How can we make interviews more relevant and time-intensive? An ideal interview would be testing in a real working environment for a couple of months. While there are several companies that can afford to operate without interviews, using paid probation periods instead, this is a very costly hiring strategy that not every business can afford. A good interview should serve as a substitute for real working experience. It should not be a test of a person's skill, but a test for a person's ability to perform a specific job well. If an ideal test for a candidate is a probation period, then the worst kind is a whiteboard interview (unless you are interviewing a computer science lecturer). To design a great interview, bring it as close as possible to your working process and to the issues you solve on a daily basis.
Managing Data Science
By :
Managing Data Science
By:
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.
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Current Title:
Managing Data Science
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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