Tutorial topic · Data Analysis
Spreadsheets, SQL and clean data — how analysts turn messy numbers into answers a business can act on.
Data analysis is turning raw, messy data into clear answers: cleaning it, asking precise questions of it, and presenting what you find. Beginners should start with spreadsheet formulas and pivot tables, then SQL (joins and aggregations), then data cleaning and visualisation — in that order.
16-week roadmap · 2 free courses · 5-part tutorial series
Weeks 1–4
Project: Analyse a messy sales dataset end-to-end
Weeks 5–8
Project: Ten business questions answered in SQL
Weeks 9–12
Project: A complete analysis notebook
Weeks 13–16
Project: An interactive dashboard with a written report
A step-by-step series — follow it in order. Each tutorial builds on the one before it, with worked examples, code you can run and a short practice task.
Excel Formulas Every Data Analyst Needs First
Pivot Tables, step by step on a messy sales sheet
SQL Joins Explained with Examples: INNER, LEFT, RIGHT
SQL GROUP BY and Aggregations: answering questions with queries
Data Cleaning: a checklist for messy datasets
AI & Data
The seven skills every data analyst needs, in learning order: spreadsheets, SQL, cleaning, statistics, visualisation, a programming language, and reporting.
17 October 2026 · 7 min read
AI & Data
FreeTurn messy spreadsheet data into clean tables, summaries, and charts using Excel and Google Sheets.
Beginner · 13 lessons · 7 hours · English + Urdu
AI & Data
FreeAsk questions of real databases with SQL, then turn the answers into clear dashboards and charts.
Intermediate · 12 lessons · 9 hours · English
Turn raw data into decisions with spreadsheets, SQL, Python and dashboards. Free, step by step, at your pace.
They need some. Spreadsheets and SQL cover much of daily analyst work, and Python makes you faster and opens better roles. This roadmap introduces them in that order, so nothing feels like a jump.
Analysts focus on understanding what happened and what a business should do next, using data from existing systems. Data scientists build predictive models and need deeper statistics and ML. Analyst is the common entry point into data careers.
Spreadsheets first — they teach you how data is structured and cleaned, and every company uses them. SQL comes right after, in weeks 5–8, once data thinking is in place.
Four finished pieces: a spreadsheet analysis, a set of answered SQL business questions, a Python analysis notebook, and an interactive dashboard with a short report — exactly what hiring managers ask to see.
