Data Science

Abstract

think of this book as having three overarching concepts: • The first is that you should mine your own company for talent. You can’t change your organization by hiring data science heroes. The best way to get value from data science is by changing part of your organization’s focus from managing objectives to researching and exploring. • The second is that you should form small agile-like data teams that focus on delivering insights early and often. • Finally, you can only 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. Much of the “science” in data science comes from the scientific method. You’re applying a scientific method to your data. This is an empirical approach to gaining new knowledge and insights. An empirical approach is where you gain new knowledge from observation and experimentation. When you dip your toe in the pool, you are using an empirical approach. You’re running a xvi Introduction small experiment and then reacting to the results. If the water’s too cold, you work on your tan. If the water’s warm, you can jump right in. You don’t have to be a statistician to be able to ask interesting questions or to run a small experiment. Many people in different fields can contribute to this method of inquiry. In fact, you often get the best questions and feedback when you have people from diverse backgrounds. This book divides the three big concepts into five parts. Each part is a skillset that you’ll need for a data science mindset. • Part I goes into the language and technology behind data science. • Part II is about building your data science team. • Part III is about how your team will work together to deliver insights and knowledge. • Part IV is about how a data science team should think about data. • Part V helps you tell an interesting story. Most scientists will tell you that your results won’t mean much if you can’t communicate your story.