The Complete Data Visualization & Storytelling Bootcamp

Master the basics of data visualization and data storytelling to turn charts into compelling narratives, reduce cognitive load, and speed time to insight.

Explore why data visualization matters through Anscombe's Quartet, showing how charts reveal insights hidden by summary statistics, reduce cognitive load, and enhance storytelling with charts and sparklines.

Learn how great data storytelling turns analysis into action and bridges the last mile, delivering clarity, persuasion, and career impact through engaging visuals and narratives.

Trace the history of data visualization from van Langren in 1644 through 19th-century pioneers like Playfair, Minard, and Nightingale, to the digital rebirth that shaped modern data storytelling and tools.

Think about chart selection by balancing basic, intermediate, and advanced charts with data complexity and audience cognitive load, and consider custom charts for unique visualization needs.

Explore a use-case based approach to chart selection, from time series with line charts to magnitude, correlation, rankings, spatial, composition, distribution, and deviation, including Sankey, mosaic, and other visuals.

Explore Flourish's vast library of chart types, upload and curate data sets, and use the data and preview tabs to format data-led stories.

Install Metabase locally with Docker and run the Metabase container on port 3000. Access localhost:3000 to onboard, explore the sample database, and begin building visualizations.

Explore the basics of SQL and its role in data analysis, learn the anatomy of a query, and how to use joins and aggregate functions on database tables.

Master the structure of a sql query: select, from, where, order by, limit. Apply through an example from users: age under 50, order by age descending.

Explore aggregate functions like count, sum, average, min, and max to summarize data and gain insights, with group by, where, order by, and joins.

Explore real sql queries in Metabase using the sample data. Build queries with accounts and invoices, applying where, joins, and order by to identify accounts with more than one seat.

Count the number of accounts with more than two seats using the count aggregate function and a where clause on the accounts table. Alias the result as number of accounts.

Explore counting invoices for accounts sourced from Facebook by performing an inner join of invoices and accounts, filtering on source equals Facebook, and using count with group by.

Count invoices by month and plan using date trunk on the date received, then group by month and plan and order results ascending.

Master the long versus wide data structures and how they drive charting and analysis. Learn to pivot and unpivot data, and when to use tools like Power Query for transformations.

Visualize order data with a bar chart by state, a category pie chart, and a bubble scatter plot across google sheets, flourish, and metabase, using pivot tables and data aggregation.

Learn to build bar, pie, and scatter plots in Flourish. Use the same data structures from Google Sheets to tell data stories with interactive visuals.

Learn to create common charts in Metabase, including bar charts by state, donut pie charts by category, and scatter plots with filters and saving to a dashboard.

Start with the question, audience, and decision; then clean, structure, and address common data issues to prepare trustworthy visualizations, using a checklist and Metabase.

Standardize column names with consistent case and readability, using snake_case when helpful, and ensure correct data types for each column, including dates, numbers, categories, and booleans.

Identify and handle missing values with deliberate strategies like removal, imputation, or preserving nulls for plotting. Examine duplicates and standardize categories to ensure consistent labels across joins and visuals.

Aggregate data for charts through cleaning and aggregations, derive visualization-ready fields, bucket data for axes, and perform quality checks to produce accurate histograms and well-documented visuals.

Use this SQL cheat sheet to clean and process data, covering renaming columns, changing data types, imputation with case statements, removing nulls and duplicates, and deriving year or buckets.

Structure data for each chart type in Metabase by identifying required fields, aggregations, and time elements; visualize with example charts, like pie and scatter, using the question builder.

Explore four data visualization tool types: spreadsheets, BI tools, presentation software, and open-source libraries, with examples like Excel, Tableau, Flourish, and Matplotlib.

Learn how to save SQL queries in MetaBase and create data visualizations from those queries. Then assemble dashboards, use common charts, and manage personal collections and history for collaboration.

Learn how to create data visualizations from a saved query in Metabase, edit and hide columns, and use conditional formatting with color spectrums to highlight high and low values.

Explore how to create and customize a bar chart in Metabase for time-series data, including axis labels, rotation, stacking (100% stacking), and a goal line.

Learn to build Metabase dashboards by saving charts as questions, creating new dashboards, and adding and arranging charts with headers, text descriptions, and data insights.

Discover metabase's question builder for querying data, joining tables, filtering results, and visualizing insights without SQL, using the orders table and time-series charts to group by created at.

Master joining tables in the Metabase question builder using pre-mapped relationships and foreign keys to filter by state and source, and view a time series of orders by day.

Connect your own data to Metabase by adding a database via the Databases panel or admin settings. Configure host, port, database name, and credentials to populate the connection.

Learn to tell data stories by designing with purpose, decluttering charts, and using color, contrast, and annotations to guide audience understanding while respecting chart baselines.

Explore three chart creator personas—the good, truth tellers with data visualization fundamentals; the bad, deceivers who misdirect; and the ugly, untrained makers who overlook fundamentals.

Examine how bad actors deceive via charts, from unannotated multi-axis visuals to misaligned data and baselines in congressional hearings. Compare truthful alternatives like bar and slope charts that reveal context.

Identify common charting mistakes in data visualization, from misusing donut and 100% stacked charts to complex sankey visuals, and learn practical fixes like bar charts, grids, recoding, and clearer storytelling.

Learn how to tailor data storytelling presentations for synchronous live settings or asynchronous reports, choosing dynamic builds and animations or static formats, and adapting visuals to audience size and environment.

Explore asynchronous delivery of reports, dashboards, and spreadsheets, with data dictionaries or quick explanations, platform familiarization, appropriate cadence, and awareness of audience seniority and data security.

Review the course foundations in data visualization, chart types, data structures, SQL basics, and data viz tools; highlight key learnings on data storytelling, chart selection, and data wrangling.