Python foundations
Someone who has never written a line of code and needs the language itself before any of the data work makes sense.
15 modules from the Python for Data Analysis curriculum.
What you'll learn
By the end of this course
- Explain what data analysis is and describe the five stages of the data analysis workflow
- Justify why Python is the dominant language for data work, and state when a spreadsheet or SQL is the better tool
- Identify the core libraries of the Python data stack (NumPy, pandas, Matplotlib, Seaborn, SciPy, scikit-learn) and what each one is responsible for
- Write and execute your first Python statements: comments, print(), variables, and arithmetic
- Recognise Python's four fundamental data types (int, float, str, bool) and inspect them with type()
- Load a real tabular dataset into a pandas DataFrame and inspect it with .head(), .info(), and .describe()
- Produce a first grouped summary and two publication-quality charts
- Apply professional coding conventions (PEP 8 naming, comments, reproducible cells) from your very first line of code
Curriculum
15 sections · 15 lessons
Section 1
15 modules
Section 1
- Introduction to Python for Data AnalysisData analysis is the process of turning raw records into decisions. That sentence is doing a lot of work, so let us unpack it with a concrete scene.~120 min
- Installing Python & JupyterLabA clean, isolated, reproducible data-science environment you control.~90 min
- Using JupyterLab EffectivelyYou stop using Jupyter and start driving it.~90 min
- Variables & Data TypesA correct mental model of how Python stores values — the foundation of every bug you will not write later.~110 min
- OperatorsFluent, unambiguous expressions — and the & vs and rule that governs every pandas filter you will ever write.~95 min
- StringsThe text-handling fluency that makes real-world data cleaning possible.~120 min
- ListsCommand of Python's most-used container — and the aliasing traps that Notebook 04's memory model predicts.~120 min
- TuplesUnderstanding why immutability is a feature — and where tuples are not merely allowed but required.~90 min
- DictionariesFluency with Python's most powerful container — and the structure that every API, config file, and JSON document on earth is made of.~120 min
- SetsInstant deduplication, O(1) membership, and the reconciliation toolkit for comparing two systems.~90 min
- FunctionsThe ability to write code that stays readable, testable, and reusable as it grows past 50 lines.~140 min
- Conditional StatementsDecision logic that stays readable when the business rules get complicated — and the vectorised equivalents pandas needs.~110 min
- LoopsRepetition done right — and a clear-eyed understanding of when a loop is the wrong tool entirely.~120 min
- File HandlingReliable, portable, encoding-correct data input and output — the step that begins every real analysis.~120 min
- Error HandlingPipelines that survive bad input, fail loudly when they should, and tell you exactly what went wrong — the last piece of Section 1.~120 min
About this course
15 modules from the Python for Data Analysis curriculum.
This course covers Introduction to Python for Data Analysis, Installing Python & JupyterLab, Using JupyterLab Effectively, Variables & Data Types, Operators and Strings, and 9 more.
About 27h 35m of material across 15 modules, each with the dataset it teaches from and exercises with worked solutions.
Every module is a notebook you download and run yourself in JupyterLab — the point is that you execute the code, not that you watch someone else execute it. Recordings are added module by module; a module without one yet is taught in full by its notebook.
Who it's for
- Researchers and postgraduate students who need the analysis done properly, not just done
- Analysts moving from spreadsheets to something repeatable and auditable
- Anyone who has followed a Python tutorial and still cannot open their own data and get an answer
What you'll need
- A computer you can install Python and JupyterLab on — notebook 02 walks you through it, on Windows and on macOS
- No prior programming experience for the foundations; the later tracks assume the earlier ones
- Your own dataset is welcome but not required — every module ships with the data it teaches from
Start Python foundations today
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