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 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.
Python Projects for Beginners: 15 Ideas to Build
Fifteen beginner Python projects ordered as a ladder, where each one adds exactly one new idea, from a bill splitter to a habit tracker with tests. Every project has a 'done when' check and a stretch goal, and the starter code shown was run.
Power BI Projects to Build for Your Portfolio
Five Power BI portfolio projects, each proving a different skill (a star schema with time intelligence, performance on millions of rows, Power Query reshaping, row-level security and a report for a real user), with datasets you are allowed to publish and what a reviewer will check.
Portfolio Building: Projects That Impress Recruiters
Recruiters and hiring managers spend minutes, not hours, on a portfolio, so a project has to show its value fast. This guide gives the rubric a reviewer applies to any tech or data project, a README structure based on GitHub's guidance, the licence checks most portfolios skip, and how to reuse each project.
Top Generative AI Projects to Build for Your Portfolio
Most generative AI portfolios are chatbot demos that look alike. This guide proposes eight projects, from a structured extractor to an agent over a database, each built on a public dataset with a clear licence and each with a measurement to report, so the portfolio shows engineering rather than enthusiasm.
GCP Data Engineering Projects for Your Portfolio
A portfolio project is evidence, and evidence means numbers, failure handling and a README someone can follow. This guide sets out six Google Cloud data engineering projects in rising difficulty, says what each proves and costs, and profiles a real public dataset to show what the write-up should contain.