Introduction to Data Science for Engineering Students

Engineering work increasingly depends on making sense of data—but getting started can feel like a leap from equations to unfamiliar tools. Ilias Bilionis builds a practical bridge in Introduction to Data Science for Engineering Students, starting with Python and the essentials of working with real datasets before moving toward predictive models and uncertainty.

Developed from a Purdue University sophomore course, the book takes an introductory, lecture-by-lecture approach. Its examples connect core ideas to engineering questions, including predicting engine efficiency from operating conditions.

Start with data, code, and clear visual thinking

Readers are introduced to Python in a Jupyter Notebook environment, along with loading and visualizing data. The book shows how to summarize and compare datasets using statistics estimated from observations—and how visual summaries can reveal patterns that a table of numbers may not make immediately apparent.

Make uncertainty part of the analysis

Because engineering data and measurements involve randomness, probability is a central thread. The material explains how to represent uncertainty, apply probability rules to propagate it, and estimate probabilities from data. These ideas give readers a basis for interpreting results rather than treating every measurement or prediction as exact.

From observations to predictive models

The modeling material introduces regression, which predicts continuous quantities, and classification, which assigns discrete labels. An example based on experimental data connects those methods to an engineering task: estimating engine efficiency from operating conditions. Model evaluation and the use of predictions in consequential decisions also enter the discussion.

Learn by running the code

Replicability is an explicit part of Bilionis’s approach: code is provided to reproduce the book’s results and figures. Sections marked for Colab connect to Jupyter notebooks that readers can run and modify, making the concepts easier to test against the actual code and data.

A grounded first step into engineering data science

This introduction is aimed at engineering students new to data science, and may also suit readers who want a structured first encounter with Python-based data analysis and predictive modeling. Calculus and matrix-vector multiplication help with the material; prior programming experience is useful, though the book is designed for readers without it. For anyone ready to move from engineering data to informed analysis, this is a focused place to begin.