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.
Trending This Week
Data Scientist Salary in Hyderabad 2026: How Pay Grows From Fresher to Senior
Data Science · 7 min
ChatGPT Complete Guide: Features, Prompts and Use Cases
Generative AI · 5 min
Python Interview Questions: Top 50 with Answers
Python · 10 min
DAX Guide: Measures, Calculated Columns and Context
Power BI · 6 min
All articles
Python File Handling: Reading and Writing Files
Everything you need to read and write files reliably in Python: what each open() mode does, why you should always pass an encoding, reading big files line by line, pathlib for paths and folders, writing without destroying the old file if something fails, and handling the errors files actually raise.
A/B Testing for Data Scientists: A Practical Guide
A/B testing from design to decision: one primary metric, calculating the sample size before launch, analysing conversion with a two-proportion test and a confidence interval, checking for sample ratio mismatch, and avoiding the traps (peeking, many metrics, novelty) that produce confident wrong answers.
Anomaly Detection: Techniques and Real-World Use Cases
How anomaly detection works in practice: rules and thresholds, z-scores and the masking problem that lets outliers hide themselves, robust median-based scores, Isolation Forest, anomalies in time series, and how to evaluate a detector when almost nothing is labelled, with fraud, sensor and data-quality use cases.
Clustering Algorithms: K-Means, DBSCAN and Hierarchical
K-means, DBSCAN and hierarchical clustering compared on two datasets where their assumptions succeed and fail: round clusters versus interlocking shapes. How each algorithm works, choosing the number of clusters with the silhouette score, why feature scaling changes everything, and how to judge clusters without labels.
Data Flow Debug and Performance in ADF
Debug mode lets you watch a data flow work on live data, and it bills like a running cluster. This guide covers what debug does and does not check, the four places a data flow loses time, cluster size and time to live, partitioning and skew measured on real data, and the pipeline designs that waste clusters.
From Data Scientist to MLOps Engineer: A Roadmap
Data scientists already know models, data and evaluation, which is most of what MLOps engineers wish their colleagues knew. The gap is software and operations. This plan sets out what transfers, what to learn, a twelve-week sequence built around one evolving project, and how to present the move to employers.