Managing Data Science

Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization

Key Features

• Learn the basics of data science and explore its possibilities and limitations
• Manage data science projects and assemble teams effectively even in the most challenging situations
• Understand management principles and approaches for data science projects to streamline the innovation process

Book Description

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way.

After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps.

By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.

What you will learn

• Understand the underlying problems of building a strong data science pipeline
• Explore the different tools for building and deploying data science solutions
• Hire, grow, and sustain a data science team
• Manage data science projects through all stages, from prototype to production
• Learn how to use ModelOps to improve your data science pipelines
• Get up to speed with the model testing techniques used in both development and production stages

Who this book is for

This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Table of Contents

1. What You Can Do with Data Science
2. Testing Your Models
3. Understanding AI
4. An ideal Data Science team
5. Conducting Data Science Interviews
6. Building Your Data Science Team
7. Managing Innovation
8. Managing Data Science Projects
9. Common Pitfalls of Data Science Projects
10. Creating Products and Improving Reusability
11. Implementing ModelOps
12. Building your Technology Stack
13. Conclusion

Kirill Dubovikov works as a CTO for Cinimex DataLab. He has more than 10 years of experience in architecting and developing complex software solutions for top Russian banks. Now, he leads the company's data science branch. His team delivers practical machine learning applications to businesses across the world. Their solutions cover an extensive list of topics, such as sales forecasting and warehouse planning, natural language processing (NLP) for IT support centers, algorithmic marketing, and predictive IT operations.

Kirill is a happy father of two boys. He loves learning all things new, reading books, and writing articles for top Medium publications.