Data Analysis with Python
Data analysis with Python means using libraries like Pandas, NumPy and Matplotlib to load, clean, explore and visualize data — turning raw numbers into insights you can act on. This free learning path guides you through all three, in the right order, from your first array to your first chart.
Free Data Analysis with Python path: learn to load, clean, explore and visualize data with NumPy, Pandas and Matplotlib — beginner-friendly, interactive, no…
Part of the free Data Analysis with Python course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.
Your learning path
Three courses that build on each other. Follow them in order for the smoothest route from beginner to confident data analyst.
Why learn data analysis?
What you'll be able to do
Ready to analyze data?
Begin with NumPy — the foundation everything else is built on — then move on to Pandas and Matplotlib. New terms along the way? Look them up in the Coding Dictionary .
NumPy
Start here. NumPy gives you fast, memory-efficient arrays and the math that every other data tool is built on.
Pandas
Load, clean, filter and reshape real-world tables with DataFrames — the everyday workhorse of data analysis.
Matplotlib
Turn numbers into clear line charts, bar charts and scatter plots so you can spot and share what the data shows.
In-demand & high-paying
Data analysis skills are among the most sought-after — and best-paid — in tech, with roles across every industry.
Used everywhere
From business reporting and scientific research to the foundations of AI and machine learning.
Beginner-friendly with Python
Python's readable syntax and friendly libraries make data analysis approachable even if you're new to coding.
Completely free here
Every lesson in all three courses is free — no paywall, no sign-up required to learn.
Frequently asked questions
What is the difference between Pandas, NumPy and Matplotlib?
They stack. NumPy gives you fast numeric arrays and the maths on them. Pandas builds labelled tables — the DataFrame — on top of NumPy, so you can work with named columns and mixed types. Matplotlib draws the result. Most analysis uses all three, which is why this path teaches them in that order.
Do I need to know Python before starting data analysis?
You need the basics: variables, lists, dictionaries, loops, functions and how to import a module. You do not need classes, decorators or async. If those first six are shaky, do the Python course first — the libraries will make far more sense.
Do I need maths or statistics for this?
For loading, cleaning, grouping and charting data, no — that is programming, not maths. You need statistics when you start drawing conclusions: what a mean hides, why a median is often better, what a correlation does and does not prove. Start with the mechanics and add the statistics as the questions arise.
How long does it take to learn data analysis with Python?
Working through this path at a few hours a week, expect a couple of months to be genuinely comfortable loading a messy CSV, cleaning it, grouping it and charting the answer. The first useful result comes much sooner than that — usually in the first week.
Should I use Jupyter notebooks or a script?
Notebooks for exploring, scripts for anything you will run twice. Notebooks let you see each step's output as you go, which is ideal while you are still asking questions of the data. Once the analysis is settled, move it into a script or module so it runs the same way every time.