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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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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.
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.
Outlier Detection for Analysts: Methods and Tools
Outlier detection for analysts, with four standard methods run on the same 19,773 real orders: why they flag anywhere from 53 to 1,797 orders, which suits skewed business data, how a log scale reveals suspiciously small orders, what the biggest outliers turned out to be, and when to keep, fix, cap or exclude.
BigQuery Best Practices for Analysts
An analyst can go wrong in BigQuery in three ways: a wrong number, a wasted scan, or a result nobody can reproduce. This guide gives a routine for each, with four correctness checks on 2.9 million real trips: NULL counts, filters that lose rows, days that shift with the time zone, and a join that adds rows.
Correlation vs Causation: A Must-Know for Analysts
Correlation versus causation for analysts, shown on real data rather than silly examples: how time of day turns humidity into an apparent driver of bike rentals, why unrelated trends correlate so often, the other ways correlations mislead, and the practical steps analysts use to move from an association to a cause.
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.