10 / 2019

TIBCO® Data Science Software 1.2

Improvements for TIBCO® Data Science - Workbench (formerly TIBCO Statistica®) and TIBCO® Spotfire software include:

TIBCO® Data Science - Statistica® renamed to TIBCO® Data Science - Workbench®

TIBCO® Data Science Service for TIBCO Spotfire® Software

Executes no-code analytics in a browser, provides integration between Workbench and Spotfire Consumer/Business Author users.

TIBCO® Data Science for TIBCO Spotfire® Analyst (formerly known as Statistica Extension)

  • If TIBCO Data Science Service for TIBCO Spotfire is installed on the server, it provides the ability to execute a data function on Spotfire Server.
  • If the Statistica Workspace path is not embedded, it can be changed for a Statistica® (now Workbench) data function.
  • Allows user to review, modify/update workspaces, and link to updated data function.
  • Extracts embedded workspace, allows user to modify / update embedded data functions.
  • Provides "smart memory" input/output of data tables in a data function, changes the workspace inputs/outputs and/or adds nodes and retains the relevant data function settings (and can be modified).
  • Implements Spotfire trust mechanism. Requires Spotfire 10.3 or newer.

08 / 2019

TIBCO® Data Science software 1.1

Improvements are for TIBCO® Data Science - Team Studio software.

Expanded Cloud Data Source Connectivity:

  • Azure SQL Data Warehouse
  • Google BigQuery
  • Oushu 3.1
  • Cloudera 6
  • Google DataProc
  • Greenplum Database 5

Running Python in Jupyter Notebooks for Team Studio on JupyterHub

You can now run the Python engine as a local managed process in Jupyter Notebooks for Team Studio, in addition to running it in a Docker container.

Integration with TIBCO Statistica

TIBCO Statistica™ workflow files (*.sdm) are now recognized and filterable through the Workfile browser.

Additional Operators


Takes an HDFS dataset in stacked format and produces an unstacked (wide) HDFS dataset using user-specified grouping and pivot columns.

Line Chart

Produces a line chart for a user-specified X-axis and Y-axis, and aggregates the values in the Y-axis.

Generalized Linear Regression Models

Fits a regression model to predict a dependent variable that follows some distribution from the exponential family of distributions.

11 / 2018

TIBCO Data Science

TIBCO Data Science software, available on AWS Marketplace, combines the capabilities of Statistica™software, TIBCO SpotfireⓇ Data Science software (formerly Alpine Data), and TIBCO® Enterprise Runtime for R (TERR) software in a single unified platform. The TIBCO Data Science solution extends team collaboration, automation, common metadata, project management, and model operations.

Innovation and collaboration capabilities

  • Easily spin up big data sandboxes in the AWS cloud
  • Get accelerated performance with Spark Node Fusion that chains Hadoop operations to process workflows in half the time or less
  • Use new operators and algorithms: rename and reorder columns; use Chi Square tests, categorical encoding for using non-numerical features in algorithms, and sessionization for identifying user sessions in log files; store common words and phrases in reference documents; edit and run multiple workflows simultaneously; export to FTP, Spotfire, and Excel; and much more

05 / 2018

TIBCO Statistica 13.4

With the release of this new version, it’s even easier to deploy sophisticated analytic models to real-time scoring environments and empower citizen data scientists:

  • Integrate with Spotfire software to run Statistica workspaces via Spotfire data functions
  • Access data from Spotfire software by importing a Spotfire workspace node (.sbdf) and exporting data back to Spotfire analytics
  • Reuse Spotfire data connections by accessing them directly from a workspace
  • Publish PMML models to TIBCO StreamBase® streaming analytics, which uses the TIBCO® Artifact Management Server
  • Use Elasticsearch text analytics to process unstructured data
  • Use support of OSI PI event frames and asset framework data
  • Use more analytics, including Lasso regression (feature selection method) and dynamic time warping to align IoT sensor data