Best Data Science Software in China

Accelerate and simplify IoT data extraction, create AI for the shop floor, and visualize KPIs. Manage decentralized, compact data pods. Each data pod is completely autonomous and includes infrastructure for powerful analytics. Flexible storage capacity allows you to create multiple pods with different sizes. In a seamless data journey, you can collect, analyze, visualize, and visualize data. You can collect raw data from multiple sources, such as IoT routers or the web. Instantly generate reports and create custom infographics from your browser. Combine the power of VS Code, Observable and TablePlus to create interactive data science workbooks. You can see the current and past processes in real time and automate package loads up to reporting.

You can build, run, and manage AI models and optimize decisions across any cloud. IBM Watson Studio allows you to deploy AI anywhere with IBM Cloud Pak®, the IBM data and AI platform. Open, flexible, multicloud architecture allows you to unite teams, simplify the AI lifecycle management, and accelerate time-to-value. ModelOps pipelines automate the AI lifecycle. AutoAI accelerates data science development. AutoAI allows you to create and programmatically build models. One-click integration allows you to deploy and run models. Promoting AI governance through fair and explicable AI. Optimizing decisions can improve business results. Open source frameworks such as PyTorch and TensorFlow can be used, as well as scikit-learn. You can combine the development tools, including popular IDEs and Jupyter notebooks. JupterLab and CLIs. This includes languages like Python, R, and Scala. IBM Watson Studio automates the management of the AI lifecycle to help you build and scale AI with trust.

To identify the best actions, you need to build and solve complex optimization models. IBM®, ILOG®, CPLEX®, Optimization Studio uses decision optimization technology. It optimizes your business decisions, creates and deploys optimization models quickly, and creates real-world applications that can significantly increase business outcomes. How does it work? How? It combines a fully-featured integrated development environment that supports Optimization Programming Language, (OPL), and the high-performance CPLEX/CP Optimizer solvers. It's data science for your decisions. IBM Decision Optimization is also available in Cloud Pak for Data. This allows you to combine optimization and machine-learning within a unified environment, IBM Watson® Studio that enables AI infused optimization modeling capabilities.

Wolfram Data Science Platform allows you to use structured and unstructured data sources, as well as real-time or static data. Alpha to convert unstructured data to structured form, with automated or guided destructuring and disambiguation. Wolfram Data Science Platform uses industry-specific database connection technology to transform database content into its highly flexible internal symbol representation. Wolfram Data Science Platform is capable of reading hundreds of data formats and converting them. Wolfram Data Science Platform can work with images, text and networks as well as sounds, GIS data, and many other formats. Wolfram Data Science Platform seamlessly handles both SQL-style data and NoSQL data thanks to the Wolfram Language's breakthrough symbolic data representation. Wolfram Data Science Platform automatically creates an interactive report using algorithms that identify interesting features in your data to highlight and visualize.

An end-to-end solution gives you all the tools your data science team needs to scale your machine learning development, from research to production. cnvrg.io, the world's leading data science platform for MLOps (model management) is a leader in creating cutting-edge machine-learning development solutions that allow you to build high-impact models in half the time. In a collaborative and clear machine learning management environment, bridge science and engineering teams. Use interactive workspaces, dashboards and model repositories to communicate and reproduce results. You should be less concerned about technical complexity and more focused on creating high-impact ML models. The Cnvrg.io container based infrastructure simplifies engineering heavy tasks such as tracking, monitoring and configuration, compute resource management, server infrastructure, feature extraction, model deployment, and serving infrastructure.

Machine learning uncovers hidden patterns in enterprise data and generates new value for businesses. Oracle Machine Learning makes it easier to create and deploy machine learning models for data scientists by using AutoML technology and reducing data movement. It also simplifies deployment. Apache Zeppelin notebook technology, which is open-source-based, can increase developer productivity and decrease their learning curve. Notebooks are compatible with SQL, PL/SQL and Python. Users can also use markdown interpreters for Oracle Autonomous Database to create models in their preferred language. No-code user interface that supports AutoML on Autonomous Database. This will increase data scientist productivity as well as non-expert users' access to powerful in-database algorithms to classify and regression. Data scientists can deploy integrated models using the Oracle Machine Learning AutoML User Interface.

All the steps involved in building machine learning algorithms and predicting results, all in one click. Data Dialog allows you to easily shape your data without having to wrangle your files. Your prediction reports can be shared with your team members or made public. Let anyone make predictions on your model. Our low-code API allows you to integrate dynamic ML predictions directly into your app. Real-time prediction of willingness to pay, score leads, and many other things. AI gives you access to the most advanced algorithms in the world, without compromising on performance. Forecast revenue, optimize supply chain, personalize your marketing. Now you can see what the next steps are. In minutes, you can add a CSV file or integrate with your favorite data sources. Select your prediction column from the dropdown and we'll automatically build the AI. Visualize the top drivers, predicted results, and simulate "what-if?" scenarios.

Unutilized data is the biggest obstacle to scaling AI-powered decision making. IBM Cloud Pak®, for Data is a unified platform that provides a data fabric to connect, access and move siloed data across multiple clouds or on premises. Automate policy enforcement and discovery to simplify access to data. A modern cloud data warehouse integrates to accelerate insights. All data can be protected with privacy and usage policy enforcement. To gain faster insights, use a modern, high-performance cloud storage data warehouse. Data scientists, analysts, and developers can use a single platform to create, deploy, and manage trusted AI models in any cloud.

Oracle Cloud Infrastructure (OCI Data Flow) is a fully managed Apache Spark service that performs processing tasks on very large data sets. There is no infrastructure to deploy or manage. This allows developers to focus on application development and not infrastructure management, allowing for rapid application delivery. OCI Data Flow manages infrastructure provisioning, network setup, teardown, and completion of Spark jobs. Spark applications for big data analysis are easier to create and manage because storage and security are managed. OCI Data Flow does not require clusters to be installed, patched, or upgraded, which reduces both time and operational costs. OCI Data Flow runs every Spark job in dedicated resources. This eliminates the need to plan for capacity ahead. OCI Data Flow allows IT to only pay for the infrastructure resources used by Spark jobs while they are running.

IBM Analytics for Apache Spark allows data scientists to ask more difficult questions and deliver business value quicker with a flexible, integrated Spark service. It's a simple-to-use, managed service that is always on and doesn't require any long-term commitment. You can start exploring immediately. You can access the power of Apache Spark without locking yourself in, thanks to IBM's open-source commitment as well as decades of enterprise experience. With Notebooks as a connector, coding and analytics are faster and easier with managed Spark services. This allows you to spend more time on innovation and delivery. You can access the power of machine learning libraries through managed Apache Spark services without having to manage a Sparkcluster by yourself.

SAS Visual Statistics allows multiple users to interactively explore data and then create and refine predictive models. Your statisticians and data scientists can use the most appropriate analytical modeling techniques to analyze your observations at a fine level. What will you get? The result? You can quickly build and refine models to target specific segments or groups, and run multiple scenarios simultaneously. To get better results, you can ask more "what-if" questions. You can also use an automatically generated score code to put your results into practice. Multiple users can interact with data visually. They can add, change, or remove outliers. You can instantly see how changes affect the predictive power of your model and make adjustments quickly. Data science teams have the freedom to work in the language they prefer, so they can make the most of their talents. SAS Visual Statistics combines all analytical assets.

SAS®, Viya®, data science offerings offer a comprehensive, scalable analytical environment that is quick and easy to use, allowing you to meet diverse business requirements. Automatically generated insights allow you to identify the most commonly used variables across all models, the most significant variables selected across models, and assess results for all models. Natural language generation capabilities allow you to create project summaries in plain language. This makes it easy to interpret reports. Analytics team members can add project notes and comments to the insights report to facilitate communication between team members. SAS allows you to embed open source code into an analysis and call open-source algorithms seamlessly within its environment. This allows for collaboration within your organization as users can program in the language they prefer. SAS Deep Learning with Python (DLPy) is also available on GitHub.

Access, explore, and prepare data while discovering new patterns and trends. SAS Visual Data Science allows you to create and share interactive visualizations and reports using a single interface. It uses machine learning, text analysis, and econometrics to improve forecasting and optimization. Additionally, it registers SAS and open source models within projects and as standalone models. Visualize your data and find relevant relationships. You can create and share interactive dashboards and reports, and use self service analytics to quickly assess possible outcomes for better, data-driven decisions. This solution runs in SAS®, Viya®. It allows you to explore data and create or adjust predictive analytical models. Analysts, statisticians, data scientists, and analysts can work together to refine and refine models for each group or segment, allowing them to make informed decisions.

Analytically driven decision flows can be created, embedded and managed at scale in batch or real-time. SAS Data Science Programming allows data scientists who prefer to work only in programmatic mode to access SAS analytical capabilities at every stage of the analytics lifecycle, including data discovery and deployment. Visualize and discover relationships in your data. You can create and share interactive dashboards and reports, and use self service analytics to quickly assess possible outcomes to make data-driven, smarter decisions. This solution runs in SAS®, Viya®. It allows you to explore data and create or adjust predictive analytical models. Analysts, statisticians, data scientists, and analysts can work together to refine and refine models for each group or segment, allowing them to make informed decisions. A comprehensive visual interface allows you to solve complex analytical problems. It handles all aspects of the analytics lifecycle.

Integrate analytics into real time interactions and event-based capabilities. SAS Visual Data Science Decisioning offers robust data management, visualization, advanced analysis, and model management. It supports decision making by creating, embedding, and governing analytically driven decision flows at scale in batch or real-time. It also provides analytics and stream-based decisions to help you uncover insights. A comprehensive visual interface allows you to solve complex analytical problems. It handles all aspects of the analytics lifecycle. SAS Visual Data Mining and Machine Learning runs in SAS®, Viya®. It combines data wrangling and exploration with feature engineering and modern statistical, data mining and machine learning techniques in one, scalable, in-memory processing environment. This web application is a development tool that you can access via your browser.

Data scientists are able to build, improve, or operate end-to–end workflows independently. This allows them to deliver data science projects that are successful. Metaflow can be used with your favorite data science libraries such as SciKit Learn or Tensorflow. You can write your models in idiomatic Python codes with little to no learning. Metaflow also supports R language. Metaflow allows you to design your workflow, scale it, and then deploy it to production. It automatically tracks and versions all your data and experiments. It allows you to easily inspect the results in notebooks. Metaflow comes pre-installed with the tutorials so it's easy to get started. Metaflow allows you to make duplicates of all tutorials in your current directory by using the command line interface.

Hex combines the best of notebooks and BI into a seamless, collaborative interface. Hex is a modern Data Workspace. It makes it easy for you to connect to data and analyze it in collaborative SQL or Python-powered notebooks. You can also share work as interactive data apps or stories. The Projects page is your default landing page in Hex. You can quickly find the projects you have created and those you share with others. The outline gives you an easy-to-read overview of all cells in a project's Logic View. Each cell in the outline lists all variables it defines and any cells that return an output (chart cells or Input Parameters cells, etc.). Display a preview of the output. To jump to a specific position in the logic, you can click on any cell in the outline.

The traditional system of bespoke review of data is not keeping pace with the increasing volume and speed of data. Cornerstone AI has created a self-learning AI platform that automatically creates smarter data rules to organize and clean up your data. This will allow you to access better analytical datasets quicker. Your team is spending too much time and effort cleaning and preparing clinical data. Our platform supports clinical trial, EHR, registry and digital health. Our platform scans every table and data point to determine structure and validity. This allows us to organize your tables and correct any errors. A quick data quality report that highlights the most problematic features in your data. Automated or UI based correction of these errors, API access for connecting directly to your data pipeline, as well as an audit trail for all. We don't keep, aggregate, nor resell your data. Your data is yours, and it is used only for you.

With a single keystroke, switch between editor and command modes. Use the arrow keys to navigate between cells. All the Jupyter shortcuts are available. Fully interactive outputs are available right under the cell. Editing code cells is easy with smart code completion, quick error checking and quick fixes, and easy navigation. You can connect to remote JupyterHub or JupyterLab servers from the IDE. Interactively run Python scripts and arbitrary expressions in a Python Console. You can see the outputs and the state variables in real time. Split Python scripts into code cells using the #%% separator, and run them individually in a Jupyter notebook. Interactive controls allow you to browse DataFrames or visualizations in real time. All popular Python scientific libraries, including Plotly and Altair, ipywidgets and others, are supported.

All teams can independently access key insights by simply talking. No data literacy or training required. Crystal can be customized to meet the needs of an organization. This means that crystal and your teams can work together to generate actionable insights. Crystal monitors your data 24 hours a day and can alert you to important changes. This ensures that you get the answers you need as well as those you didn't know were there. Your teams can access instant insights on both mobile and desktop without having to go through reports. With crystal's user-friendly setup, you can bring your use case to life in days and not months. You can increase the value of your existing BI investments by setting up a low-code, no-code data source connection.

With a fully automated cloud infrastructure, experts can explore data and write stable codes at the same time. Zerve’s data science environment gives data scientists and ML teams a unified workspace to explore, collaborate and build data science & AI project like never before. Zerve provides true language interoperability. Users can use Python, R SQL or Markdown in the same canvas and connect these code blocks. Zerve offers unlimited parallelization, allowing for code blocks and containers to run in parallel at any stage of development. Analysis artifacts can be automatically serialized, stored and preserved. This allows you to change a step without having to rerun previous steps. Selecting compute resources and memory in a fine-grained manner for complex data transformation.

Drag-and-drop and generative AI tools enable analysts to prepare and blend data up 100 times faster than traditional solutions. Self-service analytics platform gives analysts the power to remove costly bottlenecks and empowers them. Alteryx Designer, a self-service analytics platform, empowers analysts by allowing them to prepare data, blend it, and analyze it using intuitive drag-and-drop tools. The platform integrates with over 80 data sources and supports 300 automation tools. Alteryx Designer, with its focus on low-code/no-code capabilities, allows users to create analytic workflows easily, accelerate analytics processes using generative AI and generate insights, without needing to have advanced programming skills. It is also highly versatile, allowing the output of results into over 70 different tools. It is designed to be efficient, allowing businesses to speed up the preparation and analysis of data.

Microsoft continues to invest in R development, not only in the Machine Learning Server release but also in the Microsoft R Client and Microsoft R Open. R and Python support can be found in SQL Server Machine Learning Services for Windows and Linux. R support is also available in Azure SQL Database. R components are compatible with previous versions. Existing R scripts should run on older versions of R, with the exception if they have dependencies on platforms or packages that are no longer supported or known issues that need to be fixed or code changed. Microsoft R Open is an enhanced distribution of R by Microsoft Corporation. Microsoft R Open 4.0.2 is the current release. It is based on R-4.0.2, and offers additional capabilities that improve performance, reproducibility, and platform support. Compatible with all packages, scripts, and applications that use R-4.0.2.

Combine Data Science, Business Intelligence and Data Management Capabilities into One Integrated, Self-Serve Platform. Analance is an end-to-end platform with robust and salable features that combines Data Science and Advanced Analytics, Business Intelligence and Data Management into a single integrated platform. It provides core analytical processing power to ensure that data insights are easily accessible to all, performance remains consistent over time, and business objectives can be met within a single platform. Analance focuses on making quality data into accurate predictions. It provides both citizen data scientists and data scientists with pre-built algorithms as well as an environment for custom programming. Company - Overview Ducen IT provides advanced analytics, business intelligence, and data management to Fortune 1000 companies through its unique data science platform Analance.

H2O.ai, the open-source leader in AI and machinelearning, has a mission to democratize AI. Our enterprise-ready platforms, which are industry-leading, are used by thousands of data scientists from over 20,000 organizations worldwide. Every company can become an AI company in financial, insurance, healthcare and retail. We also empower them to deliver real value and transform businesses.