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Principles of Data Science
Principles of Data Science

Principles of Data Science: A beginner's guide to essential math and coding skills for data fluency and machine learning , Third Edition

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Principles of Data Science

Types of Data

For our first step into the world of data science, let’s take a look at the various ways in which data can be formed. In this chapter, we will explore three critical categorizations of data:

  • Structured versus unstructured data
  • Quantitative versus qualitative data
  • The four levels of data

We will dive further into each of these topics by showing examples of how data scientists look at and work with data. This chapter aims to familiarize us with the fundamental types of data so that when we eventually see our first dataset, we will know exactly how to dissect, diagnose, and analyze the contents to maximize our insight value and machine learning performance.

The first thing to note is my use of the word data. In the previous chapter, I defined data as merely a collection of information. This vague definition exists because we may separate data into different categories and need our definition to be loose.

The next thing to remember while...

Structured versus unstructured data

The first question we want to ask ourselves about an entire dataset is whether we are working with structured or unstructured data. The answer to this question can mean the difference between needing three days or three weeks to perform a proper analysis.

The basic breakdown is as follows (this is a rehashed definition of organized and unorganized data from Chapter 1):

  • Structured (that is, organized) data: This is data that can be thought of as observations and characteristics. It is usually organized using a table method (rows and columns) that can be organized in a spreadsheet format or a relational database.
  • Unstructured (that is, unorganized) data: This data exists as a free entity and does not follow any standard organization hierarchy such as images, text, or videos.

Here are a few examples that could help you differentiate between the two:

  • Most data that exists in text form, including server logs and Facebook...

The four levels of data

It is generally understood that a specific characteristic (feature/column) of structured data can be broken down into one of four levels of data. These levels are as follows:

  • The nominal level
  • The ordinal level
  • The interval level
  • The ratio level

As we move down the list, we gain more structure and, therefore, more returns from our analysis. Each level comes with its own accepted practice in measuring the center of the data. We usually think of the mean/average as being an acceptable form of center.

However, this is only true for a specific type of data.

The nominal level

The first level of data, the nominal level, consists of data that is described purely by name or category. Basic examples include gender, nationality, species, or yeast strain in a beer. They are not described by numbers and are therefore qualitative. The following are some examples:

  • A type of animal is on the nominal level of data. We may also say...

Summary

This chapter has provided an overview of the crucial role data types play in data science, emphasizing the importance of understanding the nature of the data before commencing any analysis. We discussed the significance of asking three key questions when encountering a new dataset: whether the data is structured or unstructured, whether each column is quantitative or qualitative, and the level of data within each column (nominal, ordinal, interval, or ratio).

By completing this chapter, you should be able to identify the types of data they are working with and understand the implications of those data types on their analysis. This knowledge will help you select appropriate graphs, interpret results, and determine the next steps in the analytical process. You should also be familiar with the concept of converting data from one level to another to gain more insights.

With this knowledge, and with the ability to classify data as nominal or ordinal through various examples...

Questions and answers

For the following statements, classify them as ordinal or nominal:

  • The origin of the beans in your cup of coffee: Nominal
  • The place someone receives after completing a foot race: Ordinal
  • The metal used to make the medal that they receive after placing in the race: Nominal
  • The telephone number of a client: Nominal
  • How many cups of coffee you drink in a day: Ordinal
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Key benefits

  • Learn practical data science combined with data theory to gain maximum insights from data
  • Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models
  • Explore actionable case studies to put your new skills to use immediately
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Principles of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights. Starting with cleaning and preparation, you’ll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you’ll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data. With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You’ll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you’ll explore medium-level data governance, including data provenance, privacy, and deletion request handling. By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.

Who is this book for?

If you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you’ll find this book useful. Familiarity with Python programming will further enhance your learning experience.

What you will learn

  • Master the fundamentals steps of data science through practical examples
  • Bridge the gap between math and programming using advanced statistics and ML
  • Harness probability, calculus, and models for effective data control
  • Explore transformative modern ML with large language models
  • Evaluate ML success with impactful metrics and MLOps
  • Create compelling visuals that convey actionable insights
  • Quantify and mitigate biases in data and ML models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Jan 31, 2024
Length: 326 pages
Edition : 3rd
Language : English
ISBN-13 : 9781837636006
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Product Details

Publication date : Jan 31, 2024
Length: 326 pages
Edition : 3rd
Language : English
ISBN-13 : 9781837636006
Category :
Languages :
Concepts :

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Table of Contents

17 Chapters
Chapter 1: Data Science Terminology Chevron down icon Chevron up icon
Chapter 2: Types of Data Chevron down icon Chevron up icon
Chapter 3: The Five Steps of Data Science Chevron down icon Chevron up icon
Chapter 4: Basic Mathematics Chevron down icon Chevron up icon
Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability Chevron down icon Chevron up icon
Chapter 6: Advanced Probability Chevron down icon Chevron up icon
Chapter 7: What Are the Chances? An Introduction to Statistics Chevron down icon Chevron up icon
Chapter 8: Advanced Statistics Chevron down icon Chevron up icon
Chapter 9: Communicating Data Chevron down icon Chevron up icon
Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials Chevron down icon Chevron up icon
Chapter 11: Predictions Don’t Grow on Trees, or Do They? Chevron down icon Chevron up icon
Chapter 12: Introduction to Transfer Learning and Pre-Trained Models Chevron down icon Chevron up icon
Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift Chevron down icon Chevron up icon
Chapter 14: AI Governance Chevron down icon Chevron up icon
Chapter 15: Navigating Real-World Data Science Case Studies in Action Chevron down icon Chevron up icon
Index 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 Half star icon 4.8
(4 Ratings)
5 star 75%
4 star 25%
3 star 0%
2 star 0%
1 star 0%
O Feb 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Appreciate all the code examples and the up to date GitHub
Amazon Verified review Amazon
Steven Fernandes May 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a fantastic resource for anyone diving into data science. It effectively bridges the gap between theoretical math and practical programming through engaging examples and clear explanations. The sections on probability, calculus, and machine learning models are particularly enlightening. I especially appreciated the deep dive into modern machine learning techniques, including large language models, and the practical advice on using metrics and MLOps to evaluate ML projects. The guidance on creating visuals and addressing data biases is also invaluable. Highly recommend for those looking to enhance their data science skills!
Amazon Verified review Amazon
Om S May 09, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Principles of Data Science is a book that helps you understand data and use it to make decisions. It teaches you how to clean data, find important patterns, and make predictions using machine learning. The book covers basic to advanced topics, including how to handle biases and use big models like GPT and BERT. It's perfect for beginners who know some Python and want to get better at using data in real projects.
Amazon Verified review Amazon
H2N Mar 11, 2024
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is for new data scientists. Each chapter is well-structured, offering clear explanations and practical examples. The inclusion of Python code snippets helps the learning experience, making it easier to apply the concepts in real-world scenarios. However, the chapters on advanced statistics and probability may disappoint those expecting more depth, as they assume a level of knowledge common in the data science field. Overall, this book is a valuable resource for aspiring data scientists looking to build a solid foundation in the field.
Amazon Verified review Amazon
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