A Practical Introduction to Data Science Training Course

Introduction

• The Data Science Process for government operations
• Roles and responsibilities of a Data Scientist in the public sector

Preparing the Development Environment for Government Use

• Libraries, frameworks, languages, and tools for government applications
• Local development environments tailored for government needs
• Collaborative web-based development platforms suitable for government projects

Data Collection for Government Operations

• Different Types of Data
  • Structured Data
    • Local databases in government systems
    • Database connectors for government datasets
    • Common formats: xlxs, XML, Json, csv, etc.
  • Unstructured Data
    • Clicks, sensors, and mobile devices in government applications
    • APIs for government services
    • Internet of Things (IoT) data in public sector initiatives
    • Documents, images, videos, and audio files for government records
• Case study: Collecting large amounts of unstructured data continuously for government use

Data Storage Solutions for Government

• Relational databases for structured government data
• Non-relational databases for flexible government datasets
• Hadoop: Distributed File System (HDFS) for large-scale government data storage
• Spark: Resilient Distributed Dataset (RDD) for efficient government data processing
• Cloud storage options for secure and scalable government data management

Data Preparation for Government Analysis

• Ingestion, selection, cleansing, and transformation of government data
• Ensuring data quality—correctness, meaningfulness, and security in government datasets
• Exception reports for government data integrity

Languages used for Data Preparation, Processing, and Analysis in Government

• R Language
  • Introduction to R for government analysts
  • Data manipulation, calculation, and graphical display in government contexts
• Python
  • Introduction to Python for government data scientists
  • Manipulating, processing, cleaning, and crunching government data

Data Analytics for Government Decision-Making

• Exploratory Analysis
  • Basic statistics for government datasets
  • Draft visualizations to understand government data
  • Understanding the implications of government data
• Causality in government data analysis
• Features and transformations for government datasets
• Machine Learning for Government Applications
  • Supervised vs. unsupervised learning for government use cases
  • Selecting appropriate models for government data analysis
• Natural Language Processing (NLP) in government contexts

Data Visualization for Government Communication

• Best Practices for government data visualization
• Selecting the right chart type for government data
• Color palettes suitable for government presentations
• Advanced Visualization Techniques
  • Dashboards for real-time government data monitoring
  • Interactive visualizations to enhance government data communication
• Storytelling with government data to inform policy and decision-making

Summary and Conclusion for Government Data Science Initiatives