Learning path · Beginner

Data Analyst

Turn raw tables into decisions with Python, Pandas and SQL.

A practical path from SQL queries and Pandas cleaning to a reproducible analysis with charts and honest limitations.

6 weeks 3 courses 42 lessons 3 certificates

Start this path Browse tutorials

The sequence

3 courses, in the right order.

Each step assumes only what came before it. Follow the sequence or jump to a course you need.

Step 1 · Beginner

SQL & Databases

Open course

Learn to query, filter, join, summarise and reshape relational data with NULL handling, CASE, CTEs, window functions, indexes and safe writes. Every lesson runs against a realistic learning-platform database.

Querying dataDatabase skills from the startRelating and summarisingReshaping resultsComposing queriesData in practiceProject

0/16 lessons0%

SQL Fundamentals

Step 2 · Beginner

Python Foundations

Open course

Learn programming fundamentals with Python — variables, control flow, functions, collections, classes, files, tests and packaging — then ship a small project you can extend.

First stepsPython from zeroStructure your codeShape the dataBuild real programsQuality and deliveryShip it

0/16 lessons0%

Python Foundations

Step 3 · Beginner

Pandas & Data Analysis

Open course

Pandas is the working language of data analysis in Python. Load, clean, filter, group, merge and visualise real tables, then finish with a reproducible analysis project.

Pandas from first Series to analysis project

0/10 lessons0%

Pandas & Data Analysis

Portfolio project

Public dataset analysis

Answer a real question from a public dataset and present the result.

about 6 hours Portfolio piece

The brief

Choose a public dataset, state a question, clean the data, analyse it and produce three charts plus a short written summary. Include the rows you dropped and why.

DeliverableA notebook or script, a data dictionary, three charts and a one-page summary.

Stretch goals

Add a statistical test with an explicit assumption check.
Publish an interactive chart.

Acceptance criteria

0/6

The question is stated before the analysis.
The raw data is untouched and the cleaning is reproducible.
Every chart has labelled axes and units.
The summary separates observation from interpretation.
Limitations and dropped rows are reported.
A second person can rerun the analysis from the repository.

Path complete

Finish with proof, not just a folder of files.

Complete every course in this path to collect 3 verifiable certificates. Each one has a unique public ID and a QR code that resolves to a live status record.

How certification works Verify a credential

Data Analyst42 lessons · 9 hours

SQL & DatabasesPython FoundationsPandas & Data Analysis

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