Data Science Foundations

Data science often looks like a pile of disconnected tools: a bit of Python here, some statistics there, a chart somewhere else. This book connects them into one workflow, so you can take a messy dataset and arrive at an insight that someone else can reproduce.

You begin with the data science workflow, then learn how to acquire and store data and clean and wrangle real-world data. You practise exploratory data analysis and descriptive statistics, learning to interpret what you find rather than just calculate it. Hypothesis testing and experimental reasoning teach you how to judge whether a pattern is real. Visualisation chapters focus on charts that explain, and SQL gives you direct access to analytical data.

Project case studies bring the pieces together, and the final chapter covers reporting, reproducibility and building a portfolio. You finish understanding the full path from raw data to clear, repeatable results, and able to build it yourself.