Pandas, SQL, Power BI, Tableau, data visualization
33 articles
A low RMSE doesn't always mean your regression model performs well. Learn why models often struggle with rare or extreme values, how RMSE can hide critical prediction errors, and which evaluation techniques lead to more reliable regression models.
SQL date filters often appear simple, yet subtle mistakes involving BETWEEN, timestamp truncation, time zones, and data types can return missing or unexpected rows. Learn why SQL date range queries fail and how to write reliable, high-performance date filters across modern relational databases.
Is your XLOOKUP formula returning #N/A even though the value clearly exists? The problem may not be your lookup value or range—it could be the match mode argument. Learn how XLOOKUP match modes work, why they produce unexpected results, and how to fix them with practical examples.
Are your Power BI slicers showing incorrect values, blank visuals, or inconsistent totals? The culprit is often an incorrect cross-filter direction between tables. Learn how filter propagation works, why bidirectional relationships cause unexpected behavior, and how to design a clean Power BI data
Does pandas.read_excel() seem to skip rows or load incorrect column names? In many Excel files, the actual header isn't on the first row because of titles, merged cells, or metadata. Learn why this happens, how the header parameter works, and the best ways to import Excel data accurately.
Your Pandas time series suddenly fills with NaN values after calling resample() or asfreq(). In most cases, Pandas isn't losing data—it is exposing missing timestamps in your dataset. Learn why these gaps appear and how to handle them correctly for reliable time series analysis.
Your pandas.merge() operation suddenly doubles or triples the number of rows, even though the merge completes successfully. The cause is usually non-unique join keys that produce many-to-many relationships. Learn why duplicate rows appear after merges and how to validate, diagnose, and fix them
Your HAVING clause looks correct but keeps dropping groups you know should be there. This guide breaks down every common reason HAVING silently eliminates valid rows — NULLs, type mismatches, WHERE vs HAVING confusion — and shows you exactly how to fix each one.
Your CASE WHEN logic looks correct, but the column keeps coming back NULL. This happens more often than you'd expect, and the root causes are almost always subtle — mismatched types, NULL comparison traps, or a missing ELSE clause silently swallowing rows.
Your Tableau dashboard looks complete, but certain rows keep disappearing without any error. If your data spans multiple time zones, a quiet UTC offset mismatch in date filters is likely the culprit. Here's how to catch and fix it.
Most data analysts use Jupyter Notebooks for one-off projects, but the same notebooks can become the foundation of a profitable recurring reporting business. Learn how to automate notebook execution, build client-ready reports, and transform your analytics expertise into a scalable subscription
Many data analysts build powerful Jupyter Notebook reports for internal projects, but few realize these notebooks can become profitable reporting services. Learn how to transform one-off analyses into scalable products, automate report generation, and build a recurring revenue business around data