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Course Outline
Introduction
- The Data Science Process for government
- Roles and responsibilities of a Data Scientist in the public sector
Preparing the Development Environment for Government
- Libraries, frameworks, languages, and tools for government use
- Local development environments tailored for government needs
- Collaborative web-based development platforms suitable for government projects
Data Collection for Government
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Different Types of Data
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Structured
- Local databases for government agencies
- Database connectors optimized for government systems
- Common formats: xlxs, XML, Json, csv, etc.
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Un-Structured
- Clicks, sensors, smartphones in government applications
- APIs for government services
- Internet of Things (IoT) for government infrastructure
- Documents, pictures, videos, sounds from government sources
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Structured
- Case study: Collecting large amounts of unstructured data continuously for government operations
Data Storage for Government
- Relational databases for government records
- Non-relational databases for flexible government data management
- Hadoop: Distributed File System (HDFS) for large-scale government data
- Spark: Resilient Distributed Dataset (RDD) for efficient government data processing
- Cloud storage solutions for secure government data
Data Preparation for Government
- Ingestion, selection, cleansing, and transformation of government data
- Ensuring data quality - correctness, meaningfulness, and security in government datasets
- Exception reports for government data management
Languages used for Preparation, Processing, and Analysis for Government
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R language for government data science
- Introduction to R for government analysts
- Data manipulation, calculation, and graphical display in government contexts
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Python for government data science
- Introduction to Python for government professionals
- Manipulating, processing, cleaning, and crunching government data
Data Analytics for Government
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Exploratory analysis for government datasets
- Basic statistics for government data
- Draft visualizations for government reports
- Understanding government data
- Causality in government data analysis
- Features and transformations for government datasets
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Machine Learning for government applications
- Supervised vs unsupervised learning for government projects
- When to use which model in government scenarios
- Natural Language Processing (NLP) for government documents and communications
Data Visualization for Government
- Best Practices in government data visualization
- Selecting the right chart for government data
- Color palettes appropriate for government presentations
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Taking it to the next level in government visualizations
- Dashboards for government decision-making
- Interactive Visualizations for government stakeholders
- Storytelling with data for government communications
Summary and Conclusion for Government
Requirements
- A foundational knowledge of database principles for government
- An introductory understanding of statistical methods
35 Hours
Testimonials (1)
Real world knowledge from someone in the industry
