KNIME for Data Experts

Accelerate time to insight, collaborate with other disciplines, and empower upskilling across business functions, so that you can focus on strategic projects.

  • Connect to any data type from any data source, access any advanced analytic or ML technique, and get the choice to code in any language with an open platform

  • Facilitate collaboration between disciplines by bringing data experts and business experts seamlessly together with a low-code/no-code interface

  • Deploy data science as interactive data apps, reports, or services for technical and business end-users, to provide in-time insights

  • Promote self-sufficiency and upskill non-experts through visual modeling and reusable analytical blueprints. Get back time to focus on data science

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Why KNIME for Data Experts

Handle more requests faster

Accelerate analyses with KNIME’s low-code/no-code interface, resulting in faster time to value and the ability to handle more requests. Combine, shape and analyze all your data as well as create, test, train and deploy machine learning models in a single, end-to-end platform. Deploy your projects to any number of users in the same intuitive platform, without worrying about coding or infrastructural details.

Eliminate repetitive work by creating reusable, automated workflows. Provide custom views of data and enable ad-hoc analysis through parameterized data apps without IT dependence.

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Collaborate across teams and disciplines

Bridge the gap between disciplines with KNIME as a lingua franca that allows data scientists, data engineers, data analysts, data experts, business experts, scripters, and non-scripters to work together seamlessly to keep up with changing business requirements.

Enable wider analytics access and self-service with a low-code/no-code interface. Facilitate collaboration between business and data teams by using easy to follow visual workflows. Create, save and share Python scripts, analytical models, or components – abstracted segments of workflows – privately with your team or or publicly with your wider organization as ready-to-use blueprints.

Reskill and upskill

Free up your bandwidth by empowering business users and non-scripters across the organization to be self-sufficient in data analysis with a low-code/no-code interface and reusable analytical workflows. Broaden your data science skills with access to the latest and most sophisticated analytics techniques, popular ML libraries such as Keras or Tensorflow for deep learning, H2O for high performance machine learning, and R and Python for coding in one uniform platform with KNIME’s open source approach.

Enhance your knowledge by gaining insights and browsing workflow samples from the expert community through the KNIME Community Hub and Forum. Provide blueprints that non-experts can learn from, enabling them to upskill and get to insights independently while allowing you to focus on more advanced solutions.

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Spend your time where it makes the most impact

Code when you want, don't code when you don't want. Abstract away software development with KNIME’s low-code/no-code interface that enables you to focus on actual data science - building and deploying analytical models efficiently.

Utilize pre-built components for feature engineering and selection, hyperparameter optimization, model interpretability, and more to automate the boring pieces and enable fast prototyping and testing. Get the flexibility to build and share your own custom nodes for any additional functionality that you desire.

Ensure model quality and accuracy

Validate models with performance metrics including Accuracy, R2, AUC, and ROC and carry out cross validation to guarantee model stability. Explain machine learning models with LIME, Shap/Shapley values. Understand model predictions with the interactive partial dependence/ICE plot.

Automatically document each step of your analysis visually to ensure that everything happening to your data process is explainable - including where it comes from, how it has been transformed, what modeling approaches have been used, and more. Retain accuracy, make model maintenance straightforward, and mistakes easier to fix with version control, debugging, tracking, and auditing.

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Recommended Resources

Find Answers to Your Data Science Questions and Interact with the KNIME Community
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Learn to apply XAI: Partial Dependence & ICE - Codeless
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How to Start Using the Python Script Node in Production Environments
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