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.
All articles
SQL for Data Science: Queries Every Data Scientist Must Know
The SQL queries data scientists write every week, each run on a real sample database with its output: joining and aggregating, the join fan-out that inflated revenue ninefold, finding what is missing with an anti-join, CTEs and window functions for running totals and top-N per group, and counting around NULLs.
Data Science vs Machine Learning vs AI: What's the Difference?
Artificial intelligence, machine learning, deep learning, generative AI and data science are not five competing fields but layers and overlaps. What each term means, how they nest, which everyday tasks belong where, the roles attached to each, and how to use the words precisely in a job description or an interview.
How to Build a Data Science Portfolio That Gets Interviews
How to build a data science portfolio designed for the few minutes a reviewer spends on it: which three projects to show, a README template that puts the result first, repository habits that signal professional work, when a live demo is worth it, and the red flags that get portfolios skipped.
Data Engineering vs Data Science: Roles Compared
Data engineering and data science compared through the work each role does: what they are responsible for according to published role definitions, a typical week in each, the skills and tools that differ and overlap, how the two depend on each other on one project, hybrid roles in between, and how to choose.