Ilias Bilionis – författare

This book offers engineering students a concise and practical introduction to data science — no prior experience required. Designed specifically for those new to programming and statistical analysis, the book introduces the essential tools and concepts behind today's predictive AI systems.Based on a proven course at Purdue University, Introduction to Data Science for Engineering Students equips students with core data science knowledge, such as Python programming, data analysis techniques, and key foundational statistical concepts necessary for predictive modelling. Through real-world engineering examples (e.g. predicting engine efficiency), students learn how to visualize and analyze real experimental data, apply probability to manage uncertainty, and learn how to build reliable predictive models step-by-step.Covering everything from data arrays and visualization to logistic regression and maximum likelihood estimation, the book prepares students to become data-ready in less than a semester. By the end of the book, readers will have gained not only theoretical insight but also hands-on experience with tools they can use immediately in labs, internships, or future careers. This is a must-have primer for any engineering student seeking to become data-literate in an increasingly AI-driven world.

This book offers engineering students a concise and practical introduction to data science — no prior experience required. Designed specifically for those new to programming and statistical analysis, the book introduces the essential tools and concepts behind today's predictive AI systems.Based on a proven course at Purdue University, Introduction to Data Science for Engineering Students equips students with core data science knowledge, such as Python programming, data analysis techniques, and key foundational statistical concepts necessary for predictive modelling. Through real-world engineering examples (e.g. predicting engine efficiency), students learn how to visualize and analyze real experimental data, apply probability to manage uncertainty, and learn how to build reliable predictive models step-by-step.Covering everything from data arrays and visualization to logistic regression and maximum likelihood estimation, the book prepares students to become data-ready in less than a semester. By the end of the book, readers will have gained not only theoretical insight but also hands-on experience with tools they can use immediately in labs, internships, or future careers. This is a must-have primer for any engineering student seeking to become data-literate in an increasingly AI-driven world.