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  • Data science strategy for dummies
  • 點閱:6
  • 作者: by Ulrika Jägare , forword by Lillian Pierson
  • 出版社:John Wiley & Sons, Inc.
  • 出版年:c2019
  • 集叢名:For dummies
  • ISBN:978-1-119-56625-0 ; 1-119-56625-8 ; 978-1-119-56626-7 ; 1-119-56626-6 ; 978-1-119-56627-4 ; 1-119-56627-4
  • 格式:EPUB,PDF
  • 附註:"Learning made easy" -- Cover. Includes index.

All the answers to your data science questions
 
Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists.
 
With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data.

 
Learn exactly what data science is and why it’s important
Adopt a data-driven mindset as the foundation to success
Understand the processes and common roadblocks behind data science
Keep your data science program focused on generating business value
Nurture a top-quality data science team
 
In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.


ABOUT THE AUTHOR

Ulrika Jägare is an M.Sc. Director at Ericsson AB. With a decade of experience in analytics and machine intelligence and 19 years in telecommunications, she has held leadership positions in R&D and product management. Ulrika was key to the Ericsson??s Machine Intelligence strategy and the recent Ericsson Operations Engine launch – a new data and AI driven operational model for Network Operations in telecommunications.

  • FOREWORD(第xv頁)
  • INTRODUCTION(第1頁)
    • About This Book(第2頁)
    • Foolish Assumptions(第3頁)
    • How This Book Is Organized(第3頁)
    • Icons Used In This Book(第4頁)
    • Beyond The Book(第4頁)
    • Where To Go From Here(第5頁)
  • PART 1: OPTIMIZING YOUR DATA SCIENCE INVESTMENT(第7頁)
    • CHAPTER 1: Framing Data Science Strategy(第9頁)
    • CHAPTER 2: Considering the Inherent Complexity in Data Science(第31頁)
    • CHAPTER 3: Dealing with Difficult Challenges(第41頁)
    • CHAPTER 4: Managing Change in Data Science(第51頁)
  • PART 2: MAKING STRATEGIC CHOICES FOR YOUR DATA(第65頁)
    • CHAPTER 5: Understanding the Past, Present, and Future of Data(第67頁)
    • CHAPTER 6: Knowing Your Data(第85頁)
    • CHAPTER 7: Considering the Ethical Aspects of Data Science(第97頁)
    • CHAPTER 8: Becoming Data-driven(第103頁)
    • CHAPTER 9: Evolving from Data-driven to Machine-driven(第113頁)
  • PART 3: BUILDING A SUCCESSFUL DATA SCIENCE ORGANIZATION(第119頁)
    • CHAPTER 10: Building Successful Data Science Teams(第121頁)
    • CHAPTER 11: Approaching a Data Science Organizational Setup(第133頁)
    • CHAPTER 12: Positioning the Role of the Chief Data Officer (CDO)(第145頁)
    • CHAPTER 13: Acquiring Resources and Competencies(第155頁)
  • PART 4: INVESTING IN THE RIGHT INFRASTRUCTURE(第173頁)
    • CHAPTER 14: Developing a Data Architecture(第175頁)
    • CHAPTER 15: Focusing Data Governance on the Right Aspects(第193頁)
    • CHAPTER 16: Managing Models During Development and Production(第203頁)
    • CHAPTER 17: Exploring the Importance of Open Source(第213頁)
    • CHAPTER 18: Realizing the Infrastructure(第223頁)
  • PART 5: DATA AS A BUSINESS(第233頁)
    • CHAPTER 19: Investing in Data as a Business(第235頁)
    • CHAPTER 20: Using Data for Insights or Commercial Opportunities(第243頁)
    • CHAPTER 21: Engaging Differently with Your Customers(第255頁)
    • CHAPTER 22: Introducing Data-driven Business Models(第265頁)
    • CHAPTER 23: Handling New Delivery Models(第281頁)
  • PART 6: THE PART OF TENS(第295頁)
    • CHAPTER 24: Ten Reasons to Develop a Data Science Strategy(第297頁)
    • CHAPTER 25: Ten Mistakes to Avoid When Investing in Data Science(第305頁)
  • INDEX(第315頁)
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