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Managing Data Science

Managing Data Science

By : Dubovikov
5 (2)
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Managing Data Science

Managing Data Science

5 (2)
By: Dubovikov

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)
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1
Section 1: What is Data Science?
5
Section 2: Building and Sustaining a Team
9
Section 3: Managing Various Data Science Projects
14
Section 4: Creating a Development Infrastructure

Conclusion

First of all, thank you for reading this book! I hope that the material was helpful in presenting the overall management approach to data science projects. Data science management is a multidimensional topic that requires a manager to show technical, organizational, and strategic proficiency so that they can execute a data science strategy inside any organization.

First, the data science manager needs to understand what data science can do, as well as its technical limitations. Without an understanding of the basic concepts, it is extremely easy to misunderstand your colleagues or provide over-promising project results to the customer. The What is Data Science section of this book described the basics of machine learning and deep learning, including explanations behind the mathematical concepts that comprise machine learning algorithms. We explored the world of technology...

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