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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Resume Building for Data Roles: A Complete Guide
A data resume has to pass software filters and a hurried human reader. US Department of Labor data shows which tools are classed as in demand for analyst, scientist and data warehousing roles, and which verbs describe the work. This guide turns that into a structure, bullet formula and checklist.
Data Scientist Salary in Hyderabad 2026: How Pay Grows From Fresher to Senior
Salary pages for data scientists in Hyderabad usually show neat ranges with no source. This guide uses real 2025 survey data instead: how pay in India grows from the first years to senior level, how much the stages overlap, and what the data can and cannot tell you about a Hyderabad offer.
BigQuery Explained: Google's Serverless Data Warehouse
What “serverless” means in BigQuery, why its bill depends on the bytes a query reads, and the three habits that keep that number small: select only needed columns, partition and cluster tables, and check a query’s cost before running it.
Data Science vs Data Analytics: Which Career Is Right for You?
A career-choice guide rather than a feature table: what a working week looks like for a data analyst and a data scientist, a self-check to see which kind of work suits you, the skills to build first for each, how people move between the two, and why the choice is less permanent than it feels.
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.