If you have a software engineering background, how often have interviewers asked you to reverse a binary tree on a whiteboard, or to find a maximum subarray sum? If you come from a data science background, how many central limit theorem proofs did you lay out on a piece of paper or a whiteboard? If you are a team leader, have you asked such questions yourself? I am not implying that those questions are bad, but quite the opposite. Knowledge of core computer science algorithms and the ability to derive proofs may be important for some jobs. But for what purpose do we ask those questions? What do we want to know about the person on the other side of the table? For most companies, the ability to give answers to those questions is not relevant at all. What is the reason for asking them? Well, because stereotypical programmers must know algorithms...
Managing Data Science
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Managing Data Science
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Overview of this book
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.
Table of Contents (18 chapters)
What You Can Do with Data Science
Testing Your Models
Understanding AI
Section 2: Building and Sustaining a Team
An Ideal Data Science Team
Conducting Data Science Interviews
Building Your Data Science Team
Section 3: Managing Various Data Science Projects
Managing Innovation
Managing Data Science Projects
Common Pitfalls of Data Science Projects
Creating Products and Improving Reusability
Section 4: Creating a Development Infrastructure
Implementing ModelOps
Building Your Technology Stack
Conclusion
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