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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Regression vs Classification: When to Use Each
Regression or classification? The difference is simple, but choosing the framing is not always obvious. This guide shows how the target and the decision should decide it, then tests both framings on wine quality scores that can be modelled either way, including a yes-or-no question where the framing clearly mattered.
Data Science Companies in Hyderabad: Employers With Official Tech Centres (2026)
Lists of 'top data science companies hiring in Hyderabad' rarely say how they know. This one includes only employers whose own announcements or official pages confirm a Hyderabad technology, data or AI centre, each with its date. Then it shows how to check who is actually hiring this month, which no list can tell you.
Computer Vision Basics: From Pixels to Predictions
Computer vision from the ground up: what an image is to a computer, how a convolution filter finds edges, a small convolutional network trained on handwritten digits against a pixel baseline, the main vision tasks from classification to segmentation, and why transfer learning is the default.
How to Read a Machine Learning Research Paper
How to read a machine learning research paper without drowning in it: the three-pass method applied to the Transformer paper, the questions to ask of every results table, the difference between what a paper shows and what it suspects, and a small NumPy re-implementation that checks one of its claims.
Statistics for Data Science: The 20% You Actually Need
The statistics data scientists use constantly, each idea shown on real data in Python: when the median beats the mean, confidence intervals by bootstrapping, what a p-value does and does not say, why effect sizes matter, how testing many things manufactures findings, and how Simpson's paradox reverses a conclusion.
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.