Course Outline

Quick Overview

  • Data Sources
  • Data Management
  • Recommender Systems
  • Targeted Marketing

Data Types

  • Structured vs. Unstructured Data
  • Static vs. Streamed Data
  • Attitudinal, Behavioral, and Demographic Data
  • Data-Driven vs. User-Driven Analytics
  • Data Validity
  • Volume, Velocity, and Variety of Data

Models

  • Model Development
  • Statistical Models
  • Machine Learning Techniques

Data Classification

  • Clustering Methods
  • k-Groups, k-Means, Nearest Neighbors
  • Ant Colony Optimization, Flocking Behaviors

Predictive Models

  • Decision Trees
  • Support Vector Machines
  • Naive Bayes Classification
  • Neural Networks
  • Markov Models
  • Regression Analysis
  • Ensemble Methods

Return on Investment (ROI)

  • Benefit-to-Cost Ratio
  • Software Costs
  • Development Costs
  • Potential Benefits for Government Operations

Building Models

  • Data Preparation (MapReduce)
  • Data Cleansing
  • Method Selection
  • Model Development
  • Model Testing
  • Model Evaluation
  • Model Deployment and Integration for Government Use

Overview of Open Source and Commercial Software

  • R-Project Package Selection
  • Python Libraries
  • Hadoop and Mahout
  • Apache Projects Related to Big Data and Analytics
  • Commercial Solutions for Government Use
  • Integration with Existing Software and Data Sources

Requirements

A solid understanding of traditional data management and analysis techniques is essential for government operations. This includes proficiency with SQL, data warehouses, business intelligence tools, and Online Analytical Processing (OLAP). Additionally, a foundational knowledge of basic statistics and probability, such as mean, variance, probability, and conditional probability, is crucial for effective data-driven decision-making for government agencies.
 21 Hours

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