| | 資料類型 | 狀態 | 應還日期 | 預約人數 | 館藏地 | 索書號 | 條碼號 | | 找書 | 圖書 | 在架上 | | 0 | 總館
西文圖書區  | TA345 .B528 2026 | W116293 |
| 內容簡介 | "This book offers a concise and practical introduction to data science, specifically designed for engineering students with no prior experience. Data science is the backbone of predictive AI and an essential prerequisite for all modern engineers. This book 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. Based on a proven course at Purdue University, this book takes engineering students from zero to data-ready in less than a semester"-- |
讀者書評 |
尚無書評，
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