Data Science Platforms

Data Science Platforms Overview

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.

Top Rated Data Science Products

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.

Data Science Products

(1-25 of 69) Sorted by Most Reviews

The list of products below is based purely on reviews (sorted from most to least). There is no paid placement and analyst opinions do not influence their rankings. Here is our Promise to Buyers to ensure information on our site is reliable, useful, and worthy of your trust.

Posit
Customer Verified
Top Rated

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

Key Features

  • Visualization (24)
    85%
    8.5
  • Connect to Multiple Data Sources (23)
    84%
    8.4
  • Extend Existing Data Sources (24)
    81%
    8.1
Alteryx
Customer Verified
Top Rated

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…

Splunk Enterprise

Splunk is software for searching, monitoring, and analyzing machine-generated big data, via a web-style interface. It captures, indexes and correlates real-time data in a searchable repository from which it can generate graphs, reports, alerts, dashboards and visualizations.

Key Features

  • Custom dashboards and workspaces (101)
    100%
    10.0
  • Event and log normalization/management (99)
    96%
    9.6
  • Centralized event and log data collection (51)
    92%
    9.2
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…

Key Features

  • Visualization (22)
    86%
    8.6
  • Connect to Multiple Data Sources (22)
    77%
    7.7
  • Extend Existing Data Sources (22)
    75%
    7.5
IBM SPSS Statistics

SPSS Statistics is a software package used for statistical analysis. It is now officially named "IBM SPSS Statistics". Companion products in the same family are used for survey authoring and deployment (IBM SPSS Data Collection), data mining (IBM SPSS Modeler), text analytics, and…

MATLAB

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

DataRobot
Customer Verified
Top Rated

The DataRobot AI Cloud 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…

Key Features

  • Automated Machine Learning (43)
    93%
    9.3
  • Automatic Data Format Detection (42)
    82%
    8.2
  • Visualization (43)
    79%
    7.9
Anaconda

Anaconda is an open source Python distribution / data discovery & analytics platform.

Key Features

  • Data Transformations (26)
    89%
    8.9
  • Visualization (25)
    88%
    8.8
  • Extend Existing Data Sources (24)
    87%
    8.7
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…

Key Features

  • Visualization (22)
    94%
    9.4
  • Interactive Data Analysis (22)
    93%
    9.3
  • Connect to Multiple Data Sources (22)
    88%
    8.8
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’…

TIBCO Data Science (including Team Studio and Statistica)

TIBCO® Data Science is presented by the vendor as 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 innovative…

Databricks Lakehouse Platform (Unified  Analytics 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…

Splunk Cloud

A data platform service thats help users search, analyze, visualize and act on data. The service can go live in as little as two days, and with an IT backend managed by Splunk experts, users can focus on acting on data. Search any kind of data in real-time to detect and prevent issues…

Key Features

  • Event and log normalization/management (21)
    97%
    9.7
  • Custom dashboards and workspaces (21)
    96%
    9.6
  • Centralized event and log data collection (12)
    82%
    8.2
KNIME Analytics Platform

Swiss company KNIME offers their KNIME Analytics Platform for big data and predictive analytics.

Key Features

  • Automatic Data Format Detection (9)
    77%
    7.7
  • Extend Existing Data Sources (9)
    76%
    7.6
  • Connect to Multiple Data Sources (9)
    70%
    7.0
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.

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.

IBM Streaming Analytics

IBM Streaming Analytics is a fully managed service that frees you from time-consuming installation, administration, and management tasks, giving you more time to develop streaming applications. It is powered by IBM Streams, an advanced analytic platform that you can use to ingest,…

Key Features

  • Visualization Dashboards (5)
    100%
    10.0
  • Data Ingestion from Multiple Data Sources (5)
    90%
    9.0
  • Real-Time Data Analysis (5)
    80%
    8.0
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.

Key Features

  • Pre-built visualization formats (heatmaps, scatter plots etc.) (9)
    99%
    9.9
  • Drill-down analysis (8)
    99%
    9.9
  • Report sharing and collaboration (9)
    98%
    9.8
Mode

Mode 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 access relevant data to answer their own questions which…

Key Features

  • Customizable dashboards (9)
    84%
    8.4
  • Integration with R or other statistical packages (9)
    81%
    8.1
  • Formatting capabilities (9)
    76%
    7.6
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)…

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.

Key Features

  • Automatic Data Format Detection (5)
    93%
    9.3
  • Extend Existing Data Sources (5)
    90%
    9.0
  • Connect to Multiple Data Sources (5)
    81%
    8.1
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…

Key Features

  • Interactive Data Analysis (5)
    90%
    9.0
  • Visualization (5)
    88%
    8.8
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…

Key Features

  • Customizable dashboards (5)
    88%
    8.8
  • Report Formatting Templates (5)
    86%
    8.6
  • Formatting capabilities (5)
    86%
    8.6
Dataiku DSS

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.

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.

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?

Data Science Platforms Best Of Awards

The following Data Science Platforms offer award-winning customer relationships, feature sets, and value for price. Learn more about our Winter Best Of Awards methodology here.

Best Of Winter 2023 Awards Winners for the Data Science category. For Best Relationship, first place is DataRobot. For Best Feature Set, first place is DataRobot. For Best Value, first place is DataRobot.

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.