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.
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Exploratory Data Analysis (EDA): A Step-by-Step Walkthrough
Exploratory data analysis done step by step on the Titanic passenger list in pandas, with every number from real runs: what a row means, missing values that predict the outcome, a fare column that is really a group price, relationships a correlation misses, and two columns that would leak the answer into any model.
From Data Analyst to Data Scientist: A Transition Roadmap
A transition roadmap for working data analysts who want to become data scientists: what you already have that newcomers lack, the five gaps to close, a phased plan where each stage ends in a piece of work from your current job, how to reposition your CV, and the traps that slow most people down.
Gradient Descent Explained: The Engine Behind ML
Gradient descent explained by building it: twenty lines of NumPy fit a regression line and land on the exact least-squares answer, then measurements show what a learning rate that is too small or too large does, why unscaled features need fifty times more steps, and how mini-batches trade noise for speed.
Handling Imbalanced Datasets: Techniques That Work
Imbalanced dataset techniques compared on real screening data where only 2.3% of cases are positive: what class weights, oversampling, SMOTE and undersampling really change, why moving the threshold did better, how a stronger model beat every resampling trick, and how resampling before the split fakes a great score.
Model Deployment: From Notebook to Production
The steps between a model that works in a notebook and one people can rely on: choosing batch or online serving, turning the notebook into a reproducible pipeline, a concrete case of training-serving skew and how a saved pipeline prevents it, a small validated prediction API, and what to monitor after launch.
Natural Language Processing (NLP) Roadmap for 2026
A natural language processing roadmap for 2026: handling raw text, building a classical baseline that is harder to beat than it looks, embeddings and semantic search, transformers and fine-tuning, working responsibly with large language models, and how to evaluate each, with a real baseline and a leakage trap measured.