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

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Product Details

Publication date : Nov 12, 2019

Length 290 pages

Edition : 1st Edition

Language : English

ISBN-13 : 9781838826321

Category :

Data

Concepts :

Data Science

18 Chapters

1. Section 1: What is Data Science?

Section 1: What is Data Science?

2. What You Can Do with Data Science

What You Can Do with Data Science

Defining AI

Introduction to machine learning

Introduction to deep learning

Deep learning use case

Introduction to causal inference

Summary

3. Testing Your Models

Testing Your Models

Offline model testing

Online model testing

Summary

4. Understanding AI

Understanding AI

Understanding mathematical optimization

Thinking with statistics

How do machines learn?

Exploring machine learning

Exploring deep learning

Summary

5. Section 2: Building and Sustaining a Team

Section 2: Building and Sustaining a Team

6. An Ideal Data Science Team

An Ideal Data Science Team

Defining data science team roles

Exploring data science team roles and their responsibilities

Summary

7. Conducting Data Science Interviews

Conducting Data Science Interviews

Common flaws of technical interviews

Introducing values and ethics into the interview

Designing good interviews

Summary

8. Building Your Data Science Team

Building Your Data Science Team

Achieving team Zen

Leadership and people management

Facilitating a growth mindset

Case study—creating a data science department

Summary

9. Section 3: Managing Various Data Science Projects

Section 3: Managing Various Data Science Projects

10. Managing Innovation

Managing Innovation

Understanding innovations

Why do big organizations fail so often?

Exploring innovation management

Balancing sales, marketing, team leadership, and technology

Managing innovations in a big company

Managing innovations in a start-up company

Finding project ideas

Summary

11. Managing Data Science Projects

Managing Data Science Projects

Understanding data science project failure

Exploring the data science project life cycle

Choosing a project management methodology

Choosing a methodology that suits your project

Estimating data science projects

Discovering the goals of the estimation process

Summary

12. Common Pitfalls of Data Science Projects

Common Pitfalls of Data Science Projects

Avoiding the common risks of data science projects

Approaching research projects

Dealing with prototypes and MVP projects

Mitigating risks in production-oriented data science systems

Summary

13. Creating Products and Improving Reusability

Creating Products and Improving Reusability

Thinking of projects as products

Determining the stage of your project

Improving reusability

Seeking and building products

Summary

14. Section 4: Creating a Development Infrastructure

Section 4: Creating a Development Infrastructure

15. Implementing ModelOps

Implementing ModelOps

Understanding ModelOps

Looking into DevOps

Managing code versions and quality

Storing data along with the code

Managing environments

Tracking experiments

The importance of automated testing

Packaging code

Continuous model training

Case study – building ModelOps for a predictive maintenance system

A power pack for your projects

Summary

16. Building Your Technology Stack

Building Your Technology Stack

Defining the elements of a technology stack

Choosing between core- and project-specific technologies

Comparing tools and products

Summary

17. Conclusion

Conclusion

Advancing your knowledge

Summary

18. Other Books You May Enjoy

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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.

See other products by Kirill Dubovikov

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

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