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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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.
Python Interview Questions: Top 50 with Answers
Fifty conceptual Python interview questions with short, precise answers, grouped by topic: basics, types and mutability, data structures, functions and scope, OOP, iterators and decorators, errors and files, and internals, with runnable examples for the ones people most often get wrong.
Python Interview Questions for Data Roles
The plain-Python coding tasks data-analyst and data-engineer interviews use, without pandas: counting, deduplicating, grouping, streaming large files, flattening JSON, joining, and the bugs interviewers plant. Each has a worked solution and the follow-up questions to expect.
Python for Data Science: The Essential Libraries
The Python data science stack explained by the job each library does: NumPy for arrays, pandas for tables, Matplotlib and seaborn for charts, SciPy and statsmodels for statistics, scikit-learn for models and Jupyter for exploration, with one runnable example that passes a real dataset through them.
Python Projects for Beginners: 15 Ideas to Build
Fifteen beginner Python projects ordered as a ladder, where each one adds exactly one new idea, from a bill splitter to a habit tracker with tests. Every project has a 'done when' check and a stretch goal, and the starter code shown was run.
NumPy Guide: Arrays, Broadcasting and Vectorization
What vectorisation actually means in NumPy, the one broadcasting rule behind every shape error, and the three bugs it causes in real data code — silent wrong-axis maths, exploding intermediate arrays, and edits that leak through views.