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 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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Wrangling Data Flows vs Mapping Data Flows in ADF
Azure Data Factory has two visual ways to transform data: mapping data flows, built as a graph, and wrangling with Power Query. Both run on Spark. This guide compares authoring, connectors, supported functions and limits, shows what each is good at, and explains how the choice carries into Microsoft Fabric.
GPU vs CPU for ML: When You Actually Need a GPU
GPUs win on large, parallel maths and lose on small, scattered work. Measured on a laptop's integrated GPU and its CPU, the GPU ran at half the CPU's speed on tiny matrices, up to nine times faster on large ones, and nearly four times faster embedding sentences in batches, but slower one sentence at a time.
How to Build an MLOps Portfolio That Stands Out
An MLOps portfolio is judged on evidence that a system works and keeps working, not on how many tools it names. This guide covers what to build, what reviewers look for, how to structure the repository and README, how to use GitHub's profile features, and a rubric for checking your own project.
Data Visualization Best Practices and Common Mistakes
Data visualisation best practices built around the mistakes they prevent, with the distortion measured rather than asserted: a truncated axis that exaggerates a 4% change 25 times, circles that triple a difference, percent versus percentage points, choosing the chart for the relationship, dual axes, colour and clutter.
Descriptive vs Diagnostic vs Predictive Analytics
Descriptive, diagnostic and predictive analytics compared in practice: one real bike-sharing dataset taken through all three, from what happened, to why the worst day happened, to forecasting the next six months and beating a simple baseline, with the limits of each type and where prescriptive analytics takes over.
Excel for Data Analysis: Formulas That Matter
The Excel formulas that do most of the work in data analysis, shown as patterns on a sales table: looking up values with XLOOKUP, conditional totals with SUMIFS and COUNTIFS, cleaning text properly, handling errors without hiding problems, dynamic arrays with UNIQUE and FILTER, and readable formulas with LET.