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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

Case study—creating a data science department

A large manufacturing company has decided to open a new data science department. They hired Robert as an experienced team leader and asked him to build the new department.

The first thing Robert did was research the scenarios his department should handle. He discovered that the understanding of data science was still vague in the company, and while some managers expected the department to build machine learning models and seek new data science use cases in the company, others wanted him to build data dashboards and reports. To create a balanced team, Robert first documented two team goals and confirmed that his views correctly summed up what the company's management wanted from the new team:

  • Data stewardship: Creating data marts, reports, and dashboards from the company's data warehouses based on incoming requests...
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