Guide to Data Science

Understanding the DNA of data science

Editor’s Note: The Field Guide to Data Science was created to help organizations of all types and missions understand how to make use of data as a resource. In this, the second edition, we updated the guide with new features and content—and it clearly resonated, becoming our most-read piece of content, by far, since it was published in December 2015. That’s why we continue to showcase The Field Guide to Data Science in our thought leadership. Data science is a fast-changing field, and the insights contained here will give you a deeper understanding into how to make data science a transformational force throughout your organization. Enjoy. And while you’re here, check out this case study on our use of data science and analytics for one of the world’s leading airlines.

The Field Guide to Data Science spells out what data science is, why it matters to organizations, as well as how to create data science teams. Along the way, our team of experts provides field-tested approaches, personal tips and tricks, and real-life case studies. Senior leaders will walk away with a deeper understanding of the concepts at the heart of data science, practitioners will add to their toolboxes, and beginners will find insights to help them start on their data science journey.

Start Here for the Basics

You’ll get an introduction to data science, including what makes data science unique from other analysis approaches. We help you understand data science maturity within an organization and how to create a robust data science capability.

Putting it All Together

Finally, we highlight our successes creating data science solutions for our clients. It follows several projects from data to insights to illustrate the impact data science can have on your organization.

Life in the Trenches

Here, we give you a firsthand account of life as a data scientist. We share insights on a variety of data science topics through illustrative case studies. We provide tips and tricks from our own experiences on these real-life analytic challenges.

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