Edureify have developed this resource to assist buyers in search of the best data science software to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best data science software providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action. Try the bootcamp coding courses at Edureify now.

 

Altair

Platform: Altair Data Analytics

 

Related products: Altair Knowledge Studio, Altair Altair SmartWorks, Altair Analytics Workbench, Altair Analytics Compiler

 

Description: Altair offers a suite of machine learning and AI solutions through its data analytics portfolio. Designed for people with different skill sets, the portfolio delivers transparency and automation with features such as AutoML and Explainable AI, and streamlines model building so more time can be spent analyzing and results can be trusted. The flexible no-code approach doesn’t restrict how models are configured and tuned, giving users control over model building. With support for common SAS language, R, Python and engines, users can integrate new models built using Altair into existing analytics infrastructure.

 

Anaconda

Platform: Anaconda Enterprise

 

Related products: Anaconda Distribution, Anaconda Team Edition

 

Description: Anaconda offers its data science and machine learning capabilities via a number of different product editions. Its flagship product is Anaconda Enterprise, an open-source Python and R-focused platform. The tool enables you to perform data science and machine learning on Linux, Windows, and Mac OS. Anaconda allows users to download more than 1,500 Python and R data science packages, manage libraries, dependencies, and environments, and analyze data with Dask, NumPy, pandas, and Numba. You can then visualize results generated in Anaconda with Matplotlib, Bokeh, Datashader, and Holoviews.  Try the bootcamp coding courses at Edureify now.

 

Data bricks Unified Analytics Platform

 

Platform: Databricks Unified Analytics Platform

 

Description: Databricks offers a cloud and Apache Spark-based unified analytics platform that combines data engineering and data science functionality. The product leverages an array of open-source languages and includes proprietary features for operationalization, performance, and real-time enablement on Amazon Web Services. A Data Science Workspace enables users to explore data and build models collaboratively. It also provides one-click access to preconfigured ML environments for augmented machine learning with popular frameworks. 

 

Dataiku Data Science 

 

Platform: Dataiku Data Science Studio (DSS)

 

Description: Dataiku offers an advanced analytics solution that allows organizations to create their own data tools. The company’s flagship product features a team-based user interface for both data analysts and data scientists. Dataiku’s unified framework for development and deployment provides immediate access to all the features needed to design data tools from scratch. Users can then apply machine learning and data science techniques to build and deploy predictive data flows. Also see the bootcamp coding courses at Edureify now.

 

DataRobot Enterprises

 

Platform: DataRobot Enterprise AI Platform

 

Related products: Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps

 

Description: DataRobot offers an enterprise AI platform that automates the end-to-end process for building, deploying, and maintaining AI. The product is powered by open-source algorithms and can be leveraged on-prem, in the cloud or as a fully-managed AI service. DataRobot includes several independent but fully integrated tools (Paxata Data Preparation, Automated Machine Learning, Automated Time Series, MLOps, and AI applications), and each can be deployed in multiple ways to match business needs and IT requirements. Download the Edureify app now to know more about the same.

Domino Data Science

 

Platform: Domino Data Science Platform

 

Related products: Domino Model Monitor

 

Description: Domino Data Lab offers an enterprise data science platform that allows data scientists to build and run predictive models. The product helps organizations with the development and delivery of these models via infrastructure automation and collaboration. Domino provides users access to a data science Workbench that provides open source and commercial tools for batch experiments, as well as Model Delivery so they can publish APIs and web apps or schedule reports.

 

Google Cloud AI Platform

 

Platform: Google Cloud AI Platform

 

Related products: Google Cloud Data Fusion, Google Cloud AutoML, Google BigQuery ML, Google AI Platform Notebooks, Google TensorFlow

 

Description: Google Cloud AI offers one of the largest machine learning stacks in the space and offers an expanding list of products for a variety of use cases. The product is fully managed and offers excellent governance with interpretable models. Key features include a built-in Data Labeling Service, AutoML, model validation via AI Explanations, a What-If Tool which helps you understand model outputs, cloud model deployment with Prediction, and MLOps via the Pipeline tool. 

 

H2O Driverless AI

 

Platform: H2O Driverless AI

 

Related products: H2O 3, H2O AutoML for ML, H2O Sparkling Water for Spark Integration, H2O Wave

 

Description: H2O.ai offers a number of AI and data science products, headlined by its commercial platform H2O Driverless AI. Driverless AI is a fully open-source, distributed in-memory machine learning platform with linear scalability. H2O supports widely used statistical and machine learning algorithms including gradient boosted machines, generalized linear models, deep learning and more. H2O has also developed AutoML functionality that automatically runs through all the algorithms to produce a leaderboard of the best models. 

 

IBM Watson Studio

 

Platform: IBM Watson Studio

 

Related products: IBM Cloud Pak for Data, IBM SPSS Modeler, IBM Decision Optimization, IBM Watson Machine Learning

 

Description: IBM Watson Studio enables users to build, run, and manage AI models at scale across any cloud. The product is a part of IBM Cloud Pak for Data, the company’s main data and AI platform. The solution lets you automate AI lifecycle management, govern and secure open-source notebooks, prepare and build models visually, deploy and run models through one-click integration, and manage and monitor models with explainable AI. IBM Watson Studio offers a flexible architecture that allows users to utilize open-source frameworks like PyTorch, TensorFlow, and sci-kit-learn. 

 

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KNIME Analytics Platform

 

Platform: KNIME Analytics Platform

 

Related products: KNIME Server

 

Description: KNIME Analytics is an open-source platform for creating data science. It enables the creation of visual workflows via a drag-and-drop-style graphical interface that requires no coding. Users can choose from more than 2000 nodes to build workflows, model each step of the analysis, control the flow of data, and ensure work is current. KNIME can blend data from any source and shape data to derive statistics, clean data, and extract and select features. The product leverages AI and machine learning and can visualize data with classic and advanced charts. 

 

MATLAB

Platform: MATLAB

 

Related products: Simulink

 

Description: MathWorks MATLAB combines a desktop environment tuned for iterative analysis and design processes with a programming language that expresses matrix and array mathematics directly. It includes the Live Editor for creating scripts that combine code, output, and formatted text in an executable notebook. MATLAB toolboxes are professionally developed, tested, and fully documented. MATLAB apps let you see how different algorithms work with your data as well.

 

AZURE Machine Learning

 

Platform: Azure Machine Learning

 

Related products: Azure Data Factory, Azure Data Catalog, Azure HDInsight, Azure Databricks, Azure DevOps, Power BI

 

Description: The Azure Machine Learning service lets developers and data scientists build, train, and deploy machine learning models. The product features productivity for all skill levels via a code-first and drag-and-drop designer and automated machine learning. It also features expansive MLops capabilities that integrate with existing DevOps processes. The service touts responsible machine learning so users can understand models with interpretability and fairness, as well as protect data with differential privacy and confidential computing. Azure Machine Learning supports open-source frameworks and languages like MLflow, Kubeflow, ONNX, PyTorch, TensorFlow, Python, and R. 

Rapid Miner Studio

 

Platform: RapidMiner Studio

 

Related products: RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud

 

Description: RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists need but simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.

 

Frequently Asked Questions (FAQs)

Question:- Who is the No 1 data scientist in the world?

 

Answer:- Yann LeCun-  He is well known as the Director of AI Research at Facebook, but has made industry-changing inventions that earned him a spot at the top of the list of best data scientists in the world.

 

Question:- What is a typical data science project?

Answer:-  A data science project is a practical application of your skills. A typical project allows you to use skills in data collection, cleaning, analysis, visualization, programming, machine learning, and so on. It helps you take your skills to solve real-world problems.

 

Question:- What is the best project for data science?

Answer:- Best Data Science Projects for Beginners

  • Fake News Detection Using Python.

  • Data Science Project on Detecting Forest Fire.

  • Detection of Road Lane Lines.

  • Project on Sentimental Analysis.

  • Project on Influences of Climatic Pattern on the food chain supply globally.

 

Question:-What are some good ML projects?

Answer:- Top 10 Machine Learning Projects:

  • Movie Recommendations with Movielens Dataset.

  • TensorFlow.

  • Sales Forecasting with Walmart.

  • Stock Price Predictions.

  • Human Activity Recognition with Smartphones.

  • Wine Quality Predictions.

  • Breast Cancer Prediction.

  • Iris Classification.

 

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