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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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Metrics That Matter: Vanity vs Actionable Metrics
Vanity metrics versus actionable metrics explained with a real example: a customer total that rose every single month while new customer acquisition fell by more than half, what makes a metric actionable, how the same number can be vanity for one business and vital for another, and a five-question test for any metric.
Funnel Analysis: Measuring Conversion Step by Step
Funnel analysis step by step on 12,330 real shopping sessions: defining the steps, calculating step-by-step and overall conversion, finding where most visitors drop out, catching a stage definition that silently lost a fifth of purchases, understanding open and closed funnels, and comparing new and returning visitors.
How to Handle Missing Data in Analytics
Handling missing data, measured on 48,842 real census records: finding the pattern behind the gaps, why these gaps were not random, how dropping incomplete rows shifted a headline rate, how filling with the most common value inflated a category by half, and when to keep, drop, fill or flag.
Customer Segmentation for Analysts: A Practical Guide
Customer segmentation for analysts, worked through on a real online shop's 4,293 customers: computing recency, frequency and monetary value, scoring them into quintiles, turning scores into named segments, finding that a quarter of customers bring two thirds of revenue, and deciding what to do with each group.
SQL for Data Analysts: The Queries You Need Daily
The SQL an analyst writes every day, each query run on a real sample database: profiling a table before trusting it, reconciling totals, monthly reports that keep empty months, share of total with a window, period-over-period change with LAG, new versus returning customers and the latest record per customer.
Spreadsheet to Insight: A Repeatable Analysis Workflow
A repeatable workflow for turning a raw spreadsheet into an insight, followed end to end on a real 541,909-row sales workbook: writing the question down, fingerprinting the raw file, logging what every cleaning rule removes, answering the question, reconciling before sharing, and making the whole analysis rerunnable.