Book Image

Principles of Strategic Data Science

By : Peter Prevos
Book Image

Principles of Strategic Data Science

By: Peter Prevos

Overview of this book

Mathematics and computer science form an integral part of data science, and understanding them is crucial for efficiently managing data. This book is designed to take you through the entire data science pipeline and help you join the dots between mathematics, programming, and business analysis. You’ll start by learning what data science is and how organizations can use it to revolutionize the way they use their data. The book then covers the criteria for the soundness of data products and demonstrates how to effectively visualize information. As you progress, you’ll discover the strategic aspects of data science by exploring the five-phase framework that enables you to enhance the value you extract from data. Toward the concluding chapters, you’ll understand the role of a data science manager in helping an organization take the data-driven approach. By the end of this book, you’ll have a good understanding of data science and how it can enable you to extract value from your data.

Related Content you might be interested in

Current Title:

Small book image
Principles of Strategic Data Science
Peter Prevos
Jun, 2019 | 104 pages
Small book image
Creators of Intelligence
Dr Alex Antic
Apr, 2023 | 374 pages
Small book image
AI & Data Literacy
Bill Schmarzo
Jul, 2023 | 238 pages
Small book image
Managing Data Science
Kirill Dubovikov
Nov, 2019 | 290 pages
Table of Contents (6 chapters)
Preface
Preface
About the Book
Acknowledgements
Free Chapter
1
Chapter 1: What is Data Science?
Chapter 1: What is Data Science?
Introduction
Data-Driven Organization
The Data Revolution
The Elements of Data Science
The Purpose of Data Science
2
Chapter 2: Good Data Science
Chapter 2: Good Data Science
Introduction
A Data Science Trivium
Useful Data Science
Sound Data Science
Aesthetic Data Science
Best-Practice Data Science
3
Chapter 3: Strategic Data Science
Chapter 3: Strategic Data Science
Introduction
The Data Science Continuum
Collecting Data
Descriptive Statistics
Diagnostics
Predicting the Future
Prescribing Action
Toward a Data-Driven Organization
4
Chapter 4: The Data-Driven Organization
Chapter 4: The Data-Driven Organization
Introduction
People
Systems
Process
The Limitations of Data Science
5
References
References

The Limitations of Data Science

The problems that data science can solve seem to be limitless when reading some of the articles on the web and in literature. There are, however, theoretical and practical limitations to what a computational analysis can achieve. Besides limits to what we can do, there are also ethical limits to what we should be doing. This final section reflects on some of the boundaries of data science.

The Limits of Computation

The limitations of computation relate to some complex and deeply philosophical issues concerning the limits of human knowledge. This short introduction to this problem looks at the restrictions of measuring physical and social processes, and the limitations of algorithms.

  • Limitations of Measurement: The first limitation relates to the fact that our collection of data will always be an incomplete description of reality. In a physical system, choose which points to measure, at which frequency, by which method, and so on. We need to make a lot of...
Previous Section
End of Section 2
Next Section