Managing Data Science

By Kirill Dubovikov
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    Section 1: What is Data Science?
About 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.
Publication date:
November 2019


Section 1: What is Data Science?

Before diving into the management issues of building systems around machine learning algorithms, we need to explore the topic of data science itself. What are the main concepts behind data science and machine learning? How do you build and test a model? What are the common pitfalls in this process? What kinds of models are there? What tasks can we solve using machine learning?

This section contains the following chapters:

About the Author
  • Kirill Dubovikov

    Kirill Dubovikov works as a CTO for Cinimex DataLab. He has more than 10 years of experience in architecting and developing complex software solutions for top Russian banks. Now, he leads the company's data science branch. His team delivers practical machine learning applications to businesses across the world. Their solutions cover an extensive list of topics, such as sales forecasting and warehouse planning, natural language processing (NLP) for IT support centers, algorithmic marketing, and predictive IT operations. Kirill is a happy father of two boys. He loves learning all things new, reading books, and writing articles for top Medium publications.

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Managing Data Science
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