▶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
▶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
▶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.
▶What this book covers
- Chapter 1, What You Can Do with Data Science, explores the practical applications of AI, data science, machine learning, deep learning, and causal inference.
- Chapter 2, Testing Your Models, explains how to distinguish good solutions from bad ones with the help of model testing. This chapter will also look at different types of metrics by using mathematical functions that evaluate the quality of predictions.
- Chapter 3, Understanding AI, looks into the inner workings of data science. Some of the main concepts behind machine learning and deep learning will be explored as well. This chapter will also give a brief introduction to data science.
- Chapter 4, An Ideal Data Science Team, explains how to build and sustain a data science team that is capable of delivering complex cross-functional projects. This chapter also gives us an understanding of the importance of software engineering and sourcing help from software development teams.
- Chapter 5, Conducting Data Science Interviews, covers how to conduct an efficient data science interview. This chapter also looks into the importance of setting goals before starting the interview process.
- Chapter 6, Building Your Data Science Team, develops guidelines for building data science teams. You will learn the three key aspects of building a successful team and the role of a leader in a data science team.
- Chapter 7, Managing Innovation, explores innovations and how to manage them. We will find out how to identify projects and problems that have real value behind them.
- Chapter 8, Managing Data Science Projects, explores the data science project life cycle that allows you to structure and plan tasks for your team. We will also look into what distinguishes analytical projects from software engineering projects.
- Chapter 9, Common Pitfalls of Data Science Projects, looks closely at the common pitfalls of data science projects. This chapter explores the mistakes that increase the risks associated with your projects and mitigates the issues one by one, following the data science project life cycle.
- Chapter 10, Creating Products and Improving Reusability, looks at how to grow data science products and improve your internal team performance by using reusable technology. We will also look at strategies for improving the reusability of your projects and explore the conditions that allow the building of standalone products from your experience.
- Chapter 11, Implementing ModelOps, will explore how ModelOps is related to DevOps and the main steps involved in the ModelOps pipeline. This chapter also looks at the strategies for managing code, versioning data, and sharing project environments between team members.
- Chapter 12, Building Your Technology Stack, looks at how to build and manage the data science technology stack. This chapter also discusses the differences between core and project-specific technology stacks and examines an analytical approach for comparing different technologies.
- Chapter 13, Conclusion, provides a list of books that help you advance your knowledge in the domain of data science.
