Resources for learning by doing.

Python for Data Science · basics

Study file: Python_for Data_Science_Cheat_Sheet.pdf

For

New Python learners and analysts moving from spreadsheets

Use it to

Keep variables, lists, functions and common operations beside your first exploratory notebook.

Connects to

Programming for Data Science

Python basics · expanded reference

Study file: Python_Basics_Cheat_Sheet-updated.pdf · Python Basics Cheat Sheet Updated.pdf

For

Beginners revising everyday Python

Use it to

Look up lists, strings, control flow and other foundations while practising small problems. The two supplied filenames contain identical copies; one download serves both.

Connects to

Programming for Data Science

The publisher now serves a newer edition, so its layout and examples may differ from my study copy.

Jupyter notebook workspace

Study file: Jupyterlab_Cheat_Sheet.pdf

For

Learners documenting analyses in notebooks

Use it to

Find cell, kernel, saving and widget controls while turning code and explanation into a reproducible narrative.

Connects to

Programming for Data Science

This supplied file is named JupyterLab, while DataCamp labels its source page Jupyter Notebook; follow the version shown in the PDF.

NumPy arrays

Study file: Numpy_Cheat_Sheet.pdf

For

Python learners preparing numerical data

Use it to

Check array creation, selection, shapes and calculations before reaching for statistical or machine learning methods.

Connects to

Programming · Statistics

Importing data in Python

Study file: Importing_Data_Python_Cheat_Sheet.pdf

For

Analysts working across files and databases

Use it to

Compare common ways to bring flat files, spreadsheets and database results into Python, then inspect the result.

Connects to

Data Engineering · Programming

Pandas basics

Study file: Pandas_Cheat_Sheet.pdf

For

Analysts beginning tabular work

Use it to

Quickly recall DataFrame selection, input/output, sorting and descriptive operations when exploring a table.

Connects to

Data Driven Analytics

Data wrangling with pandas

Study file: Data_Wrangling_Pandas_Cheat_Sheet.pdf

For

Analysts cleaning and joining imperfect tables

Use it to

Revisit reshaping, duplicate handling, dates and combining data; record each transformation and validate the output.

Connects to

Data Engineering · Analytics

Regular expressions

Study file: Regular_Expressions_Cheat_Sheet.pdf

For

People cleaning text fields and extracting patterns

Use it to

Look up matching and capture syntax, then test it against edge cases before using it in a pipeline.

Connects to

Programming · Data Engineering

The publisher download is a newer edition of this reference.

Matplotlib plotting

Study file: Matplotlib_Python_Cheat_Sheet.pdf

For

Analysts making reproducible charts

Use it to

Recall figure, axis and styling commands for a clear exploratory or report-ready plot.

Connects to

Data Driven Analytics · Communication

Seaborn visualisation

Study file: Seaborn_Cheat_Sheet.pdf

For

Analysts exploring distributions and relationships

Use it to

Choose statistical plotting patterns and compare groups before writing an interpretation.

Connects to

Analytics · Communication

Scikit-learn workflow

Study file: Scikit-Learn_Cheat_Sheet.pdf

For

Learners building a first supervised or unsupervised model

Use it to

Trace preprocessing, splitting, fitting and evaluation; check every example against the current library API.

Connects to

Applied Machine Intelligence

Top machine learning algorithms

Study file: ML+Cheat+Sheet_2.pdf

For

Practitioners and decision makers comparing approaches

Use it to

Compare what common algorithms do, when they fit and where their assumptions or trade-offs matter.

Connects to

Applied Machine Intelligence · Capstone

The original publisher PDF link currently denies access. The download is DataCamp’s related supervised-model guide, not the exact supplied edition.

Deep learning with PyTorch

Study file: Deep_Learning_with_PyTorch_1.pdf

For

Python learners taking a first step into neural networks

Use it to

Review tensors, datasets, model training and evaluation as a compact reference alongside a hands-on exercise.

Connects to

Applied Machine Intelligence