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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:

Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization´s data

Understand key data science terms and concepts

Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member

Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering value

Use sprints and storytelling to help your team stay on track and adapt to new knowledge

Who 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.

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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

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