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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From Power BI Beginner to Developer: A Career Roadmap
Learning Power BI is one roadmap; building a career on it is another. This guide follows the roles, from report author to BI developer and analytics engineer, with what each one owns, what employers' official descriptions say the work involves, and where Microsoft's PL-300 and DP-600 certifications fit along the way.
From Backend Developer to Data Engineer: A Roadmap
A transition guide for backend developers moving into data engineering: the skills that carry over unchanged, a concept map from requests and services to batches and pipelines, the habits that work against you in analytical systems, a four-phase learning plan, and a first project that shows the switch.
Git Interview Questions Every Developer Should Know
Git questions in interviews check whether you can undo mistakes safely, not whether you know every command. Six common questions are answered by running Git on a throwaway repository: merge versus rebase, the three reset modes, revert, recovering a lost commit, why .gitignore ignored nothing, and bisect.
Top Generative AI Tools Every Developer Should Know in 2026
There are more generative AI tools than anyone can learn. This guide groups the ones developers actually use by the job they do, from calling models to evaluation, lists their licences and latest releases as checked on 3 October 2026, and suggests a small starting toolkit and how to judge new tools.
Python String Methods Every Developer Should Know
The string methods behind most real text work in Python, grouped by job (cleaning, splitting and joining, searching, comparing, checking) with the traps: strip removing characters rather than a suffix, split() versus split(' '), casefold, and why isdigit accepts characters int() rejects.
Top Python Libraries Every Developer Should Know
A map of the Python libraries that come up again and again in real projects, grouped by job: standard-library modules to learn first, then HTTP clients, web frameworks, data and machine learning, validation, databases, testing, command-line tools and code-quality tooling, and how to choose.