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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Excel Power Query: Cleaning Data Without Code
How to clean data in Excel with Power Query instead of formulas and manual edits: combining a folder of monthly exports, fixing data types and messy text, removing duplicates without the case-sensitivity trap, merging lookup tables, unpivoting wide layouts, and turning it all into a query you refresh each month.
Google Sheets for Data Analysis: Underrated Power Tools
Google Sheets features that turn it into a serious analysis tool: the QUERY function for SQL-like summaries inside a spreadsheet, ARRAYFORMULA for whole-column calculations, FILTER, REGEXEXTRACT for messy text, IMPORTRANGE for combining files, pivot tables, and where Sheets stops being the right tool.
How Data Analysts Add Real Business Value
How data analysts add real business value beyond producing reports: framing the decision behind a request, sizing opportunities with explicit assumptions, measuring whether changes worked, finding leaks and risks, and saving people time, with a worked sizing example on real retail data and tips on showing your impact.
Pivot Tables in Excel: A Complete Tutorial
A complete Excel pivot table tutorial: preparing data so pivots work, building a first summary, choosing between sum, count and average, showing values as percentages and running totals, grouping dates into months and quarters, filtering with slicers, refreshing correctly, and the mistakes that produce wrong totals.
Product Analytics 101: Events, Funnels and Retention
Product analytics built on real data: how event data is structured, how to write a tracking plan, measuring return rates and time to a second order, finding early behaviour linked to retention, and how funnels, cohorts, segmentation and experiments fit together, plus the pitfalls that corrupt event data.
Time Intelligence in Analytics: Trends and Seasonality
Time intelligence for analysts, worked through two years of real daily bike-share data: why a 23% monthly fall was good news, smoothing daily noise with moving averages, year-to-date comparisons, seasonal indexes, weekday effects, and the partial-period trap that turned a 4.5% rise into a 48% fall.