1
Spotfire, formerly known as TIBCO Spotfire, is a visual data science platform that combines visual analytics, data science, and data wrangling, so users can analyze data at-rest and at-scale to solve complex industry-specific problems.
Best Data Science Platforms 2026
What are Data Science Platforms? Data Science Platforms build predictive models using code, Machine Learning, and vast amounts of data. These models facilitate the creation of business solutions. Data science modeling can predict events and outcomes in the real world and answer questions such as ‘how much product will be sold next year?’, ‘how to optimize supply chain delivery times?’, or ‘how to reduce customer churn?’. The accuracy of the modeling depends on how well the full scope of inputs ...
We’ve collected videos, features, and capabilities below. Take me there.
All Products(1-25 of 72)
- 2
KNIME enables users to analyze, upskill, and scale data science without any coding. The platform that lets users blend, transform, model and visualize data, deploy and monitor analytical models, and share insights organization-wide with data apps and services. - 3
The Alteryx AI Platform gives organization automated data preparation, AI-powered analytics, and machine learning with embedded governance and security.Its self-service functionality, with self-service data prep, machine learning, and AI-generated insights, gives enterprise teams with a simplified ... - 4
Saturn Cloud is an ML platform for individuals and teams, available on multiple clouds: AWS, Azure, GCP, and OCI. It provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a wholly hosted environment. Saturn ... - 5
Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products. - 6
The DataRobot AI Platform is presented as a solution that accelerates and democratizes data science by automating the end-to-end journey from data to value and allows users to deploy AI applications at scale. DataRobot provides a centrally governed platform that gives users AI to drive business ... - 7
Jupyter Notebook is an open-source web application that allows users to create and share documents containing live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine ... - 8
Anaconda provides access to the foundational open-source Python and R packages used in modern AI, data science, and machine learning. These enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness open-source for competitive advantage and ... - 9
RapidMiner is a data science and data mining platform, from Altair since the late 2022 acquisition. RapidMiner offers full automation for non-coding domain experts, an integrated JupyterLab environment for seasoned data scientists, and a visual drag-and-drop designer. RapidMiner’s project-based ... - 10
IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and ... - 11
MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia. - 12
The Dataiku platform unifies all data work, from analytics to Generative AI. It can modernize enterprise analytics and accelerate time to insights with visual, cloud-based tooling for data preparation, visualization, and workflow automation. - 13
Wolfram's flagship product Mathematica is a modern technical computing application featuring a flexible symbolic coding language and a wide array of graphing and data visualization capabilities. - 14
IBM® ILOG® CPLEX® Optimization Studio is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming. - 15
The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality and impact of data science at scale. Domino is presented as open and flexible, to empower professional data scientists to use their preferred tools and infrastructure. Data science models get into production ... - 16
IBM Watson Studio enables users to build, run and manage AI models, and optimize decisions at scale across any cloud. IBM Watson Studio enables users can operationalize AI anywhere as part of IBM Cloud Pak® for Data, the IBM data and AI platform. The vendor states the solution simplifies AI ... - 17
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data ... - 18
Mode, or Mode Analytics, from ThoughtSpot since the June 2023 acquisition, is a business intelligence platform that unifies company analytics by bringing data teams and business teams together, so analysts can provide rapid answers to strategic, ad hoc questions. And, business stakeholder can ... - 19
cnvrg.io presents their technology as the fastest way to get from research to production - cnvrg.io is an end-to-end machine learning platform that helps teams manage, build and automate machine learning pipelines. - 20
Toric is a smart data-workspace where the user can leverage all data in one place.Toric is used by Architecture & Planning, Civil Engineering, Construction, Real Estate operations, and developers to leverage their existing data. Using Toric, companies can combine design, project, and finance ... - 21
IBM Spectrum Discover is modern metadata management software that provides data insight for exabyte-scale unstructured storage. IBM Spectrum Discover connects to IBM Cloud Object Storage System and IBM Spectrum Scale to rapidly ingest, consolidate and index metadata for billions of files and ... - 22
Deepnote is a data science notebook. Jupyter-compatible with real-time collaboration and running in the cloud. - 23
Einblick is a visual data computing platform that provides a way for organizations to understand the past, predict the future and make the best data-driven decisions for their business. Its AutoML is focused on helping users to create explainable predictions and identify key drivers without fuss. ... - 24
SAS Enterprise Miner is a data science and statistical modeling solution enabling the creation of predictive and descriptive models on very large data sources across the organization.
Learn More about Data Science Software
What are Data Science Platforms?
Data Science Platforms build predictive models using code, Machine Learning, and vast amounts of data. These models facilitate the creation of business solutions. Data science modeling can predict events and outcomes in the real world and answer questions such as ‘how much product will be sold next year?’, ‘how to optimize supply chain delivery times?’, or ‘how to reduce customer churn?’. The accuracy of the modeling depends on how well the full scope of inputs is understood, and the completeness and accuracy of data.
Their tools can handle volumes of structured and unstructured data that are not supported by traditional databases and statistical software. Data Science Platforms centralize access to multiple data sources and a variety of data science tools, including open-source tools.
These platforms are used by data scientists who capture, clean, and visualize data and use statistical analysis, machine learning capabilities, and coding skills to produce their models. It enables them to run, track, and deploy models. Machine learning engineers help data scientists scale and optimize their models. These platforms are also used by data engineers, citizen data scientists who leverage user-friendly features, and business stakeholders to better understand their business. The platforms readily support model iterations based on stakeholder feedback.
Data Science Tools
Individual data science tools are also included in this category. Data science tools do the same work as data science platforms, helping data scientists to capture, clean, analyze, and visualize data. Individual data science tools may be more limited in scope, assisting with only a portion of this process, or supporting only one programming language, while data science platforms usually support the entire data science process and multiple programming languages.
Difference Between Business Intelligence and Data Science
Business Intelligence uses data analytics to understand how the business has been performing, driving decision-making that is based on past results. This intelligence is used to operate a business in the most effective manner. Data science creates models to predict future performance, and the information these models provide helps transform a business to align with its goals.
Data Science Platforms Features
Data Science Platforms will have many of these features.
- Integrate multiple data science tools
- Centralize data resources
- Handle very large amounts of structured and unstructured data
- Data mining
- Data access, gathering, and preparation
- Data visualization
- Multiple programming language support
- No code options
- Model development and iteration
- Model deployment
- Machine learning
- Deep learning
- Collaboration
- GUI
- Drag and drop building
- Dashboards
- Automated documentation and explainers
- Permissions for access to data and models
- Security
- Audit logs
- Reporting
- Cloud-based, on-premises, hybrid installations
Data Science Platforms Comparison
Consider the following when purchasing Data Science Platforms
Data Science Platforms vs. Data Science Tools: If a variety of complex problems need to be modeled, a data science platform offers many different tools, additional features, and programming language options to meet your needs. Open-source data science tools such as Hadoop, Python, R, and MLlib Apache Spark are sufficient for modeling common and less complex problems such as customer churn.
Open vs Closed Platforms: On some platforms, you are restricted to using their proprietary language such as MATLAB. Others like Anaconda Enterprise support multiple open source programming languages.
Collaboration: Cloud-based platforms facilitate collaboration. Platforms that have robust collaboration features include RapidMiner, DataRobot, and Domino Data Lab.
Data Security: Industries and organizations that have strict data security regulatory requirements such as healthcare and governmental organizations may require an on-premises solution to satisfy those standards. Some vendors offer cloud, on-premises, and hybrid installation options.
Model Deployment: Model deployments entail software engineering skills that are often beyond the capabilities of data scientists. Your in-house expertise will determine how quickly models can be deployed. Evaluate the platform’s deployment features to understand how they may impact deployment timeframes.
In-house Expertise Requirements: Some vendors such as Anaconda Enterprise provide data science modeling environments that will require your in-house data science team to produce all the necessary coding. Others such as Alteryx offer additional support and a more automated approach to data modeling.
Transparency: Platforms shed light on the model they produce by including automated documentation and explainer functionality which promotes data governance and helps reduce bias, compliance, and financial risk. Determine that the features of your selection meet your business’s requirements.
Pricing Information
Data Science Platforms can be expensive starting at around $1,000 a year and ranging over $50,000 a year depending on the tools, features, the number of users, and amount of data that can be supported. Installation fees costing a few thousand dollars are often added to the price. Some vendors will require obtaining a price quote.
More Resources
TrustRadius blog resources for Data Science.
Distinguishing Between Business Intelligence, Business Analytics, and Data Science
Data Science FAQs
What do Data Science Platforms do?
Data Science Platforms build predictive models to provide answers to business questions that help inform decision making and future planning. Using multiple data science tools, large amounts of data, machine learning algorithms, and custom coding, they produce complex predictive models that can be readily modified based on user feedback.
What are the benefits of using Data Science Platforms
Data Science Platforms leverage vast amounts of company data to create value and provide a competitive advantage. They facilitate collaboration, support more efficient modeling and deployment, and create timely insights into the business.
What are the best Data Science platforms?
These are some of the most popular Data Science platforms.
How much do Data Science platforms cost?
Data Science Platforms range from $1,000 a year to over $50,000 a year depending on the tools, features, and the number of users. Vendor price quotes are sometimes required.