Beginning Data Science with Python and Jupyter

Beginning Data Science with Python and Jupyter is a practical introduction to working with data in Python, built around the notebook-first workflow that makes Jupyter such a natural fit for exploration, analysis, and reproducible results. Alex Galea takes the reader through the early stages of data science with a clear emphasis on doing rather than simply reading about it.

Start with the Jupyter workflow

The opening material focuses on the essentials of Jupyter fundamentals: navigating notebooks, using features that speed up work, and bringing in the Python libraries needed for analysis. From there, the book moves into exploratory work with a real dataset, helping readers see how notebook-based analysis supports quick experimentation and clearer thinking.

From cleaning data to building models

Later sections introduce practical machine learning topics in a way that keeps the workflow grounded. The book covers preprocessing, classification, cross-validation, and dimensionality reduction, showing how these ideas fit together in a real analysis pipeline rather than in isolation.

Web scraping and interactive presentation

The final section extends the toolkit beyond local data. Readers work through HTTP requests, HTML parsing, and web scraping before turning to interactive visualisation with Bokeh. That combination gives the book a nice balance: it is not just about modelling, but about collecting, preparing, and presenting data too.

What this ebook is especially good for

• Readers who are new to data science and want a guided, hands-on introduction
• Python users who want to work more effectively in Jupyter Notebooks
• Beginners looking for a practical path into data cleaning, modelling, and visualisation
• Anyone who wants to understand the full early-stage workflow from raw data to readable results

For shoppers looking for a focused beginner resource on Python data science in Jupyter, this ebook offers a well-scaffolded route through the tools and techniques that matter most at the start of the learning curve.