Course Outline
Introduction to Data Science
This section establishes a foundational understanding of data science by defining the discipline, outlining the operational workflow, and demonstrating its application to government challenges. The chapter concludes with guidance on organizing data teams to align with organizational objectives and ensure effective for government service delivery.
Analysis and Visualization
This section examines methods for exploring and visualizing data through interactive dashboards. It details dashboard components, protocols for fulfilling specific reporting requests, and procedures for ad hoc data inquiries. Additionally, it addresses A/B testing as a rigorous analytical instrument designed to reduce uncertainty in policy and operational decision-making.
Data Collection and Storage
Building on the established data science workflow, this section focuses on the initial phase of data acquisition. It identifies diverse data sources available to federal agencies and outlines best practices for the secure storage and management of collected information.
Prediction
In this final module, we address advanced analytical techniques, specifically machine learning. Topics include supervised and unsupervised learning models, as well as clustering algorithms. The section also explores specialized applications such as time series forecasting, natural language processing, deep learning, and explainable artificial intelligence (AI) to support evidence-based governance.
Testimonials (1)
Hands-on exercises related to content really helps to understand more about each topic. Also, style of start class with lecture and continue with hands-on exercise is good and helpful to relate with the lecture that presented earlier.
