Data Science
Apress (Verlag)
978-1-4842-2252-2 (ISBN)
- Shows how to create data science teams from existing talent in organizations
- Teaches how to cost-efficiently extract maximum business value from the organizations’ data
- Presents the data science life cycle (DSLC) to model the essential processes and practices for delivering value
- Details the use of sprints and storytelling to help teams stay on track and adapt to new knowledge
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.
What Your Will Learn:
Shows project managers and team leaders how to create data science teams from existing talent in their organizations to cost-efficiently extract maximum business value from their organizations’ data
Introduces managers to key data science terms and concepts
Gives practical guidance on how to create and integrate an effective data science team with key roles and the responsibilities for each team member
Presents the data science life cycle (DSLC) to model essential processes and practices for delivering value
Details the use of sprints and storytelling to help the team stay on track and adapt to new knowledge
This book is for Data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.
Doug Rose specializes in organizational coaching, training, and change management. He has worked for over twenty years transforming organizations with technology and training and helping large companies optimize their business processes and improve productivity and delivery. He teaches business and project management courses at the University of Chicago, Syracuse University, and the University of Virginia, and he delivers his online Agile @Work business courses on Lynda.com. He is the author of Leading Agile Teams (PMI Press). Rose has an MS in Information Management and a JD from Syracuse University, and a BA from the University of Wisconsin-Madison.
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
| Erscheinungsdatum | 28.11.2016 |
|---|---|
| Zusatzinfo | 2 black & white illustrations, 43 colour illustrations, biography |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 427 g |
| Einbandart | kartoniert |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik | |
| Wirtschaft ► Betriebswirtschaft / Management ► Projektmanagement | |
| Schlagworte | Agile • Analytics • Big Data • critical thinking • data-driven organization • Data Mining • Data Science • Datenmanagement • essential questions • Intuition • predictive analytics • Project Management • Scientific Method • Scrum • SDLC • Statistics • Storytelling • Team • Visualization |
| ISBN-10 | 1-4842-2252-0 / 1484222520 |
| ISBN-13 | 978-1-4842-2252-2 / 9781484222522 |
| Zustand | Neuware |
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