Data Science Create Teams That Ask the Right Questions and Deliver Real Value /

• Κύριος συγγραφέας: Rose, Doug (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berkeley, CA : Apress : Imprint: Apress, 2016.
• Θέματα: Computer science.; Project management.; Management information systems.; Big data.; Software engineering.; Computer Science.; Software Engineering.; Project Management.; Big Data/Analytics.; Software Management.

 Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business. Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story. Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts: You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes. You should form small, agile-like data teams that focus on delivering valuable insights early and often. You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry. 

 Part 1: Defining Data Science -- Chapter 1: Understanding Data Science -- Chapter 2: Covering Database Basics -- Chapter 3: Recognizing Different Data Types -- Chapter 4: Applying Statistical Analysis -- Chapter 5: Avoiding Pitfalls in Defining Data Science -- Part 2: Building your Data Science Team -- Chapter 6: Rounding Out Your Talent -- Chapter 7: Forming the Team -- Chapter 8: Starting the Work -- Chapter 9: Thinking Like a Data Science Team -- Chapter 10: Avoiding Pitfalls in Building Your Data Science Team -- Part 3: Delivering in Data Science Sprints -- Chapter 11: A New Way of Working -- Chapter 12: Using a Data Science Lifecycle -- Chapter 13: Working in Sprints -- Chapter 14: Avoiding Pitfalls in Delivering in Data Science Sprints -- Part 4: Asking Great Questions -- Chapter 15: Understanding Critical Thinking -- Chapter 16: Encouraging Questions -- Chapter 17: Places to Look for Questions -- Chapter 18: Avoiding Pitfalls in Asking Great Questions -- Chapter 19: Defining a Story -- Part 5: Storytelling with Data Science -- Chapter 20: Understanding Story Structure -- Chapter 21: Defining Story Details -- Chapter 22: Humanizing Your Story -- Chapter 23: Using Metaphors -- Chapter 24: Avoiding Storytelling Pitfalls -- Part 6: Finishing Up -- Chapter 25: Starting an Organizational Change.- .