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Course Outline

Day 1:

Module 1: Overview of the KNIME Analytics Platform for Government

  • Installation and Configuration
  • Launching and Customizing the KNIME Analytics Platform
  • Understanding Nodes, Data, and Workflows
  • The Data Science Lifecycle for Government Operations

Module 2: Data Access for Government

  • Reading Data from Files
  • Accessing REST Services for Data Integration

Module 3: ETL and Data Manipulation for Government

  • Row and Column Filtering Techniques
  • Aggregation Methods
  • Joining and Concatenating Datasets
  • Data Transformation: Conversion, Replacement, Standardization, and Feature Engineering
  • Preparing Data for Time Series Analysis in Government Applications

Day 2:

Module 4: Exporting Data for Government

  • Writing Data to Files
  • Generating Reports for Government Use

Module 5: Data Visualization for Government

  • Interactive Univariate Visual Exploration for Decision-Making
  • Interactive Multivariate Visual Exploration for Comprehensive Analysis
  • Advanced Visualization Features for Enhanced Insights

Module 6: Predictive Analytics Using KNIME for Government

  • Basic Concepts of Data Mining for Government Applications
  • Regression Techniques
  • Decision Tree Algorithms and Variants
  • Model Evaluation and Validation for Government Projects

Day 3:

Module 7: Controlling Workflow Execution for Government

  • Parameterizing Workflows with Flow Variables
  • Re-executing Workflow Segments Using Loops
  • Optimizing and Cleaning Up Workflows for Efficiency

Module 8: Hands-On Case Study Using KNIME Analytics Platform for Government
 

Requirements

Recommended

  • A foundational understanding of data interpretation.
  • Experience with basic data processing techniques.

Audience

  • Data analysts
  • Data scientists
  • Business analysts
This training is designed to enhance the skills of professionals in data analysis and interpretation, ensuring they are well-equipped to handle data-related tasks efficiently for government operations.
 21 Hours

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