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SQL row grain: stop double-counting orders before you query
A SQL row's grain is the real-world thing that one row represents. An order table may contain one row per order; an order-item table contains one row per item line. Joining those tables changes the rows you are adding. I
SQL running totals: choose the correct window frame
A running total depends on both ordering and the window frame: the set of rows included for each result. A row-by-row cumulative amount is different from a cumulative amount that includes every peer sharing the current d
SQL slowly changing dimensions in a historical customer report
A customer can change region while their earlier purchases remain unchanged. Joining every purchase to today's customer record reallocates historical sales to the current region. If the report asks where the customer bel
SQL subqueries versus CTEs: make an audit-friendly query
A subquery is a query used within another SQL statement. A common table expression, or CTE, gives a query result a name within a statement's WITH clause. Both can express intermediate steps. Choose the form that makes th
SQL text cleaning: normalize categories without merging different people
Text cleaning should preserve the meaning required by the analysis. Trimming whitespace and mapping approved category variants can improve a region report. Applying the same aggressive transformation to customer names or
SQL versus pandas versus Spark: choose from measured constraints
SQL, pandas and Spark overlap in transformation capability but run in different environments. Choose from data size, existing platform, concurrency, latency, reliability and team skill. Distributed syntax is not evidence
Standard deviation versus standard error with a simulation
Standard deviation describes variation among observations. The standard error of a mean describes variation in the estimated mean across repeated samples under a specified sampling model. They have the same units but ans
Stationarity: what differencing changes and what it loses
Many time-series models assume that relevant statistical behavior is stable over time. Differencing is one way to remove a changing level: replace each observation with the change from the previous observation. It can ma
Stemming versus lemmatization for a specific task
Stemming removes character suffixes using rules; lemmatization maps inflected forms to a dictionary base form, often using part of speech. Both reduce vocabulary, but both can merge distinctions or create awkward outputs
Stockout analysis with incomplete availability data
Zero sales do not prove a stockout. Customers may not have wanted the item, the store may have been closed, or the sales feed may be incomplete. Likewise, a missing availability record is not evidence that stock was avai
Supplier delivery performance with promised-date changes
On-time delivery depends on which promise you compare with the actual receipt. If a supplier moves a promised date after missing it, a dashboard using only the latest date can make the same delivery appear on time. Prese
Support vector machines: choose a kernel with validation
A kernel changes the relationships an SVM can represent. Choose it using a validation design that matches the task, with preprocessing fitted only on training data. A nonlinear kernel is not automatically better because
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