Data Science Platforms

Best Data Science Platforms include:

Anaconda.

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 Platforms 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

(1-25 of 31) Sorted by Most Reviews

IBM Watson Studio (formerly IBM Data Science Experience)
162 ratings
49 reviews
IBM Watson Studio (formerly IBM Data Science Experience) is a collaborative, cloud-based environment providing data scientists with a variety of tools including RStudio, Jupyter, Python, Scala, Spark, IBM Watson Machine Learning, and more.
MATLAB
126 ratings
46 reviews
Top Rated
MatLab is a predictive analytics and computing platform based on a proprietary programming language. MatLab is used across industry and academia.
Alteryx Platform
149 ratings
44 reviews
Top Rated
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…
RStudio
71 ratings
32 reviews
Top Rated
The primary mission of RStudio is to build a sustainable open-source business that creates software for data science and statistical computing, including such as the RStudio IDE, R Markdown, shiny, and many packages in the tidyverse.RStudio open source projects are supported by their commercial prod…
Anaconda
66 ratings
22 reviews
Top Rated
Anaconda is an open source Python distribution / data discovery & analytics platform.
RapidMiner Studio
43 ratings
17 reviews
RapidMiner Studio is a data science and data mining platform from RapidMiner in Cambridge, Massachusetts.
TIBCO Data Science (including Team Studio and Statistica)
28 ratings
15 reviews
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 solutions u…
Oracle Machine Learning (formerly Oracle Advanced Analytics)
121 ratings
9 reviews
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.
OpenText Magellan
18 ratings
9 reviews
OpenText Magellan Analytics Suite leverages a comprehensive set of data analytics software to identify patterns, relationships and trends through data visualizations and interactive dashboards.
KNIME Analytics Platform
32 ratings
9 reviews
Swiss company KNIME offers their KNIME Analytics Platform for big data and predictive analytics.
IBM Streaming Analytics
59 ratings
8 reviews
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 Mathematica
25 ratings
8 reviews
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.
Databricks Unified Analytics Platform
19 ratings
5 reviews
Databricks in San Francisco offers the Databricks Unified Analytics Platform, a data science platform and Apache Spark cluster manager.
SAS Enterprise Miner
8 ratings
5 reviews
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.
Microsoft R (formerly Revolution R)
15 ratings
4 reviews
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…
H2O
6 ratings
3 reviews
H2O.ai is an open-source predictive analytics and machine learning platform.
SAP Predictive Analytics
10 ratings
2 reviews
SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.
Dataiku DSS
8 ratings
2 reviews
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.
Cloudera Data Science Workbench
13 ratings
2 reviews
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.
Domino
6 ratings
1 reviews
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.
Angoss KnowledgeSTUDIO
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.
FICO Model Builder
The FICO Model Builder is a predictive analytics and modeling tool, from FICO.
Wolfram Data Science Platform
Wolfram Data Science Platform enabling a full spectrum of data science analysis and visualization and automatically generating rich interactive reports.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open source software library for data science and analytics performed across GPUs.
Azure Data Science Virtual Machines (DSVM)
Available on Microsoft's Azure platform, Data Science Virtual Machines (DSVMs) are comprehensive pre-configured virtual machines for data science modelling, development and deployment.