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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All articles
ADF Global Parameters and Variables Explained
Azure Data Factory has three ways to hold a value: pipeline parameters, variables and global parameters. This guide compares their scope and whether they can change, shows Set Variable and Append Variable in JSON, explains why a variable cannot reference itself and why variables misbehave in parallel loops.
How to Become a Data Scientist in 2026 with No Experience
People do start as data scientists with no prior job, but rarely with nothing: in a 2025 survey, data scientists and AI/ML engineers in their first two years had typically been coding for six years, and nearly half were still learning at university. This guide shows what newcomers bring and how to build it.
Power BI vs Tableau: A Detailed 2026 Comparison
A detailed comparison of Power BI and Tableau for teams and analysts: a concept-by-concept translation table, authoring and platforms, data preparation, import versus extract, DAX measures versus LOD expressions and how filters treat them, row-level security, sharing, licence types and a worked cost example.
MLOps Maturity Levels: Where Does Your Team Stand?
MLOps maturity describes how much of the path from experiment to production, and back again, a team has automated. This guide compares the two most cited models, Google's three levels and Microsoft's five, turns them into a self-assessment, and suggests the single next step that pays off most at each level.
Data Governance Basics Every Analyst Should Know
Data governance explained from an analyst's desk: who owns data and definitions, the six dimensions of data quality, handling personal data responsibly with reference to India's Digital Personal Data Protection Act, documenting lineage, certified datasets in BI tools, and a checklist for before you publish a report.
Data Versioning with DVC: A Hands-On Guide
Git versions code but chokes on data. DVC fills the gap by keeping a small pointer to each dataset version in Git and the data itself in its own store. This hands-on guide versions a real dataset twice, rolls back to the first version, and builds a pipeline that reruns training only when something changes.