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Home > Data > Data Science > Principles of Data Science
Principles of Data Science
Principles of Data Science

Principles of Data Science: Mathematical techniques and theory to succeed in data-driven industries

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Principles of Data Science

Chapter 2. Types of Data

Now that we have 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. This chapter is aimed to familiarize ourselves with the fundamental ideas underlying 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, knowing whether the data is unstructured or perhaps quantitative can also tell you a lot about the real-world phenomenon being measured.

We will look at the three basic classifications of data:

  • Structured vs unstructured (sometimes called organized vs unorganized)
  • Quantitative vs qualitative
  • The four levels of data

The first thing to pay attention to 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 what type of data this is, I will refer to either a specific characteristic of a dataset or to the entire dataset as a...

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, like statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

Consider an example where we are looking at election results for a county. In the dataset of people, there is a "race" column that is denoted via an identifying number to save space. For example perhaps caucasian is denoted by 7 while Asian American is 2. Without understanding that these numbers are not actually ordered numbers like we think about them (where 7 is greater than 2 and therefore caucasian is "greater than" Asian American) we will make terrible mistakes in our analysis.Discuss

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...

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 preprocessing 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...

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 preprocessed 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...

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, knowing whether the data is unstructured or perhaps quantitative can also tell you a lot about the real-world phenomenon being measured.

We will look at the three basic classifications of data:

  • Structured vs unstructured (sometimes called organized vs unorganized)

  • Quantitative vs qualitative

  • The four levels of data

The first thing to pay attention to 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 what type of data this is, I will refer to either a specific characteristic of a dataset or to the entire dataset as a whole...

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, like statistics and machine learning, but this is arguably one of the most important steps you need to take to perform data science.

Consider an example where we are looking at election results for a county. In the dataset of people, there is a "race" column that is denoted via an identifying number to save space. For example perhaps caucasian is denoted by 7 while Asian American is 2. Without understanding that these numbers are not actually ordered numbers like we think about them (where 7 is greater than 2 and therefore caucasian is "greater than" Asian American) we will make terrible mistakes in our analysis.Discuss

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...

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 genetic...

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Key benefits

  • Enhance your knowledge of coding with data science theory for practical insight into data science and analysis
  • More than just a math class, learn how to perform real-world data science tasks with R and Python
  • Create actionable insights and transform raw data into tangible value

Description

Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you’ll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you’ll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You’ll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means.

Who is this book for?

You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you.

What you will learn

  • Get to know the 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
  • Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results
  • Build and evaluate baseline machine learning models
  • Explore the most effective metrics to determine the success of your machine learning models
  • Create data visualizations that communicate actionable insights
  • Read and apply machine learning concepts to your problems and make actual predictions

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Publication date : Dec 16, 2016
Length: 388 pages
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Language : English
ISBN-13 : 9781785888922
Category :
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Languages :
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Concepts :
Data Science

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Publication date : Dec 16, 2016
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ISBN-13 : 9781785888922
Category :
Data
Languages :
Python
Concepts :
Data Science

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Table of Contents

14 Chapters
1. How to Sound Like a Data Scientist Chevron down icon Chevron up icon
1. How to Sound Like a Data Scientist
What is data science?
The data science Venn diagram
Some more terminology
Data science case studies
Summary
2. Types of Data Chevron down icon Chevron up icon
2. Types of Data
Flavors of data
Why look at these distinctions?
Structured versus unstructured data
Quantitative versus qualitative data
The road thus far…
The four levels of data
Data is in the eye of the beholder
Summary
3. The Five Steps of Data Science Chevron down icon Chevron up icon
3. The Five Steps of Data Science
Introduction to data science
Overview of the five steps
Explore the data
Summary
4. Basic Mathematics Chevron down icon Chevron up icon
4. Basic Mathematics
Mathematics as a discipline
Basic symbols and terminology
Linear algebra
Summary
5. Impossible or Improbable – A Gentle Introduction to Probability Chevron down icon Chevron up icon
5. Impossible or Improbable – A Gentle Introduction to Probability
Basic definitions
Probability
Bayesian versus Frequentist
Compound events
Conditional probability
The rules of probability
A bit deeper
Summary
6. Advanced Probability Chevron down icon Chevron up icon
6. Advanced Probability
Collectively exhaustive events
Bayesian ideas revisited
Random variables
Summary
7. Basic Statistics Chevron down icon Chevron up icon
7. Basic Statistics
What are statistics?
How do we obtain and sample data?
Sampling data
How do we measure statistics?
The Empirical rule
Summary
8. Advanced Statistics Chevron down icon Chevron up icon
8. Advanced Statistics
Point estimates
Sampling distributions
Confidence intervals
Hypothesis tests
Summary
9. Communicating Data Chevron down icon Chevron up icon
9. Communicating Data
Why does communication matter?
Identifying effective and ineffective visualizations
When graphs and statistics lie
Verbal communication
The why/how/what strategy of presenting
Summary
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials Chevron down icon Chevron up icon
10. How to Tell If Your Toaster Is Learning – Machine Learning Essentials
What is machine learning?
Machine learning isn't perfect
How does machine learning work?
Types of machine learning
How does statistical modeling fit into all of this?
Linear regression
Logistic regression
Probability, odds, and log odds
Dummy variables
Summary
11. Predictions Don't Grow on Trees – or Do They? Chevron down icon Chevron up icon
11. Predictions Don't Grow on Trees – or Do They?
Naïve Bayes classification
Decision trees
Unsupervised learning
K-means clustering
Choosing an optimal number for K and cluster validation
Feature extraction and principal component analysis
Summary
12. Beyond the Essentials Chevron down icon Chevron up icon
12. Beyond the Essentials
The bias variance tradeoff
K folds cross-validation
Grid searching
Ensembling techniques
Neural networks
Summary
13. Case Studies Chevron down icon Chevron up icon
13. Case Studies
Case study 1 – predicting stock prices based on social media
Case study 2 – why do some people cheat on their spouses?
Case study 3 – using tensorflow
Summary
Index Chevron down icon Chevron up icon
Index

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Amazon Customer Jan 06, 2017
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I was lucky to attend classroom Data Science course with Sinan Ozdemir. Sinan is very good at explain really complex mathematical concepts in a very approachable and everyday terms. This really helps people like myself who aren't in touch with theoretical mathematics. This book is written in the same tone that Sinan delivers lectures and that is very valuable in absorbing the complex concepts delivered in this book. The examples and datasets are carefully chosen to deliver specific concepts provide reference for future use. I would recommend this book to anybody with or without formal training in statistics but would like to pick up on the breadth of Data Science. I would also recommend this book to Data Science instructors as an ideal for organization and flow of training on Data Science. This book is the best Data Science book on the market that can introduce you to the world of Data Science and prepare you for interviews.
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Dr Rashmi Yogendra Dhote Jul 09, 2019
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The quality of the product I ordered is up to the mark
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willmidwest Jan 19, 2019
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Preparing to enter an advanced degree program. This book is Foundational to beginning that journey!
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Suresh Deshpande Sep 20, 2017
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Wonderful big with simple explanation
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Amazon Customer Aug 05, 2020
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Author really has grip on mathematical tools applied for data analytics. Good books for folks like me who did not consider mathematics seriously in engineering
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