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.
Featured Guides
Data Science Roadmap 2026: A Complete Step-by-Step Guide
A data science roadmap in dependency order, from Python and SQL through cleaning, statistics and honest model evaluation to communicating results, with an exit test at every stage and one dataset that carries through the whole path.
What Is Generative AI? A Complete Beginner's Guide
What generative AI is, in plain language: how language models produce text one token at a time (shown with a tiny runnable model), how chat models are trained, how image models differ, what the technology is reliable for, and why it confabulates.
Learn Python from Scratch: A Complete 2026 Roadmap
How to learn Python from zero: install a current version (including the new Windows install manager), then work through seven stages, from values and loops to files, virtual environments, classes and tests, each ending with a small program you should be able to write on your own.
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AWS Glue Studio: Visual ETL for Beginners
AWS Glue Studio lets you build a Spark ETL job by drawing it: source, transform and target nodes on a canvas, with Glue generating the script. This beginner's guide explains the editor, the built-in transforms, data previews and what they cost, the generated script, and when a visual job should become code.
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%.
Probability for Data Science: Concepts You Must Know
Six probability concepts data scientists use, each computed and checked by simulation in Python: why a positive result from a good test can mean only a 9% chance, expected value for decisions, binomial and Poisson counts, why averages become normal, and whether a model's probabilities can be trusted.
Building Incremental Data Loads That Scale
Designing incremental data loads that stay correct: the main patterns, a month of hourly loads simulated on real taxi data in which a watermark on event time silently missed 22.5% of trips while a watermark on availability time missed none, lookback windows, storing watermarks safely, deletes and backfills.
Disaster Recovery and Backups on GCP
Most data loss is not a disaster. It is a person running the wrong statement. This guide separates mistakes from outages, explains RPO and RTO, covers what BigQuery, Cloud Storage and Cloud SQL keep and for how long, and rehearses a backup and restore on a real database to show what a restore loses.
Blue-Green Deployments for ML Services
Blue-green deployment keeps the old model serving while the new one waits beside it, so switching either way is quick. This guide runs it with two real spam filters, shows a pre-switch check catching a score-scale change that would have made a review queue five times longer, and switches under live traffic.