Best Data Science PlatformsIBM Watson Studio (formerly IBM Data Science Experience)1 Platform3 Studio5 Data Science (including Statistica)6 Magellan7 Streaming Analytics8 Mathematica9 Analytics Platform10 Unified Analytics Platform11 Enterprise Miner12 R (formerly Revolution R)13 Predictive Analytics15 Data Science Workbench16 KnowledgeSTUDIO18 Model Builder19 Data Science Platform20 Spectrum Discover21 RAPIDS23 Data Science Virtual Machines (DSVM)24

Data Science Platforms

Best Data Science Platforms

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Data Science Software Overview

What is Data Science Software?

Data science technology can help organizations turn their data into a valuable resource in the creation of business value. Data science tools are capable of handling data volumes that are too big for traditional databases or statistical tools.

Data science tools create value by mining large amounts of structured and unstructured data to identify patterns can help an organization to more effectively manage costs and achieve competitive advantage.

Data science tools incorporate a variety of component technologies such as machine learning, data mining, data modeling, data mining, and visualization.

Why did Data Science Technology Emerge?

Data science is an emerging response to the unprecedented volumes of data that are available to businesses for decision-making purposes. The desire to derive social and economic value from this newly available data is limited only by the lack of expertise and tools. Data science tools have emerged to fill this gap.

Data Science Software Features & Capabilities

Data Science platform features vary considerably from one product to the next. Available features also differ between features for bona fide data science platforms and machine learning tools which are really a subset of data science.

  • Data access and ingestion

  • Data preparation

  • Data visualization and discovery

  • Advanced modeling

  • Model deployment

  • Integration with big data tools like Hadoop

  • Access control

  • Audit logs

  • Reports

What Skills are Required?

The skill sets required for true data science are in short supply. This puts pressure on tool developers to reduce complexity to increase the potential user pool. In general, data scientists have advanced analytics skills like actuaries, who calculate insurance risks and premiums. They are quantitative researchers who typically have strong programming skills.

What do Data Scientists Do?

Although data scientists write code, they are quite different to developers. One of the main differences is that data scientists are primarily engaged in research, rather than in building products.

The job performed by data scientists is largely experimental. They build models that are designed to be predictive and then run them to see how well they perform. Then they tweak them and run them again.

The kinds of problems data scientists try to solve use huge amounts of unstructured data. Some examples are models that accurately predict customer churn or models to optimize vacation rental pricing based on location, time of year, etc.

Collaboration with Business

Agile development processes require a user or client to articulate the business need and provide constant feedback on what is being built. Data science teams are similar. They are most effective when they work closely with the people who actually use the models that they build. Without this alignment, the models built may not effectively solve the business problem that the business unit is struggling with.

Types of Data Science Platforms

Some platforms are primarily about model development and contain coding language capabilities to this end. Data Science models are typically quite complex and require advanced coding skills and often specialized hardware. Data scientists also frequently utilize many machines concurrently by spreading work across them.

A number of platforms do not contain languages for writing code but, instead, allow products like R, SAS, or Python to execute model code. Instead, they function as a system of record for all the models being developed by an entire data science team.

Pricing Information

Data scientists use a wide variety of tools some of which, like Python and Apache Spark and Hadoop, are open-source. Data scientists also use data mining tools, NoSQL databases, statistical computing tools like R, and others.

Enterprise data science platforms that keep track of projects and automate some of the code writing are relatively new arrivals. These platforms charge for an annual subscription. The price can vary depending on whether the software is running in the cloud or on-premise. There are also likely to be usage fees for compute time.

Data Science Products

Listings (1-25 of 29)


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124 Ratings

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

Alteryx Platform

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148 Ratings

The Alteryx Platform is a business intelligence and predictive analytics solution. The vendor's value proposition is that their solution provides analysts with the unique ability to easily prep, blend, and analyze all of their data using a repeatable workflow, then deploy and share analytics at scal…


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66 Ratings

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

TIBCO Statistica offers a comprehensive and open data science portfolio featuring the latest techniques – via code free and open source programming. According to the vendor key features and business benefits include: • Accelerate innovation and solve your organization’s most complex problems by scal…

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

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, analyze, and corre…

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.

Microsoft R (formerly Revolution R) is a 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) and Microsoft R Enterpris…

6 Ratings

Domino Data Lab in San Francisco offers the Domino data science platform. It accelerates the development and delivery of models and increases data scientist productivity.

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Angoss Software in Toronto offers KnowledgeSTUDIO, billed as an easy to use data mining and predictive analytics platform. Angoss Software was acquired by Datawatch Corporation in January 2018, Angoss products are now supported by Datawatch.

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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 objects…

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According to the vendor, Analance is a robust, scalable end-to-end advanced analytics platform that combines machine learning, artificial intelligence, business intelligence, and data management capabilities in one integrated, self-serve platform. The platform is built to deliver core analytical pro…

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NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs.

We don't have enough ratings and reviews to provide an overall score. presents their technology as the fastest way to get from research to production - is an end-to-end machine learning platform that helps teams manage, build and automate machine learning pipelines.