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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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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.
Cohort Analysis Explained with Examples
Cohort analysis explained with a worked example on real online shop transactions: grouping customers by the month they first bought, building a retention table in pandas, reading it along rows, columns and diagonals, and two traps it exposes: a cohort that only looks loyal and a spike that is really the calendar.
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 Write a Data Analysis Report That Gets Read
How to write a data analysis report that busy people read and act on: putting the answer and recommendation first, a summary that works on its own, findings written as evidence for that answer, method and limitations in their proper place, clear numbers and charts, a reusable template, and a checklist before sending.
Exploratory Data Analysis (EDA): A Step-by-Step Walkthrough
Exploratory data analysis done step by step on the Titanic passenger list in pandas, with every number from real runs: what a row means, missing values that predict the outcome, a fare column that is really a group price, relationships a correlation misses, and two columns that would leak the answer into any model.
Pandas vs SQL for Data Analysis: When to Use Which
Pandas versus SQL for data analysis, compared on the same real dataset: identical answers from a GROUP BY and a groupby, a moving average as a window function and as rolling, a pivot that is one line in pandas and many in SQL, and why filtering in the database first matters when data is large.