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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Apache Spark Explained for Beginners
A beginner's guide to Apache Spark: what it is for, how the driver, executors and cluster manager fit together, jobs, stages and tasks, lazy transformations and actions demonstrated on three million New York taxi trips, partitions, installing PySpark, and the mistakes beginners make.
Vertex AI Tutorial: ML on Google Cloud
Vertex AI is now called Gemini Enterprise Agent Platform, and its machine learning tools work as before. This tutorial follows one model from data to predictions, explains the contract your training code must follow, and trains a real model locally to show what testing on a later day reveals.
Mapping Data Flows in ADF: Transformations Without Code
Mapping data flows let you design transformations on a canvas in Azure Data Factory and run them on Spark clusters the service manages. This guide explains how they work, walks through one flow stage by stage with real row counts, and sets out when a data flow is the right tool.
Portfolio Building: Projects That Impress Recruiters
Recruiters and hiring managers spend minutes, not hours, on a portfolio, so a project has to show its value fast. This guide gives the rubric a reviewer applies to any tech or data project, a README structure based on GitHub's guidance, the licence checks most portfolios skip, and how to reuse each project.
Amazon Redshift Explained: The Cloud Data Warehouse
An introduction to Amazon Redshift, AWS's data warehouse: how a leader node, compute nodes and slices run a query in parallel, why data is stored in columns, how managed storage separates compute from storage, Serverless versus a provisioned cluster, how it differs from PostgreSQL, and what to learn first.
Feature Engineering: A Practical Guide with Examples
Feature engineering, measured step by step on 17,379 hours of real bike-rental data: how encoding the hour, adding an hour-by-working-day interaction and two domain features cut a linear model's error from 131 to 73 rentals, why the same features hurt a tree model, and the leakage rules every new feature must pass.