Book Image

Managing Data Science

By : Kirill Dubovikov
Book Image

Managing Data Science

By: Kirill 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.

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Table of Contents (18 chapters)
Free Chapter
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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Understanding data science project failure

Every data science project ends up being a software system that generates scheduled reports or operates online. The world of software engineering already provides us with a multitude of software project management methodologies, so why do we need to reinvent a special approach for data science projects? The answer is that data science projects require much more experimentation and have to tolerate far more failures than software engineering projects.

To see the difference between a traditional software system and a system with predictive algorithms, let's look at the common causes of failure for data science projects:

  • Dependence on data: A robust customer relationship management (CRM) system that organizes the sales process will work well in many organizations, independent of their business. A system that predicts the outcome of...
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