, widely respected in the data science community.
• Real-Time Insights:
  • Accelerates decision-making and boosts business growth through timely data use.