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

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

Who This Book Is For

The primary readership for this book is data science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.

Table of Contents

Part I: 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 II: 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 III: Delivering in Data Science Sprints
Chapter 11: A New Way of Working
Chapter 12: Using a Data Science Life Cycle
Chapter 13: Working in Sprints
Chapter 14: Avoiding Pitfalls in Delivering in Data Science Sprints

Part IV: 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

Part V: Storytelling with Data Science
Chapter 19: Defining a Story
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 VI: Finishing Up
Chapter 25: Starting an Organizational Change