IT Career Guides & Free Resources
Expert roadmaps, tutorials, salary guides and interview prep across Data Science, Generative AI, Python, Cloud & Analytics — written by GloryTecks trainers in Hyderabad.
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Data Quality: Testing and Validation in Pipelines
How to test data quality in pipelines: the dimensions worth checking, nine concrete checks written as SQL that counts failing rows and run on three million real taxi trips, where one fails and one day's volume looks wrong, how to set thresholds and severity, where checks belong, and the tools that package them.
How to Transition from Testing to Data Engineering
Software testers already share most of the technologies listed for data warehousing work, and about two in five of its core tasks build directly on testing. Task and technology data from O*NET shows exactly what carries over, what is new, and a step-by-step route from QA into data engineering through data quality work.
Testing Machine Learning Code and Data
ML code can pass every unit test and still ship a broken model, because data and model behaviour need tests of their own. This guide writes nine pytest checks for a real model, covering schema, invariants, plausibility, a baseline gate and behaviour, and one catches a genuine sensor fault in the data.
A/B Testing ML Models in Production
A model that wins offline still has to prove itself with real customers. On real bank marketing outcomes, this guide shows why a one-point offline gain needed about 36,000 calls per arm to confirm, how to split traffic and check the split, and how checking results early turned 4% false alarms into 18%.
Unit Testing in Python with pytest: A Beginner's Guide
A beginner's guide to pytest built on a small real module: writing tests as plain functions with assert, reading pytest's failure reports, parametrising edge cases, testing that errors are raised, and using fixtures, tmp_path and monkeypatch so tests never touch real files or settings.
A/B Testing for Data Scientists: A Practical Guide
A/B testing from design to decision: one primary metric, calculating the sample size before launch, analysing conversion with a two-proportion test and a confidence interval, checking for sample ratio mismatch, and avoiding the traps (peeking, many metrics, novelty) that produce confident wrong answers.