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.
All articles
Excel vs Power BI: Which Should You Learn First?
Should you learn Excel or Power BI first? For most beginners, a slice of Excel first, then Power Query and the data model, which Excel and Power BI share, and then Power BI. Here is why, what each tool teaches on its own, and which to start with in your situation.
Power BI vs Excel: When to Use Each Tool
A task-by-task guide to choosing between Power BI and Excel at work: the signs a workbook has outgrown Excel, what Excel still does better, the worksheet row limit tested on a real two-sheet retail dataset, and the ways the two tools work together, from Power Query to Analyze in Excel and connected tables.
From Excel to SQL: A Smooth Transition Guide
A transition guide from Excel to SQL that starts from what you already know: filters become WHERE, SUMIFS becomes GROUP BY, XLOOKUP becomes a join, COUNTIFS and IF become CASE, and pivot tables become conditional aggregation, each shown as a real query with its output, plus the mindset shifts and pitfalls to expect.
Forecasting in Excel: Simple Methods That Work
Forecasting in Excel with simple methods that work: the formula for each, from repeating last month to a moving average, FORECAST.LINEAR, a trend times seasonal index and FORECAST.ETS, plus an honest test on a real monthly series where the right simple method cut the error from over 40% to 12%.
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.
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.