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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Data Cleaning: A Practical Step-by-Step Guide
A step-by-step data cleaning guide on 541,909 real transaction lines: profiling before touching anything, removing duplicates, standardising messy text, fixing inconsistent categories, investigating extreme values, keeping a cleaning log, and validation checks that caught a problem the cleaning itself created.
Data Warehouse Explained: Concepts and Architecture
A clear explanation of data warehouses: why analytical queries need a separate store from transactional systems, the architecture from sources through staging, modelled layers and a semantic model, facts, dimensions, grain and history, modelling approaches, modern platforms and common pitfalls.
Linked Services in Azure Data Factory Explained
A linked service is how Azure Data Factory connects to a data store or a compute service. This guide covers the two kinds, the anatomy of the JSON, authentication from keys to managed identities, keeping secrets in Key Vault, parameterising one linked service for many databases, and handling development and production.
Data Science Projects for Freshers: 12 Ideas with Datasets
Twelve data science project ideas for freshers, each paired with a real public dataset and framed as a question worth answering: campaign response, customer segments, demand, fairness, text classification, big data and more, with the skills each proves and a stretch goal that makes it stand out.
Data Science Interview Questions and Answers 2026
A data science interview is several interviews in one: statistics, machine learning, coding, product sense and a take-home. This guide maps each round with model answers, then works a churn take-home on real telecom data, including the checks that separate a strong submission from a high score.
Dashboard Design Principles for Data Analysts
Dashboard design principles for data analysts: designing for one audience and a few questions, fitting on one screen, giving every number context, choosing charts that encode values accurately, arranging content in reading order, and using colour sparingly and accessibly, with a script that checks palette contrast.