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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

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Managing Data Science

What You Can Do with Data Science

I once told a friend who works as a software developer about one of the largest European data science conferences. He showed genuine interest and asked whether we could go together. Sure, I said. Let's broaden our knowledge together. It will be great to talk to you about machine learning. Several days later, we were sitting in the middle of a large conference hall. The first speaker had come on stage and told us about some technical tricks he used to win several data science competitions. When the next speaker talked about tensor algebra, I noticed a depleted look in the eyes of my friend.

What's up? I asked.

I'm just wondering when they'll show us the robots.

To avoid having incorrect expectations, we need to inform ourselves. Before building a house, you'd better know how a hammer works. Having basic...

Defining AI

Media and news use AI as a substitute buzzword for any technology related to data analysis. In fact, AI is a sub-field of computer science and mathematics. It all started in the 1950s, when several researchers started asking whether computers can learn, think, and reason. 70 years later, we still do not know the answer. However, we have made significant progress in a specific kind of AI that solves thoroughly specified narrow tasks: weak AI.

Science fiction novels tell about machines that can reason and think like humans. In scientific language, they are described as strong AI. Strong AI can think like a human, and its intellectual abilities may be much more advanced. The creation of strong AI remains the main long-term dream of the scientific community. However, practical applications are all about weak AI. While strong AI tries to solve the problem of general intelligence...

Introduction to machine learning

Machine learning is by far the most important tool of a data scientist. It allows us to create algorithms that discover patterns in data with thousands of variables. We will now explore different types and capabilities of machine learning algorithms.

Machine learning is a scientific field that studies algorithms that can learn to perform tasks without specific instructions, relying on patterns discovered in data. For example, we can use algorithms to predict the likelihood of having a disease or assess the risk of failure in complex manufacturing equipment. Every machine learning algorithm follows a simple formula. In the following diagram, you can see a high-level decision process that is based on a machine learning algorithm. Each machine learning model consumes data to produce information that can support human decisions or fully automate them...

Introduction to deep learning

Before writing this section, I was thinking about the many ways we can draw a line between machine learning and deep learning. Each of them was contradictory in some way. In truth, you can't separate deep learning from machine learning because deep learning is a subfield of machine learning. Deep learning studies a specific set of models called neural networks. The first mentions of the mathematical foundations of neural networks date back to the 1980s, and the theory behind modern neural networks originated in 1958. Still, they failed to show good results until the 2010s. Why?

The answer is simple: hardware. Training big neural networks uses a great amount of computation power. But not any computation power will suffice. It turns out that neural networks do a lot of matrix operations under the hood. Strangely, rendering computer graphics also...

Deep learning use case

To show how deep learning may work in practical settings, we will explore product matching.

Up-to-date pricing is very important for large internet retailers. In situations where your competitor lowers the price of a popular product, late reaction leads to large profit losses. If you know the correct market price distributions for your product catalog, you can always remain a step ahead of your competitors. To create such a distribution for a single product, you first need to find this product description on a competitor's site. While automated collection of product descriptions is easy, product matching is the hard part.

Once we have a large volume of unstructured text, we need to extract product attributes from it. To do this, we first need to tell whether two descriptions refer to the same product. Suppose that we have collected a large dataset of...

Introduction to causal inference

Up to this point, we have talked about predictive models. The main purpose of a predictive model is to recognize and forecast. The explanation behind the model's reasoning is of lower priority. On the contrary, causal inference tries to explain relationships in the data rather than to make predictions about the future events. In causal inference, we check whether an outcome of some action was not caused by so-called confounding variables. Those variables can indirectly influence action through the outcome. Let's compare causal inference and predictive models through several questions that they can help to answer:

  • Prediction models:
    • When will our sales double?
    • What is the probability of this client buying a certain product?
  • Causal inference models:
    • Was this cancer treatment effective? Or is the effect apparent only because of the...

Summary

In this chapter, we have explored the practical applications of AI, data science, machine learning, deep learning, and causal inference. We have defined machine learning as a field that studies algorithms that use data to support decisions and give insights without specific instructions. There are three main machine learning methodologies: supervised, unsupervised, and reinforcement learning. In practice, the most common types of task we solve using machine learning are regression and classification. Next, we described deep learning as a subset of machine learning devoted to studying neural network algorithms. The main application domains of deep learning are computer vision and NLP. We have also touched on the important topic of causal inference: the field that studies a set of methods for discovering causal relationships in data. You now know a lot about general data...

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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.

Who is this book for?

This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

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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Publication date : Nov 12, 2019
Length: 290 pages
Edition : 1st
Language : English
ISBN-13 : 9781838826321
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Table of Contents

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

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
5 star 100%
4 star 0%
3 star 0%
2 star 0%
1 star 0%
Gustavo Hernandez Jun 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good content, tools and techniques to manage Data Science projects.
Amazon Verified review Amazon
Ignacio Estrada Dec 21, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very useful book, providing an overview of best practices, pitfalls etc. for Data Science projects. I found it particularly useful (vs. other books I've read) for someone like me, coming from the business side, trying to add value through Data Science.
Amazon Verified review Amazon
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