ePrivacy and GPDR Cookie Consent by Cookie Consent

What to read after Perspectives on Data Science for Software Engineering?

Hello there! I go by the name Robo Ratel, your very own AI librarian, and I'm excited to assist you in discovering your next fantastic read after "Perspectives on Data Science for Software Engineering" by Laurie Williams! 😉 Simply click on the button below, and witness what I have discovered for you.

Exciting news! I've found some fantastic books for you! 📚✨ Check below to see your tailored recommendations. Happy reading! 📖😊

Perspectives on Data Science for Software Engineering

Laurie Williams , Thomas Zimmermann , Tim Menzies

Computers / Database Administration & Management

Perspectives on Data Science for Software Engineering presents the best practices of seasoned data miners in software engineering. The idea for this book was created during the 2014 conference at Dagstuhl, an invitation-only gathering of leading computer scientists who meet to identify and discuss cutting-edge informatics topics.

At the 2014 conference, the concept of how to transfer the knowledge of experts from seasoned software engineers and data scientists to newcomers in the field highlighted many discussions. While there are many books covering data mining and software engineering basics, they present only the fundamentals and lack the perspective that comes from real-world experience. This book offers unique insights into the wisdom of the community’s leaders gathered to share hard-won lessons from the trenches.

Ideas are presented in digestible chapters designed to be applicable across many domains. Topics included cover data collection, data sharing, data mining, and how to utilize these techniques in successful software projects. Newcomers to software engineering data science will learn the tips and tricks of the trade, while more experienced data scientists will benefit from war stories that show what traps to avoid.

  • Presents the wisdom of community experts, derived from a summit on software analytics
  • Provides contributed chapters that share discrete ideas and technique from the trenches
  • Covers top areas of concern, including mining security and social data, data visualization, and cloud-based data
  • Presented in clear chapters designed to be applicable across many domains
Do you want to read this book? 😳
Buy it now!

Are you curious to discover the likelihood of your enjoyment of "Perspectives on Data Science for Software Engineering" by Laurie Williams? Allow me to assist you! However, to better understand your reading preferences, it would greatly help if you could rate at least two books.