Course Outline
Introduction to Data Science
This module begins with a formal definition of data science. It examines the data science workflow and the application of these methodologies to resolve operational challenges in public sector contexts. The section concludes by addressing organizational structures for data teams, ensuring alignment with agency requirements for government operations.
Analysis and Visualization
This chapter addresses the exploration and visualization of data using dashboard interfaces. It details the constituent elements of effective dashboards and the protocols for issuing formal requests. Furthermore, the content covers ad hoc data inquiries and A/B testing, presenting these as rigorous analytic instruments that mitigate risk in strategic decision-making.
Data Collection and Storage
Building upon the understanding of the data science workflow, this section examines the initial phase: data acquisition. It identifies relevant data sources available to institutions and outlines the methods for securely storing data once collected, ensuring compliance and accessibility for government use.
Prediction
The final chapter focuses on machine learning, a critical area of advancement in data science. It covers both supervised and unsupervised learning frameworks, as well as clustering techniques. The discussion extends to specialized domains, including time series forecasting, natural language processing, deep learning, and explainable artificial intelligence, all applicable to predictive modeling for government services.
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.
