Glossary¶

Use this page to record terms and ideas that help you understand professional analytics projects.

This project covers OLAP reporting: querying the data warehouse using slice, dice, and drilldown operations.

Pro-tip: Expand the VS Code Outline view (below the navigator on the right) to see this file organization at-a-glance.

OLAP Concepts¶

OLAP¶

OLAP (Online Analytical Processing) is an approach to analyzing multidimensional business data at different levels of detail. OLAP supports slice, dice, drilldown, and rollup operations used in BI reporting.

slice¶

A slice filters data to one value of a single dimension. For example, filtering sales to only the Electronics category is a slice operation. Slicing isolates a subset for focused analysis.

dice¶

Dicing filters data across two or more dimensions simultaneously. For example, filtering by both product category and customer region is a dice operation. Dicing reveals which combinations of dimensions drive performance.

drilldown¶

Drilldown moves from a high-level summary to more granular detail. For example, moving from annual sales totals to monthly totals is a drilldown operation. Drilldown helps identify which time periods or subcategories explain a trend.

rollup¶

Rollup is the opposite of drilldown. It moves from granular detail to a higher-level summary. For example, aggregating daily sales into monthly totals is a rollup.

data cube¶

A data cube is a multidimensional view of data organized by dimensions and metrics. It allows analysts to quickly pivot, slice, and dice large datasets. A data cube can be pre-computed or generated on the fly from a warehouse.

pivot table¶

A pivot table reorganizes data by placing dimension values as row and column headers and aggregated metrics as cell values. It is a common way to compare performance across two dimensions at once.

SQL for Reporting¶

SELECT¶

SELECT specifies which columns to return in a query result. SELECT * returns all columns. Good practice is to name only the columns you need.

WHERE¶

WHERE filters rows based on a condition. WHERE Category = 'Electronics' returns only Electronics rows. WHERE can implement a slice by selecting one value from a dimension.

GROUP BY¶

GROUP BY organizes rows into groups based on one or more columns and allows aggregation functions to be applied to each group. GROUP BY Region with SUM(SaleAmount) gives total sales per region.

ORDER BY¶

ORDER BY sorts query results by one or more columns. ORDER BY TotalSales DESC sorts results from highest to lowest.

strftime¶

strftime is a DuckDB function that formats date values as strings. strftime(SaleDate, '%Y-%m') extracts the year and month from a date. It is used to group sales by month for trend analysis.

subquery¶

A subquery is a SELECT statement nested inside another SELECT statement. It can be used to filter, rank, or compute intermediate results before the outer query runs.

Visualization for Reporting¶

seaborn¶

seaborn is a Python visualization library built on matplotlib. It provides high-level chart functions with sensible defaults. sns.barplot() and sns.lineplot() are used throughout this course.

bar chart¶

A bar chart compares values across categories using rectangular bars. It is the most common chart for comparing totals by dimension.

line chart¶

A line chart shows values over time by connecting data points with a line. It is the standard chart for visualizing trends and time series.