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Principles of Data Science
Principles of Data Science

Principles of Data Science: Understand, analyze, and predict data using Machine Learning concepts and tools , Second Edition

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Profile Icon Sinan Ozdemir Profile Icon Tibaldeschi Profile Icon Kakade
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Arrow left icon
Profile Icon Sinan Ozdemir Profile Icon Tibaldeschi Profile Icon Kakade
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Paperback Dec 2018 424 pages 2nd Edition
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Principles of Data Science

Chapter 2. Types of Data

Now that we have had a basic introduction to the world of data science and understand why the field is so important, let's take a look at the various ways in which data can be formed. Specifically, in this chapter, we will look at the following topics:

  • 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. The aim of this chapter is to familiarize ourselves with the fundamental ideas underpinning data science.

Flavors of data

In the field, it is important to understand the different flavors of data for several reasons. Not only will the type of data dictate the methods used to analyze and extract results, but knowing whether the data is unstructured, or perhaps quantitative, can also tell you a lot about the real-world phenomenon being measured.

The first thing to note is my use of the word data. In the last chapter, I defined data as merely being 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 we go through this chapter is that for the most part, when I talk about the type of data, I will refer to either a specific characteristic of a dataset or to the entire dataset as a whole. I will be very clear about which one I refer to at any given time.

Why look at these distinctions?

It might seem worthless to stop and think about what type of data we have before getting into the fun stuff, such as statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

The same principle applies to data science. When given a dataset, it is tempting to jump right into exploring, applying statistical models, and researching the applications of machine learning in order to get results faster. However, if you don't understand the type of data that you are working with, then you might waste a lot of time applying models that are known to be ineffective with that specific type of data.

When given a new dataset, I always recommend taking about an hour (usually less) to make the distinctions mentioned in the following sections.

Structured versus unstructured data

The distinction between structured and unstructured data is usually the first question you want to ask yourself about the entire dataset. The answer to this question can mean the difference between needing three days or three weeks of time to perform a proper analysis.

The basic breakdown is as follows (this is a rehashed definition of organized and unorganized data in the first chapter):

  • Structured (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).
  • Unstructured (unorganized) data: This data exists as a free entity and does not follow any standard organization hierarchy.

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 posts, is unstructured
  • Scientific observations, as recorded by careful scientists, are kept in a very neat and organized (structured) format
  • A...

Quantitative versus qualitative data

When you ask a data scientist, "what type of data is this?", they will usually assume that you are asking them whether or not it is mostly quantitative or qualitative. It is likely the most common way of describing the specific characteristics of a dataset.

For the most part, when talking about quantitative data, you are usually (not always) talking about a structured dataset with a strict row/column structure (because we don't assume unstructured data even has any characteristics). All the more reason why the pre-processing step is so important.

These two data types can be defined as follows:

  • Quantitative data: This data can be described using numbers, and basic mathematical procedures, including addition, are possible on the set.
  • Qualitative data: This data cannot be described using numbers and basic mathematics. This data is generally thought of as being described using natural categories and language.

Example – coffee shop data

Say...

The road thus far

So far in this chapter, we have looked at the differences between structured and unstructured data, as well as between qualitative and quantitative characteristics. These two simple distinctions can have drastic effects on the analysis that is performed. Allow me to summarize before moving on the second half of the chapter.

Data as a whole can either be structured or unstructured, meaning that the data can either take on an organized row/column structure with distinct features that describe each row of the dataset, or exist in a free-form state that usually must be pre-processed into a form that is easily digestible.

If data is structured, we can look at each column (feature) of the dataset as being either quantitative or qualitative. Basically, can the column be described using mathematics and numbers, or not? The next part of this chapter will break down data into four very specific and detailed levels. At each order, we will apply more complicated rules of mathematics...

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. The 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 a 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 that if you are a chimpanzee, then you belong to the mammalian...

Data is in the eye of the beholder

It is possible to impose structure on data. For example, while I said that you technically cannot use a mean for the one to five data at the ordinal scale, many statisticians would not have a problem using this number as a descriptor of the dataset.

The level at which you are interpreting data is a huge assumption that should be made at the beginning of any analysis. If you are looking at data that is generally thought of at the ordinal level and applying tools such as the arithmetic mean and standard deviation, this is something that data scientists must be aware of. This is mainly because if you continue to hold these assumptions as valid in your analysis, you may encounter problems. For example, if you also assume divisibility at the ordinal level by mistake, you are imposing a structure where the structure may not exist.

Summary

The type of data that you are working with is a very large piece of data science. It must precede most of your analysis because the type of data you have impacts the type of analysis that is even possible!

Whenever you are faced with a new dataset, the first three questions you should ask about it are the following:

  • Is the data organized or unorganized? For example, does our data exist in a nice, clean row/column structure?
  • Is each column quantitative or qualitative? For example, are the values numbers, strings, or do they represent quantities?
  • At what level is the data in each column? For example, are the values at the nominal, ordinal, interval, or ratio level?

The answers to these questions will not only impact your knowledge of the data at the end but will also dictate the next steps of your analysis. They will dictate the types of graphs you are able to use and how you interpret them in your upcoming data models. Sometimes, we will have to convert from one level to another in order...

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Key benefits

  • Enhance your knowledge of coding with the theory for practical insight in data science and analysis
  • More than just a math class; you’ll perform real-world data science tasks using Python
  • Get the best insights and transform your data to get tangible value out of it

Description

Need to turn programming skills into effective data science skills? This book helps you connect mathematics, programming, and business analysis. You’ll feel confident asking—and answering—complex, sophisticated questions of your data, making abstract and raw statistics into actionable ideas. Going through the data science pipeline, you'll clean and prepare data and learn effective data mining strategies and techniques to gain a comprehensive view of how the data science puzzle fits together. You’ll learn fundamentals of computational mathematics and statistics and pseudo-code used by data scientists and analysts. You’ll learn machine learning, discovering statistical models that help control and navigate even the densest datasets, and learn powerful visualizations that communicate what your data means.

Who is this book for?

If you are an aspiring data scientist who wants to take your first steps in data science, this book is for you. If you have the basic math skills but want to apply them in data science, or you have good programming skills but lack the necessary math, this book will also help you. Some knowledge of Python programming will also help.

What you will learn

  • Understand five most important steps of data science
  • Use your data intelligently and learn how to handle it with care
  • Bridge the gap between mathematics and programming
  • Drive actionable results and clean your data using statistical models, calculus, and probability
  • Build and evaluate baseline machine learning models
  • Explore effective metrics to determine the success of your machine learning models
  • Create data visualizations that communicate actionable insights
  • Apply machine learning concepts to your problems and make actual predictions
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Table of Contents

16 Chapters
1. How to Sound Like a Data Scientist Chevron down icon Chevron up icon
2. Types of Data Chevron down icon Chevron up icon
3. The Five Steps of Data Science Chevron down icon Chevron up icon
4. Basic Mathematics Chevron down icon Chevron up icon
5. Impossible or Improbable - A Gentle Introduction to Probability Chevron down icon Chevron up icon
6. Advanced Probability Chevron down icon Chevron up icon
7. Basic Statistics Chevron down icon Chevron up icon
8. Advanced Statistics Chevron down icon Chevron up icon
9. Communicating Data Chevron down icon Chevron up icon
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials Chevron down icon Chevron up icon
11. Predictions Don't Grow on Trees - or Do They? Chevron down icon Chevron up icon
12. Beyond the Essentials Chevron down icon Chevron up icon
13. Case Studies Chevron down icon Chevron up icon
14. Building Machine Learning Models with Azure Databricks and Azure Machine Learning service Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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