Data Science Platforms

TrustRadius Top Rated for 2023

Top Rated Products

(1-3 of 3)

1
Alteryx

Alteryx aims to be the launchpad for automation breakthroughs. Be it for personal growth, achieving transformative digital outcomes, or rapid innovation, Alteryx converges analytics, data science and process automation to enable users across organizations to make business-altering…

2
Posit

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

3
DataRobot

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…

All Products

(1-25 of 68)

1
Alteryx

Alteryx aims to be the launchpad for automation breakthroughs. Be it for personal growth, achieving transformative digital outcomes, or rapid innovation, Alteryx converges analytics, data science and process automation to enable users across organizations to make business-altering…

2
Posit

Posit, formerly RStudio, is a modular data science platform, combining open source and commercial products.

3
IBM Watson Studio on Cloud Pak for Data

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…

4
DataRobot

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…

5
MATLAB

MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia.

6
Anaconda

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…

7
KNIME Analytics Platform

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.

8
Jupyter Notebook

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…

9
RapidMiner

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’…

10
Databricks Lakehouse Platform

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…

11
Spotfire Data Science

Spotfire Data Science (formerly TIBCO Data Science) is a comprehensive platform for operationalizing data science, allowing users to scale data science across an organization to solve complex challenges faster and speed innovation. It is designed to enable data scientists to create…

12
Saturn Cloud

Saturn Cloud is a cloud data science platform for individuals and teams. Saturn Cloud provides access to computing resources with customizable amounts of memory and power, including GPUs and Dask distributed computing clusters, in a completely hosted environment. Saturn Cloud…

13
SAS Viya

An end-to-end platform for AI, data science, and analytics, used for modeling, as well as management and deployment of AI models.

14
OpenText Magellan

OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.

15
Oracle Machine Learning

Oracle Machine Learning (formerly Oracle Advanced Analytics) combines the Oracle database with Oracle Data Miner and SQL as well as R programming language functionality, providing a complete predictive analytics suite.

16
Wolfram Mathematica

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.

17
Mode Analytics

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…

18
IBM SPSS Modeler

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…

19
Microsoft R Open / Revolution R Enterprise

Microsoft R Open and Revolution R Enterprise are big data R distribution for servers, Hadoop clusters, and data warehouses. Microsoft acquired original developer Revolution Analytics in 2016. Microsoft R is available in two editions: Microsoft R Open (formerly Revolution R Open)…

20
SAS Enterprise Miner

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.

21
Rational BI

Rational BI provides analytics, data science and business intelligence in an analytical platform that connects to databases, data files and cloud drives including AWS and Azure data sources, enabling users to explore and visualize data. Users can build real-time notebook-style reports…

22
Plotly Dash

Plotly headquartered in Montreal creates data visualization and UI tools for ML, data science, engineering, and the sciences with language support for Python, R, Julia, and JS. Plotly's Dash aims to empower teams to build data science and ML apps that put Python, R, and Julia in…

23
Dataiku

Dataiku is a French startup and its product, DSS, is a challenger to market incumbents and features some visual tools to assist in building workflows.

24
Azure Machine Learning

Microsoft's Azure Machine Learning is and end-to-end data science and analytics solution that helps professional data scientists to prepare data, develop experiments, and deploy models in the cloud. It replaces the Azure Machine Learning Workbench.

25
Cloudera Data Science Workbench

Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.

Data Science Platforms TrustMap

TrustMaps are two-dimensional charts that compare products based on trScore and research frequency by prospective buyers. Products must have 10 or more ratings to appear on this TrustMap.

Learn More About Data Science Platforms

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

From Business Intelligence to Data Science: An Explainer

What is a Data Science Platform, and Do I Need One?

Related Categories

Frequently Asked Questions

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.