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 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.
Business Analytics Explained: A Beginner's Guide
Business analytics explained for beginners: what it is for, the four types of analytics and the business question each answers, the process that turns a question into a decision, the roles and tools involved, and a worked example on a real online shop's sales that goes from what happened to why it happened.
Self-Service Analytics: Empowering Business Teams
How to make self-service analytics work: the three ways organisations divide ownership of data and reports, why managed self-service is the usual goal, the foundations it needs, from certified data to shared metric definitions, warning signs of report sprawl, how to measure success, and how the analyst's role changes.
The Modern Data Analytics Stack Explained
The modern data analytics stack explained one layer at a time: sources, ingestion and ELT, cloud warehouses and lakehouses, transformation with dbt and its data tests, the semantic layer, BI, orchestration and governance, then one revenue number followed through every layer, and what a small team actually needs.
How to Handle Missing Data in Analytics
Handling missing data, measured on 48,842 real census records: finding the pattern behind the gaps, why these gaps were not random, how dropping incomplete rows shifted a headline rate, how filling with the most common value inflated a category by half, and when to keep, drop, fill or flag.
Azure Synapse Analytics Explained for Beginners
Azure Synapse Analytics puts a data warehouse, a query engine over the data lake, Spark and pipelines in one workspace. This guide explains each part in plain terms, shows with real data why the distribution of a table matters in a dedicated SQL pool, and sets out what Microsoft now says about Fabric.