List of Top Data Science Platforms 2020

Best Data Science Platforms

Best Data Science Platforms include: Databricks Unified Analytics Platform, TIBCO Data Science (including Statistica), Anaconda, RapidMiner Studio, and IBM Watson Studio.
A complete list of Data Science Platforms is available here.

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

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.

IBM Watson Studio (formerly IBM Data Science Experience)

161 Ratings

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

117 Ratings

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

Alteryx Platform

145 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 s...

RapidMiner Studio

43 Ratings

RapidMiner Studio is a data science and data mining platform from RapidMiner in Cambridge, Massachusetts.

TIBCO Data Science (including Statistica)

28 Ratings

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 s...

Anaconda

56 Ratings

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

IBM Streaming Analytics

58 Ratings

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 co...

Wolfram Mathematica

22 Ratings

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.

KNIME Analytics Platform

27 Ratings

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

Databricks Unified Analytics Platform

17 Ratings

Databricks in San Francisco offers the Databricks Unified Analytics Platform, a data science platform and Apache Spark cluster manager.

SAS Enterprise Miner

8 Ratings

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)

16 Ratings

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 Enterp...

H2O

6 Ratings

H2O.ai is an open-source predictive analytics and machine learning platform.

Cloudera Data Science Workbench

13 Ratings

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.

SAP Predictive Analytics

9 Ratings

SAP Predictive Analytics is, as the name would suggest, a statistical analysis and data mining platform that can be deployed with SAP HANA.

Domino

4 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.