The objective of this course is to equip participants with the quantitative and computational skills needed to conduct rigorous, evidence-based urban data analysis. Upon completion, participants will be able to:

• Understand the principles of urban data science, data sources, and the ethics of urban data use.
• Clean, transform, and manage complex, high-dimensional urban datasets (e.g., transit ridership, 311 calls).
• Apply descriptive and inferential statistics to analyze urban trends and test policy hypotheses.
• Utilize computational tools (e.g., Python/R basics) for data wrangling and spatial statistics.
• Develop clear, compelling data visualizations and dashboards for communicating policy insights.
• Apply basic predictive modeling techniques (e.g., regression) to forecast urban phenomena.
• Conduct ethical, rigorous, and transparent data analysis to support planning and policy decisions.

• Urban Planners and Policy Analysts with a quantitative focus
• Chief Data Officers and Data Scientists in Government
• Transportation and Public Health Analysts
• Students in Data Science, Statistics, or Urban Planning
• Consultants specializing in performance measurement
• Municipal Staff involved in budgeting and resource allocation
• Researchers studying urban systems and social dynamics

• Hands-on Computational Labs using Python or R for data wrangling and statistical modeling
• Group Activities: Conducting a full data analysis project on a real-world urban dataset
• Case Studies of data-driven policy changes and their outcomes (e.g., optimizing bus routes)
• Individual Exercises: Developing a predictive regression model for property values or crime rates
• Expert discussions on data ethics, privacy, and open data policy
• Workshops on creating professional data visualizations and interactive dashboards

Unit 1: Foundations of Urban Data Science

• The Urban Data Ecosystem: sources (sensors, administrative, social media, census) and challenges (volume, velocity)
• Principles of open data, data governance, and the ethics of algorithmic decision-making
• The challenge of data privacy, anonymity, and bias in urban data collection
• Establishing a data-driven culture in public sector organizations

• Introduction to computational environments (e.g., Jupyter Notebooks, RStudio)
• Basics of data structures and manipulation using Python (Pandas) or R (Tidyverse)
• Data cleaning, transformation, and management of messy, real-world urban data
• Version control and collaborative coding best practices (e.g., Git/GitHub basics)

Unit 2: Descriptive and Inferential Statistics

• Measures of central tendency, dispersion, and distribution in urban datasets
• Techniques for exploratory data analysis (EDA): histograms, box plots, scatter plots
• Time series analysis basics: identifying trends, seasonality, and anomalies in urban data
• Understanding sampling methods, probability, and hypothesis testing in policy research

• Introduction to linear regression: modeling relationships between urban variables (e.g., income and commute time)
• Interpreting regression coefficients, R-squared, and assessing model fit and assumptions
• Logistical regression and its use in modeling binary outcomes (e.g., homeowner vs. renter)
• Introduction to spatial statistics: Tobler's First Law and addressing spatial autocorrelation

Unit 3: Data Visualization and Communication

• The grammar of graphics and principles of effective data visualization (e.g., Tufte's principles)
• Choosing the right visualization (bar charts, line plots, heatmaps) for the policy question
• Using computational libraries (e.g., Matplotlib, Seaborn, ggplot2) for static and interactive plots
• Designing for accessibility and ensuring visualizations do not mislead or misrepresent data

• Developing data narratives and translating analytical results into clear policy insights
• Creating interactive dashboards and web applications for public engagement (e.g., Tableau, R Shiny)
• Strategies for communicating uncertainty and limitations of the analysis to non-technical audiences
• The role of automated reporting and performance monitoring dashboards

Unit 4: Advanced Applications in Planning

• Predictive modeling for housing demand, traffic congestion, or public service demand (e.g., 311 calls)
• Clustering analysis and machine learning basics for identifying neighborhood types and patterns
• Application of causal inference techniques to evaluate policy interventions (e.g., A/B testing, diff-in-diff)
• Simulation and scenario planning using data-driven models (e.g., growth forecasting)

• Geocoding and mapping non-spatial data for integration with GIS
• Introduction to spatial regression and geographically weighted regression (GWR)
• Analyzing accessibility and service areas using network data and routing algorithms
• Working with non-traditional spatial data (e.g., remote sensing, geotagged social media)

Unit 5: Project and Institutionalization

• Best practices for data project workflow, documentation, and metadata creation
• Ensuring research reproducibility and auditability in public sector analysis
• Developing a final, comprehensive analytical report that guides policy decisions

• Strategies for building a municipal data platform and data governance team
• Case studies of effective use of data analytics in public sector innovation
• Addressing the skills gap: hiring and training for data-savvy planners and analysts