Data Science: A First Introduction with Python

Timbers, Tiffany, Campbell, Trevor, Lee, Melissa

Data Science: A First Introduction with Python
  • 出版商: CRC
  • 出版日期: 2024-08-23
  • 售價: $2,630
  • 貴賓價: 9.5 折 $2,499
  • 語言: 英文
  • 頁數: 432
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 103257223X
  • ISBN-13: 9781032572239
  • 相關分類: Python、程式語言、Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

Data Science: A First Introduction with Python focuses on using the Python programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. It emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. Based on educational research and active learning principles, the book uses a modern approach to Python and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The text will leave readers well-prepared for data science projects. It is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates at the University of British Columbia.

Key Features:

  • Includes autograded worksheets for interactive, self-directed learning.
  • Introduces readers to modern data analysis and workflow tools such as Jupyter notebooks and GitHub, and covers cutting-edge data analysis and manipulation Python libraries such as pandas, scikit-learn, and altair.
  • Is designed for a broad audience of learners from all backgrounds and disciplines.

商品描述(中文翻譯)

《數據科學：Python 的初步介紹》專注於使用 Python 程式語言在 Jupyter notebooks 中進行數據操作和清理，創建有效的視覺化，並利用分類、回歸、聚類和推斷從數據中提取見解。它強調清晰、可重現和可分享的工作流程，並涵蓋版本控制的基本知識。基於教育研究和主動學習原則，本書採用現代的 Python 方法，並附有自動評分的 Jupyter 工作表，以便進行互動式、自主學習。這本書將使讀者為數據科學項目做好充分準備。它旨在為來自各個學科的學習者設計，對數學和程式設計的先前知識要求最低。作者在不列顛哥倫比亞大學教學多年，已經磨練出這些材料，培訓了數千名本科生。

主要特色：
- 包含自動評分的工作表，以便進行互動式、自主學習。
- 向讀者介紹現代數據分析和工作流程工具，如 Jupyter notebooks 和 GitHub，並涵蓋尖端的數據分析和操作 Python 函式庫，如 pandas、scikit-learn 和 altair。
- 設計適合來自各種背景和學科的廣泛學習者。

作者簡介

Tiffany Timbers is an Associate Professor of Teaching in the Department of Statistics and Co-Director for the Master of Data Science program (Vancouver Option) at the University of British Columbia. In these roles she teaches and develops curriculum around the responsible application of Data Science to solve real-world problems. One of her favourite courses she teaches is a graduate course on collaborative software development, which focuses on teaching how to create R and Python packages using modern tools and workflows.

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and Bayesian theory. He was previously a postdoctoral associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT and a Ph.D. candidate in the Laboratory for Information and Decision Systems (LIDS) at MIT.

Melissa Lee is an Assistant Professor of Teaching in the Department of Statistics at the University of British Columbia. She teaches and develops curriculum for undergraduate statistics and data science courses. Her work focuses on student-centered approaches to teaching, developing and assessing open educational resources, and promoting equity, diversity, and inclusion initiatives.

Joel Ostblom is an Assistant Professor of Teaching in the Statistics Department at the University of British Columbia. He teaches and develops data science courses at the graduate and undergraduate level, with a focus on data visualization, data science ethics, and machine learning. Joel cares deeply about spreading data literacy and excitement over programmatic data analysis, which is reflected in his contributions to open source projects and openly accessible data science learning resources.

Lindsey Heagy is an Assistant Professor in the Department of Earth, Ocean and Atmospheric Sciences and Director of the Geophysical Inversion Facility at UBC. Her research combines computational methods in numerical simulations, inversions, and machine learning for using geophysical data to characterize the subsurface. Primary applications of interest include mineral exploration, carbon sequestration, groundwater, and environmental studies.

作者簡介(中文翻譯)

Tiffany Timbers 是不列顛哥倫比亞大學統計系的教學副教授及數據科學碩士課程（溫哥華選項）的共同主任。在這些角色中，她教授並開發有關負責任地應用數據科學以解決現實世界問題的課程。她教授的最愛課程之一是關於協作軟體開發的研究生課程，專注於教授如何使用現代工具和工作流程創建 R 和 Python 套件。

Trevor Campbell 是不列顛哥倫比亞大學統計系的副教授。他的研究專注於自動化、可擴展的貝葉斯推斷演算法、貝葉斯非參數、串流數據和貝葉斯理論。他曾在麻省理工學院的計算機科學與人工智慧實驗室（CSAIL）和數據、系統與社會研究所（IDSS）擔任博士後研究員，並在麻省理工學院的資訊與決策系統實驗室（LIDS）擔任博士候選人。

Melissa Lee 是不列顛哥倫比亞大學統計系的教學助理教授。她教授並開發本科統計和數據科學課程的課程內容。她的工作專注於以學生為中心的教學方法，開發和評估開放教育資源，並推動公平、多樣性和包容性倡議。

Joel Ostblom 是不列顛哥倫比亞大學統計系的教學助理教授。他教授並開發研究生和本科層級的數據科學課程，重點在於數據視覺化、數據科學倫理和機器學習。Joel 深切關心推廣數據素養和對程式化數據分析的熱情，這在他對開源項目和公開可獲得的數據科學學習資源的貢獻中得以體現。

Lindsey Heagy 是不列顛哥倫比亞大學地球、海洋與大氣科學系的助理教授及地球物理反演設施的主任。她的研究結合了數值模擬、反演和機器學習中的計算方法，利用地球物理數據來表徵地下結構。主要的應用興趣包括礦產勘探、碳封存、地下水和環境研究。

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