Top 8 Data Science Platforms

DatabricksAlteryxMicrosoft Azure Machine Learning StudioKNIMEIBM SPSS StatisticsRapidMinerDataiku Data Science StudioIBM SPSS Modeler
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    We can scale the product.The most valuable feature of Databricks is the notebook, data factory, and ease of use.
  2. leader badge
    The three data signs and data engineering are great features.The initial setup is easy.
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  4. What I like best about Microsoft Azure Machine Learning Studio is that it's a straightforward tool and it's easy to use. Another valuable feature of the tool is AutoML which lets you get better metrics to train the model right and with good accuracy. The AutoML feature allows you to simply put in your data, and it'll pre-process and create a more accurate model for you. You don't have to do anything because AutoML in Microsoft Azure Machine Learning Studio will take care of it.
  5. The solution allows for sharing model designs and model operations with other data analysts. Easy to use, stable, and powerful.
  6. SPSS can handle whatever you throw at it, whether your data set contains 10,000, 100,000, or a million objects. It's like the heavy artillery of analytical tools.
  7. The most valuable feature of RapidMiner is that it is code free. It is similar to playing with Lego pieces and executing after you are finished to see the results. Additionally, it is easy to use and has interesting utilities when preparing the data. It has a utility to automatically launch a series of models and show the comparisons. When finished with the comparisons you can select the best one, and deploy it automatically.
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  9. Data Science Studio's data science model is very useful. The solution is quite stable.
  10. The most valuable features of the IBM SPSS Modeler are visual programming, you don't have to write any code, and it is easy to use. 90 to 95 percent of the use cases, you don't have to fine-tune anything. If you want to do something deeper, for example, create a better neural network, then you have to go into the features and try to fine-tune them. However, the default selection which is made by the tool, it's very practical and works well.

Advice From The Community

Read answers to top Data Science Platforms questions. 656,862 professionals have gotten help from our community of experts.
Glen Green - PeerSpot reviewer
Glen Green
Sr. Project Manager at a manufacturing company with 10,001+ employees
Aug 22 2022
I have experience working as a senior integration architect for AI/ML enablement for a manufacturing company with 10,000+ employees.   We are evaluating data science platforms. Which vendor offers an end-to-end solution that really works from features management to model deployment?  Thanks! I ...
Read More »
ChunqiangGong - PeerSpot reviewer
ChunqiangGongKNIME or Alterxy is a good choice for a company to deploy AI applications… more »
16 Answers
Rony_Sklar - PeerSpot reviewer
PeerSpot (formerly IT Central Station)

Hello community members,

There are many Data Science Platforms available. Which platform would you recommend that can handle large amounts of data? Why?

Ziad Chaudhry - PeerSpot reviewer
Ziad ChaudhryDakaIku is a great general purpose data science platform for both supervised and… more »
9 Answers
Rony_Sklar - PeerSpot reviewer
PeerSpot (formerly IT Central Station)
Hi peers, There are so many data science platforms to choose from. Which platform would you recommend to enterprise-level companies that want flexible and powerful data visualization capabilities to drill down into the data?  What makes the solution that you recommend a better choice than others?
Read More »
Peter Eerdekens - PeerSpot reviewer
Peter EerdekensQlikSense. The associative analytics engine makes it kind of child's play to… more »
9 Answers

Data Science Platforms Articles

Ariful Mondal - PeerSpot reviewer
Ariful Mondal
Consulting Practice Partner - Data, Analytics & AI at FH
Following primary steps should be followed in Predictive Modeling/AI-ML Modeling implementation process (ModelOps/MLOps/AIOps etc.) Step 1: Understand Business Objective Step 2: Define Modeling Goals Step 3: Select/Get Data Step 4: Prepare Data Step 5: Analyze and Transform Variables/Featu...
Read More »
Prithwis De, PhD, CStat - PeerSpot reviewer
Prithwis De, PhD, CStatNicely articulated
AtanuChakraborty - PeerSpot reviewer
AtanuChakrabortyPrecise illustration

Data Science Platforms Topics

What are the 3 main uses of data science?

Data science can be used to accomplish many different tasks and goals. The three main goals that it is typically used for are:

  • Strategic planning. Organizations that employ data science and data scientists typically want to leverage the data that they have available in a way that will enable them to look toward the future. Data science allows them to gain insights that can enable them to set goals for and plan for the future. Knowing what potential trends their organization may experience can make it easier for decision-makers to define the direction in which they want to take their business.
  • Business decision-making. Businesses, regardless of the service or product that they offer, seek to be proactive rather than simply reactive when it comes to their day-to-day operations. The interpretation of data via the use of data science can enable organizations to make better and more informed decisions as far as how they operate on a daily basis.
  • Simplification of critical data extraction-related tasks. Data science makes it easy for organizations to efficiently process and make use of large quantities of data. The amount of time required to transform data into something actionable is significantly reduced when data science is employed to handle an organization’s data.
What is a data science platform?

Data science platforms are the framework or structure where the lifecycle of the data science process takes place. A data science platform contains all of the tools that an organization needs to successfully initiate and complete every stage of a data science project. It gives data scientists the tools they need to analyze the data in front of them in the most efficient and effective way possible. A data science platform achieves this by significantly cutting the time required to run analyses of the data and transform them into deployable visual models.

Why do you need a data science platform?

There are a number of reasons why organizations should incorporate data science platforms into their business strategy. These reasons include:

  • Their ability to handle massive amounts of data. Organizations and businesses rely on data in order to run efficiently. If they want to avoid being overloaded by large payloads of data, then they need an efficient way of transforming the data into something that they can utilize. If they employ a data science platform, they will be empowered to use vast amounts of data effectively without being overwhelmed.
  • Enable users to remove the risk of errors or inconsistencies in their data models. People can make mistakes, especially when they are dealing with large datasets. This can mean that the models that they develop using that data are potentially full of mistakes. The AI and ML capabilities of data science platforms head this off and significantly reduce the possibility that the data models will contain errors or inconsistencies
  • Users can protect themselves from threats. Among the many capabilities that a data science platform can offer organizations is the ability to search out and find patterns, anomalies, or flaws that potentially pose threats to them or their customers. Data science platforms make it possible to find things that human supervision could potentially miss.
Data Science Platform Features

Data science platforms typically offer users access to a wide array of features. These features include:

  • Broad data collection capabilities. Data science platforms have tools that enable users to easily collect data from various sources and integrate them into the data models that they are creating.
  • Data visualization tools. Users are given the tools that they need to convert data into visual representations that make them easier to understand. These visualizations can be customized to meet an organization’s specific needs.
  • AI and ML analytics. Artificial intelligence and machine learning technologies are the backbones of data science platforms. They give organizations a wide array of capabilities that allow them to tackle any project or task in the most effective way possible.
Benefits of Data Science Platforms

Data science platforms can offer users various benefits. These benefits include:

  • Centralized control of an organization’s data analytics capabilities. These platforms place all of the tools that data scientists need to complete projects in a single centralized location. This makes it easy for teams to engage in internal collaboration; it also makes it relatively simple for organizations to train employees to use these platforms.
  • Flexibility. A data science platform enables every team in every department to leverage its tools and benefit from them. Additionally, it should easily integrate with new tools so that users can use it alongside whatever new investments they happen to make.
  • Ease of use. Data science platforms are designed so that any member of an organization can use them without having to reach out to an expert. They are made with the users in mind.
Buyer's Guide
Data Science Platforms
November 2022
Find out what your peers are saying about Databricks, Alteryx, Microsoft and others in Data Science Platforms. Updated: November 2022.
656,862 professionals have used our research since 2012.