KNIME Training and Certification in Copenhagen, Denmark

KNIME training offers participants a comprehensive understanding of data analytics and machine learning using the KNIME Analytics Platform. The course covers how to create data workflows by integrating various data sources, preprocessing data, and applying statistical or machine learning models. Participants will learn to utilize KNIME's visual interface to design and execute analytical pipelines without needing extensive coding skills. The training also explores advanced topics such as text mining, time series analysis, and integrating KNIME with other tools like Python, R, and big data platforms. By the end of the training, learners will be proficient in leveraging KNIME to process complex datasets and generate actionable insights for business and research purposes.

Introduction to KNIME and Data Workflow Design

1. Introduction to KNIME

• Overview of KNIME and its capabilities for data analytics.

• Key components of the KNIME platform: Workflow, Nodes, and Views.

• Setting up KNIME on your system and understanding its interface.

• Hands-on Exercise

2. Creating Data Workflows in KNIME

• Understanding the concept of workflows and nodes in KNIME.

• Basic workflow design: Connecting nodes and executing tasks.

• Hands-on Exercise

3. Data Preprocessing with KNIME

• Cleaning and transforming data: Filtering, sorting, and handling missing values.

• Using KNIME’s data manipulation nodes: String manipulations, column filtering, etc.

• Hands-on Exercise

4. Data Transformation Techniques

• Aggregating, pivoting, and reshaping data with KNIME nodes.

• Exploring data transformations using advanced nodes like “GroupBy” and “Joiner”.

• Hands-on Exercise

Advanced Data Analytics, Integration, and Visualization

5. Integrating Data Sources in KNIME

• Connecting KNIME to various data sources (databases, CSV files, Excel, etc.).

• Importing and exporting data using appropriate nodes.

• Hands-on Exercise

6. Advanced Analytics in KNIME

• Utilizing KNIME’s machine learning, statistical, and clustering nodes.

• Exploring data analysis techniques like decision trees, regression, and clustering.

• Hands-on Exercise

7. Data Visualization and Reporting in KNIME

• Creating visualizations using KNIME’s built-in plotting and charting tools.

• Generating reports with the KNIME Reporting functionality.

• Hands-on Exercise

8. KNIME Workflow Optimization and Best Practices

• Optimizing workflow performance and managing large datasets.

• Best practices for designing efficient workflows in KNIME.

• Hands-on Exercise