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Course Outline

Introduction

Getting Started with KNIME for Government

  • Overview of KNIME
  • KNIME Analytics for Government Operations
  • KNIME Server for Enhanced Data Management

Machine Learning in the Public Sector

  • Computational Learning Theory for Government Applications
  • Algorithms for Analyzing Government Data

Preparing the Development Environment for Government Use

  • Installing and Configuring KNIME for Government Systems

KNIME Nodes for Government Data Analysis

  • Adding Nodes to Workflows
  • Accessing and Reading Government Data
  • Merging, Splitting, and Filtering Data Sets
  • Grouping and Pivoting Data for Enhanced Analysis
  • Cleaning Data to Ensure Accuracy

Modeling Government Data with KNIME

  • Creating Workflows for Government Projects
  • Importing Data from Various Sources
  • Preparing Data for Analysis
  • Visualizing Data for Clear Insights
  • Creating a Decision Tree Model for Policy Analysis
  • Working with Regression Models for Predictive Analytics
  • Predicting Data Trends and Outcomes
  • Comparing and Matching Data Sets for Validation

Advanced Learning Techniques for Government Applications

  • Utilizing Random Forest Techniques for Robust Analysis
  • Implementing Polynomial Regression for Complex Data Relationships
  • Assigning Classes for Categorical Data Analysis
  • Evaluating Models to Ensure Reliability and Accuracy

Summary and Conclusion for Government Users

Requirements

  • Proficiency with Python
  • Knowledge of R programming

Audience for government

  • Data Scientists
 14 Hours

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Price per participant

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