What are the top predictive analytics software tools for data scientists?

?? Curious about seeing examples of robust platforms and their potential to assist you? * SAS Analytics: Extensive tools for data preparation, statistical modeling, and machine learning. User-friendly interface, powerful computational capabilities. * IBM Watson Studio: Integrates with IBM Cloud for scalability. Offers automated machine learning. * RapidMiner: Intuitive visual workflow design. Vast repository of pre-built machine learning algorithms for rapid model prototyping. Essential for tackling big data challenges. ?? Curious to explore further or have specific questions? Feel free to ask! ?? Like to support this

As per my experience here are some of the top predictive analytics software tools: IBM Watson Studio: IBM Watson Studio offers a suite of tools for data preparation, model development, and deployment. Microsoft Azure Machine Learning: Azure Machine Learning is a cloud-based platform that enables data scientists to build, train, and deploy predictive models at scale. Google Cloud AI Platform: Google Cloud AI Platform provides a suite of tools for building and deploying machine learning models on Google Cloud infrastructure. Amazon SageMaker: Amazon SageMaker is a fully managed machine learning service that enables data scientists to build, train, and deploy models at scale on Amazon Web Services (AWS).

Here are eight categories and my "top choice" in each as a former CPO and designer of many of the Gartner leading products in this space and industry analyst: Best Overall for Data Prep, Blending, and Advanced Analytics: Dataiku Best for Visualizing and Exploring Complex Data: Tableau Best for End-to-End ML Workflow: Azure Machine Learning Best for Advanced Statistical Analysis: IBM SPSS Best for Enterprise MLOps: Domino Data Labs Best for AutoML: H2O.ai Best for Integrated Predictive Analytics in a BI Tool: TIBCO Spotfire (full disclosure, I was the CPO / GM for Spotfire) Best Cloud Native AI Platform: Pick your cloud platform of choice. Azure, Amazon, and Google are all good choices.

These are a few of the best predictive analytics software applications, in my opinion: A set of tools for data preparation, model building, and deployment is provided by IBM Watson Studio. Microsoft Azure Machine Learning: Data scientists can create, train, and implement predictive models at scale with the help of this cloud-based platform. A set of tools for creating and implementing machine learning models on Google Cloud infrastructure is offered by the Google Cloud AI Platform. Amazon SageMaker: Data scientists can create, train, and implement models at scale on Amazon Web Services (AWS) with the help of this fully managed machine learning solution.

Some of the top user-friendly predictive analytics software tools for data scientists include IBM SPSS Modeler, RapidMiner, and Alteryx. These tools offer intuitive interfaces and drag-and-drop functionalities, making it easier for users to build predictive models without extensive programming knowledge. Additionally, tools like Tableau and Microsoft Power BI are often used for visualizing predictive insights in a user-friendly manner.

Here's some information i came accross when diving into the subject online. Popular user-friendly predictive analytics tools for data scientists include Python libraries like scikit-learn, TensorFlow, and Keras, R programming with packages like caret and randomForest, as well as platforms like KNIME, RapidMiner, DataRobot, and Microsoft Azure Machine Learning.

"User-friendly" depends on who the "user" is :). For developers, consider tools like TensorFlow, Pandas, H2O.ai, Matplotlib, and Scikit-learn. These are developer-friendly and provide excellent control and low-level APIs. For low-code / no-code tools and "Everyday AI," Dataiku is the leader, followed by Knime, Altair Rapid Miner, and Alteryx. For BI users, most modern BI tools, including Tableau, PowerBI, Thoughtspot, Looker, and Spotfire, have built-in predictive analytics capabilities. These use predictive analytics and libraries developed in one of the other platforms and made accessible to the BI tool. So the best tool depends on the user behind the wheel!

Cloud-based predictive analytics software tools for data scientists include SAP Predictive Analytics, Oracle Cloud Infrastructure (OCI) Data Science, SAS Visual Data Science, IBM Watson Studio, Dataiku DSS, and Alteryx Designer. SAP Predictive Analytics offers advanced machine learning and security features for creating, operationalizing, and monitoring predictive models. OCI Data Science allows data scientists to build, train, deploy, and manage machine learning models using Python and open-source tools. SAS Visual Data Science offers comprehensive features for data scientists, while IBM Watson Studio simplifies predictive analytics for expert data scientists and improves collaborative data science for business users.

For line-of-business users, such as marketing, sales, and finance, specialized predictive analytics are becoming a major part of every tool. This is a good option, but it tends to be "black box" solutions with built-in algorithms you don't control. To combine the best of both worlds, look for applications that have good API support so that you can inject, call, and integrate the output of custom models into your specialized tool. This architecture combines black-box, in-built capabilities with the ability to add your own "special sauce" developed in an AI platform for customizable AI.

Some of the top predictive analytics software tools for data scientists include: RapidMiner: Known for its user-friendly interface and powerful capabilities, RapidMiner offers a wide range of advanced analytics tools for data preparation, modeling, and deployment. KNIME: This open-source platform allows data scientists to design, build, and deploy data workflows, incorporating machine learning algorithms, data manipulation tools, and visualization capabilities. SAS Predictive Analytics: SAS offers a comprehensive suite of predictive analytics tools, including SAS Enterprise Miner, for building predictive models and SAS Visual Analytics for exploring and visualizing data.

For cloud-agnostic options, consider pure-play platforms like Dataiku (my favorite and the leader, in my opinion). Other options include IBM Watson Studio, Alteryx, DataRobot, Knime, and "legacy" platforms like SAS. Also, consider the analytics tools provided by your cloud provider. AWS, Azure, and Google all offer comprehensive platforms for predictive analytics. Finally, as noted in my answers above, there are many open-source options and toolkits that you can use to "cobble together" an integrated offering. And then there's pricing, which varies and is tool complicated to post here. Choose carefully: low-code agnostic platforms are the most agile, cloud platforms are terrific but lock you in, and open source requires the most work.

Cost! Each of these options comes with a cost, short-term (licensing), long-term (TCO), and the cost to use them (training cost and cost of staff to use them). To me, low-code platforms like Dataiku are in the sweet spot of ease of use for everyone ("AI for All" is their slogan), reasonable cost, quality, and comprehensiveness (ability to scale). Cloud platforms are terrific but lock you into their tooling, platform, operations, and style. Among these, I believe Microsoft's Azure AI studio is the leader. Finally, open source requires the most work and focuses more on developer-level coding. But this option also gives you the most control. While open source is cheapest from a licensing perspective, its TCO is by far the highest.