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Managing Data Science
Managing Data Science

Managing Data Science: Effective strategies to manage data science projects and build a sustainable team

By Kirill Dubovikov
€19.99 €13.98
Book Nov 2019 290 pages 1st Edition
eBook
€19.99 €13.98
Print
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eBook
€19.99 €13.98
Print
€24.99
Subscription
Free Trial
Renews at €14.99p/m

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Managing Data Science

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:

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Key benefits

  • Learn the basics of data science and explore its possibilities and limitations
  • Manage data science projects and assemble teams effectively even in the most challenging situations
  • Understand management principles and approaches for data science projects to streamline the innovation process

Description

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.

What you will learn

Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages

Product Details

Country selected

Publication date : Nov 12, 2019
Length 290 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838826321
Category :
Concepts :

What do you get with eBook?

Product feature icon Instant access to your Digital eBook purchase
Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want

Product Details


Publication date : Nov 12, 2019
Length 290 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781838826321
Category :
Concepts :

Table of Contents

18 Chapters
1. Section 1: What is Data Science? Chevron down icon Chevron up icon
2. What You Can Do with Data Science Chevron down icon Chevron up icon
3. Testing Your Models Chevron down icon Chevron up icon
4. Understanding AI Chevron down icon Chevron up icon
5. Section 2: Building and Sustaining a Team Chevron down icon Chevron up icon
6. An Ideal Data Science Team Chevron down icon Chevron up icon
7. Conducting Data Science Interviews Chevron down icon Chevron up icon
8. Building Your Data Science Team Chevron down icon Chevron up icon
9. Section 3: Managing Various Data Science Projects Chevron down icon Chevron up icon
10. Managing Innovation Chevron down icon Chevron up icon
11. Managing Data Science Projects Chevron down icon Chevron up icon
12. Common Pitfalls of Data Science Projects Chevron down icon Chevron up icon
13. Creating Products and Improving Reusability Chevron down icon Chevron up icon
14. Section 4: Creating a Development Infrastructure Chevron down icon Chevron up icon
15. Implementing ModelOps Chevron down icon Chevron up icon
16. Building Your Technology Stack Chevron down icon Chevron up icon
17. Conclusion Chevron down icon Chevron up icon
18. Other Books You May Enjoy Chevron down icon Chevron up icon

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