Introduction to Data Analysis for Government

Identify and articulate a well-defined problem through a project charter, clarifying the scope, stakeholders, data sources, and deliverables for a free smoke alarm program.

Identify a well-defined problem by clarifying why it matters, scoping what’s needed, and asking questions to your boss and coworkers to craft a concise problem statement.

Explore your problem in depth to sharpen the problem statement and identify data needs, then interview colleagues and review online research to predict smoke-detector presence in buildings.

Develop a clear model and hypothesis to predict smoke alarm installation using data analysis, conditional statements, deductive and inductive reasoning, and brainstorming building conditions.

Navigate the data analysis crossroad by gathering data to test a hypothesis about a conceptual model, using data to refine which approach suits the problem.

Refine your problem with a conceptual model and hypotheses, then identify qualitative and quantitative data by translating attributes into measurable facts from public government data portals.

Identify the kinds of data and the discrete activities of data analysis in government, and learn places to start looking for public data.

Identify fact from opinion and assess data trustworthiness by examining sources, questions, and potential biases; avoid cherry-picking data and map local trends to national data.

Download and inspect the 2015 national CSP data file, a comma separated values format, open the household file in Excel, and learn to handle incomplete data and cleaning for analysis.

Explore descriptive statistics in part 1 with government data as analysts build intuition for data flaws and reveal what the dataset says about the world by walking through its details.

Walk through creating a working copy in Excel, then use formulas to compute descriptive statistics like max, min, and count, while inspecting a clean dataset.

Generate descriptive statistics to build the foundation of your mental model for the analysis, describe the data without inferring or predicting, and inform your next steps.

Explore the distribution, central tendency, and standard deviation through histograms. Learn to bin continuous data, recognize skewed and normal distributions, and interpret income data and building age.

Explore three measurements of central tendency—the mean and the median—to derive a single number that describes what is typical in your data.

Learn how mean, median, and mode describe data distributions, compute averages in Excel, and choose appropriate central tendencies for normal and skewed data.

Examine the standard deviation and a notable discovery from descriptive statistics for this government data in part 4 of the descriptive statistics module.

Explore standard deviation and mean to describe data variation around central tendency. Use Excel tools like standard deviation and average if to test hypotheses about building age and smoke alarms.

Decide which graphs and data to present, and outline conclusions and recommendations from descriptive statistics for reporting to the team.

Investigate the dataset to identify building age patterns and demographics like young children or elderly residents, report findings, and refine a predictive model to target neighborhoods for free smoke detectors.