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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GCP Data Engineering Projects for Your Portfolio
A portfolio project is evidence, and evidence means numbers, failure handling and a README someone can follow. This guide sets out six Google Cloud data engineering projects in rising difficulty, says what each proves and costs, and profiles a real public dataset to show what the write-up should contain.
Data Modeling in Redshift: A Practical Guide
How to model data in Amazon Redshift: a star schema written as Redshift DDL, why primary and foreign keys are declared but not enforced, how to choose column types, where distribution and sort keys fit, what to do with semi-structured data, and when a materialized view or a wide table is the better answer.
ADF Data Flow Transformations: A Complete Reference
A reference to the transformations available in Azure Data Factory mapping data flows, grouped by task: shaping columns, filtering and marking rows, combining streams and handling nested data. Each has its SQL equivalent and the documented detail worth knowing, with row counts from a real example.
Python Coding Challenges to Practice for Interviews
Interview coding challenges reward recognising a pattern: a dictionary instead of a nested loop, a sliding window instead of restarting, a running total instead of re-summing. Three classic challenges are solved both ways, checked on 600 random inputs and measured in steps, with one surprising result.
System Design Interview Questions for Data Engineers
Data engineering system design interviews reward numbers more than diagrams. This guide gives a framework for the round and works its hardest parts on a real day of New York taxi trips: why sizing for the average builds a six-hour backlog, what Little's law says about delay, and how a natural key overloads partitions.
Data Science Career Guide: Roles, Skills and Growth Path
A map of data science careers: what data analysts, data scientists, machine learning engineers, data engineers and analytics engineers actually do, the skills that separate them, realistic routes into a first role, and how people grow from junior work to senior, lead and specialist positions.