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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FastAPI Guide: Build a Production API Step by Step
A step-by-step FastAPI build: request validation with Pydantic models, response models that control what leaves the API, dependency injection, proper status codes and errors, tests with TestClient and dependency overrides, then the settings, workers and health checks a production deployment needs.
How to Structure a Python Project the Right Way
A Python project layout that works from the first commit: why the src layout catches packaging mistakes, a complete pyproject.toml with dependencies and a command, where tests and configuration go, running the package with python -m, and the files that should never be committed.
Lambda Functions, map, filter and reduce in Python
Lambda, map, filter and reduce explained by what they are good for: key functions for sorting and max, map and filter as lazy one-shot iterators, why reduce is usually replaced by a built-in, the operator module, and the loop-variable trap that makes every lambda return the same value.
Multithreading vs Multiprocessing in Python
Threads or processes? A timed experiment with concurrent.futures shows the rule the global interpreter lock creates: threads help while programs wait, processes help while they compute. Then the costs of each, shared state and locks, the Windows and macOS start-up rule, and a decision table that includes asyncio.
Handling Errors and Retries in ADF Pipelines
Error handling in Azure Data Factory has a trap: a pipeline can catch a failure, send an alert and still be reported as succeeded. This guide covers retry and timeout settings, the four conditional paths, the rule that decides a pipeline's status, modelled across four patterns, and the Fail activity.
Pandas Performance: Making Your Code 10x Faster
Where pandas code loses its time and the fixes that recover it, each timed on 200,000 rows: replacing iterrows and row-wise apply with column arithmetic, np.select for if-else logic, built-in groupby aggregations instead of lambdas, category columns for memory, and loading only what you need.