Data Science and Analytics Strategy: An Emergent Design Approach
暫譯: 數據科學與分析策略：一種新興設計方法

Awati, Kailash, Scriven, Alexander

Data Science and Analytics Strategy: An Emergent Design Approach
  • 出版商: CRC
  • 出版日期: 2023-04-05
  • 售價: $2,010
  • 貴賓價: 9.5 折 $1,910
  • 語言: 英文
  • 頁數: 230
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032196327
  • ISBN-13: 9781032196329
  • 相關分類: Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

This book describes how to establish data science and analytics capabilities in organisations using Emergent Design, an evolutionary approach that increases the chances of successful outcomes while minimising upfront investment. Based on their experiences and those of a number of data leaders, the authors provide actionable advice on data technologies, processes, and governance structures so that readers can make choices that are appropriate to their organisational contexts and requirements.

The book blends academic research on organisational change and data science processes with real-world stories from experienced data analytics leaders, focusing on the practical aspects of setting up a data capability. In addition to a detailed coverage of capability, culture, and technology choices, a unique feature of the book is its treatment of emerging issues such as data ethics and algorithmic fairness.

Data Science and Analytics Strategy: An Emergent Design Approach has been written for professionals who are looking to build data science and analytics capabilities within their organisations as well as those who wish to expand their knowledge and advance their careers in the data space. Providing deep insights into the intersection between data science and business, this guide will help professionals understand how to help their organisations reap the benefits offered by data. Most importantly, readers will learn how to build a fit-for-purpose data science capability in a manner that avoids the most common pitfalls.

商品描述(中文翻譯)

這本書描述了如何在組織中建立資料科學和分析能力，採用Emergent Design（漸進設計）這一演進性方法，該方法在最小化前期投資的同時，提高成功結果的機會。根據作者及多位資料領導者的經驗，提供了關於資料技術、流程和治理結構的可行建議，使讀者能夠根據其組織的背景和需求做出適當的選擇。

本書將有關組織變革和資料科學流程的學術研究與來自經驗豐富的資料分析領導者的真實故事相結合，重點關注建立資料能力的實際方面。除了對能力、文化和技術選擇的詳細探討外，本書的一個獨特特點是對新興議題如資料倫理和演算法公平性的處理。

《資料科學與分析策略：漸進設計方法》是為那些希望在其組織內建立資料科學和分析能力的專業人士撰寫的，同時也適合希望擴展其知識並在資料領域推進職業生涯的人士。這本指南深入探討了資料科學與商業之間的交集，將幫助專業人士理解如何幫助其組織獲取資料所提供的好處。最重要的是，讀者將學會如何以避免最常見陷阱的方式建立適合目的的資料科學能力。

作者簡介

Kailash Awati is a data and sensemaking professional with a deep interest in helping organisations tackle complex problems. He is an Adjunct Fellow in Human-Centred Data Science at the UTS Connected Intelligence Centre and a Data and Insights Manager at a government agency. Over the last decade, he has established data capabilities in diverse organisations using the principles described in this book. In addition to his work in industry, he has developed and taught postgraduate courses in machine learning and decision-making under uncertainty. He is the co-author of two well-regarded books on managing socially complex problems in organisations: The Heretic's Guide to Best Practices and The Heretic's Guide to Management.

Alexander Scriven is a senior data scientist at Atlassian in Sydney, Australia, and has experience across start-ups, government, and enterprise building analytical capacities and executing on data science projects. He greatly enjoys teaching and mentoring and has built and delivered both master's-level university courses in machine learning and deep learning and highly rated courses for online platforms such as Datacamp. His research interests are in applying data science techniques to novel industry challenges. Alex greatly enjoys bridging the gap between cutting-edge technology and business applications.

作者簡介(中文翻譯)

Kailash Awati 是一位數據與意義建構專業人士，對於幫助組織解決複雜問題有著深厚的興趣。他是 UTS 連結智慧中心的人本數據科學兼任研究員，並擔任某政府機構的數據與洞察經理。在過去十年中，他利用本書中所描述的原則，在多樣的組織中建立了數據能力。除了在業界的工作外，他還開發並教授有關機器學習和不確定性下決策的研究生課程。他是兩本備受推崇的書籍的共同作者，這些書籍探討了如何在組織中管理社會複雜問題：異端者的最佳實踐指南 和 異端者的管理指南。

Alexander Scriven 是澳大利亞悉尼 Atlassian 的高級數據科學家，擁有在初創公司、政府和企業中建立分析能力及執行數據科學項目的經驗。他非常喜歡教學和指導，並且建立並提供了碩士級別的機器學習和深度學習大學課程，以及在 Datacamp 等線上平台上高度評價的課程。他的研究興趣在於將數據科學技術應用於新穎的行業挑戰。Alex 非常享受在尖端技術與商業應用之間架起橋樑。

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