Data storytelling and visualization

Identify government databases and public records relevant to investigative stories.

Locate and download datasets from official statistical agencies and international organizations.

Submit Freedom of Information requests effectively, including under Hungarian and EU procedures.

Extract tabular data from PDF documents using a specialized tool.

Assess the quality and limitations of a dataset for journalistic purposes.

Document data provenance and maintain source records for fact-checking.

Describe the structure and purpose of a pandas DataFrame and Series.

Load data from common file formats (e.g., CSV, Excel) into a pandas DataFrame.

Identify and handle missing values in datasets using pandas.

Detect and remove duplicate entries from a dataset.

Correct common formatting issues in numeric and date fields.

Standardize text data (e.g., names, addresses, categorical variables) for analysis.

Merge datasets from different sources using common identifiers.

Write a Python script to automate a sequence of data cleaning tasks on a raw dataset.

Apply pandas filtering and selection methods (boolean indexing, .loc, .query()) to isolate data subsets for journalistic inquiry.

Apply complex SQL queries (e.g., joins, aggregations, window functions) to test a journalistic hypothesis.

Calculate and interpret summary statistics (mean, median, percentiles) to understand data distributions.

Identify outliers and anomalies that may indicate newsworthy stories.

Compare groups using crosstabs and aggregations to find patterns.

Generate a basic histogram and scatter plot using a Python library to inspect a variable's distribution and the relationship between two variables.

Document initial findings and formulate questions for further investigation.

Calculate and explain percentage changes, rates, and ratios in stories.

Identify when data is skewed and choose appropriate measures of central tendency (mean vs. median).

Interpret a p-value from a published study and explain its practical meaning to readers.

Explain the difference between correlation and causation and identify this fallacy in real-world examples.

Describe Simpson's paradox and how it can lead to misleading conclusions using a practical dataset.

Interpret margins of error in polls and surveys and explain their implications for readers.

Recognize common situations where sample sizes may be too small to support strong conclusions.

Calculate year-over-year and period-to-period changes in time series data.

Adjust monetary values for inflation to make fair historical comparisons.

Identify and describe seasonal patterns in time series data.

Calculate moving averages to smooth noisy data and identify underlying trends.

Visually compare multiple time series to identify patterns and divergences.

Interpret the output of a statistical trend test to determine if an observed pattern is meaningful.

Describe the purpose and common structure of census datasets.

Access and extract a specific demographic dataset from a national census data portal.

Calculate per-capita rates to enable fair geographic comparisons.

Load and inspect vector-based geographic data (e.g., shapefiles, GeoJSON) using Python.

Join tabular demographic data with geographic boundaries for mapping.

Group a dataset by demographic variables (e.g., race, gender, age) and calculate summary statistics for each group.

Aggregate data at different geographic levels (e.g., neighborhood, city, region).

Interpret spatial patterns and potential stories from a choropleth map.

Choose appropriate chart types for different data stories (e.g., bar, line, scatter, map).

Apply core design principles (e.g., data-ink ratio, gestalt principles) to make visualizations clear and accessible.

Identify and avoid common misleading visualization techniques (e.g., truncated axes, inappropriate scales).

Use color effectively in visualizations and ensure accessibility for colorblind readers.

Write clear headlines, labels, and annotations that guide readers through a visualization.

Determine when a well-structured table is more appropriate than a chart.

Create static charts (bar, line, scatter) for publication using a library like Matplotlib or Plotly.

Customize chart styling to meet journalistic standards (fonts, colors, labels).

Create small multiples to facilitate comparison across categories.

Create choropleth maps to visualize geographic patterns.

Enhance Python-generated charts with interactive elements like tooltips and filters using a library like Plotly.

Export publication-ready visualizations in static (PNG, SVG) and interactive (HTML) formats.

Embed an interactive Python visualization into a simple web page.

Write a Python script using BeautifulSoup to scrape and parse data from a static HTML table.

Write a Python script to fetch and process data from a JSON-based public API.

Perform frequency analysis and keyword extraction on a collection of documents to identify story leads.

Represent relational data as a network graph using a Python library (e.g., NetworkX).

Calculate and interpret a basic centrality measure to identify key entities in a network.

Translate statistical findings into a compelling narrative lede.

Structure a data story with a clear beginning, middle, and end.

Integrate data evidence with human sources and anecdotes to create a complete picture.

Write accurate and precise statements about data and statistical findings.

Contextualize numbers with analogies and comparisons to make them meaningful for a general audience.

Integrate charts and tables effectively within the story flow.

Pitch data story ideas effectively to editors.

Create a methodology box explaining data sources, cleaning steps, and analysis.

Publish data and code for transparency and reproducibility when appropriate.

Select 2-3 completed data stories to serve as core portfolio pieces.

Write a polished project summary and methodology for each selected portfolio piece.

Choose a platform (e.g., GitHub Pages, personal site) and structure the portfolio's content.

Publish an initial version of the portfolio and outline a plan for its future updates.