Data Scientists at Work (Paperback)
暫譯: 數據科學家的工作

Sebastian Gutierrez

Data Scientists at Work (Paperback)
  • 出版商: Apress
  • 出版日期: 2014-12-08
  • 售價: $1,520
  • 貴賓價: 9.5 折 $1,444
  • 語言: 英文
  • 頁數: 364
  • 裝訂: Paperback
  • ISBN: 1430265981
  • ISBN-13: 9781430265986
  • 相關分類: 大數據 Big-data、Data Science

    • 海外代購書籍(需單獨結帳)

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商品描述

Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report.

Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); oceanographic big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). 

Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees' earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.

Readers will learn:
  • How the data scientists arrived at their positions and what advice they have for others
  • What projects the data scientists work on and the techniques and tools they apply
  • How to frame problems that data science can solve
  • Where data scientists think the most exciting opportunities lie in the future of data science
  • How data scientists add value to their organizations and help people around the world

Who this book is for
The primary readership for this book is general-interest readers interested in this hot new profession and in the nature of the people who work up the readers' own data trails. The secondary readerships are (a) scientists, mathematicians, and students in feeder disciplines who are interested in scouting the vocational prospects and daily working conditions of data scientists with a view to becoming data scientists themselves, and (b) of business colleagues and managers seeking to understand and collaborate with data scientists to integrate their data management and interpretation capabilities into the competitive intelligence capabilities of the enterprise.

Table of Contents
Chapter 1. Chris Wiggins (The New York Times)
Chapter 2. Caitlin Smallwood (Netflix)
Chapter 3. Yann LeCun (Facebook)
Chapter 4. Erin Shellman (Nordstrom)
Chapter 5. Daniel Tunkelang (LinkedIn)
Chapter 6. John Foreman (MailChimp)
Chapter 7. Roger Ehrenberg (IA Ventures)
Chapter 8. Claudia Perlich (Dstillery)
Chapter 9. Jonathan Lenaghan (PlaceIQ)
Chapter 10. Anna Smith (Rent The Runway)
Chapter 11. Andre Karpistsenko (Planet OS)
Chapter 12. Amy Heineike (Quid)
Chapter 13. Victor Hu (Next Big Sound)
Chapter 14. Kira Radinsky (SalesPredict)
Chapter 15. Eric Jonas (Independent Scientist)
Chapter 16. Jake Porway (DataKind)

商品描述(中文翻譯)

《數據科學家的工作》是一本訪談集，收錄了來自這個熱門新職業領域的十六位全球最具影響力和創新性的數據科學家的訪談。根據《哈佛商業評論》，「數據科學家是21世紀最性感的工作」。根據麥肯錫的報告，到2018年，美國將面臨190,000名技術熟練的數據科學家短缺的問題。

通過深入的訪談，本書探討了數據科學實踐的「什麼」、「如何」和「為什麼」，從各行各業的傑出從業者的故事、想法、行業對話和預測中挖掘出來：社交網絡（Yann LeCun, Facebook）；專業網絡（Daniel Tunkelang, LinkedIn）；風險投資（Roger Ehrenberg, IA Ventures）；企業雲計算和神經科學（Eric Jonas, 前Salesforce.com）；報紙和媒體（Chris Wiggins, The New York Times）；串流電視（Caitlin Smallwood, Netflix）；音樂預測（Victor Hu, Next Big Sound）；戰略情報（Amy Heineike, Quid）；海洋大數據（André Karpištšenko, Planet OS）；地理空間市場情報（Jonathan Lenaghan, PlaceIQ）；廣告（Claudia Perlich, Dstillery）；時尚電子商務（Anna Smith, Rent the Runway）；專業零售（Erin Shellman, Nordstrom）；電子郵件行銷（John Foreman, MailChimp）；預測銷售情報（Kira Radinsky, SalesPredict）；以及人道非營利組織（Jake Porway, DataKind）。

這些數據科學家分享了他們如何運用創意、想像力、耐心和熱情，將大數據、數據可視化、搜索和統計的技術應用於特定工作。 《數據科學家的工作》揭示了受訪者最早的數據項目、他們如何成為數據科學家、在數據工作中的發現和驚喜、對這個職業的過去、現在和未來的看法、在組織內的團隊合作經驗，以及他們在將大量原始數據轉化為對其組織和客戶具有商業、科學和教育價值的物件時所獲得的見解。

讀者將學到：
- 數據科學家是如何到達他們的職位的，以及他們對其他人的建議
- 數據科學家正在進行的項目以及他們所應用的技術和工具
- 如何框定數據科學可以解決的問題
- 數據科學家認為未來數據科學中最令人興奮的機會在哪裡
- 數據科學家如何為他們的組織增值並幫助全球的人們

本書的讀者對象
本書的主要讀者是對這個熱門新職業及從事數據工作的人的性質感興趣的一般讀者。次要讀者包括（a）對數據科學家的職業前景和日常工作條件感興趣的科學家、數學家和相關學科的學生，旨在成為數據科學家，以及（b）希望理解和與數據科學家合作的商業同事和管理者，以將他們的數據管理和解釋能力整合到企業的競爭情報能力中。

目錄
第1章. Chris Wiggins (The New York Times)
第2章. Caitlin Smallwood (Netflix)
第3章. Yann LeCun (Facebook)
第4章. Erin Shellman (Nordstrom)
第5章. Daniel Tunkelang (LinkedIn)
第6章. John Foreman (MailChimp)
第7章. Roger Ehrenberg (IA Ventures)
第8章. Claudia Perlich (Dstillery)
第9章. Jonathan Lenaghan (PlaceIQ)
第10章. Anna Smith (Rent The Runway)
第11章. Andre Karpistsenko (Planet OS)
第12章. Amy Heineike (Quid)
第13章. Victor Hu (Next Big Sound)
第14章. Kira Radinsky (SalesPredict)
第15章. Eric Jonas (獨立科學家)
第16章. Jake Porway (DataKind)

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