Data Science

data infrastructure, which include the use of in-database machine learning, the use of Hadoop for data storage and processing, and the development of hybrid data systems that seamlessly combine traditional database software and Hadoop-like solutions. The chapter concludes by highlighting some of the challenges in integrating data from across an organization into a unified representation that is suitable for machine learning. Chapter 4 introduces the field of machine learning and explains some of the most popular machine learning algorithms and models, including neural networks, deep learning, and decision tree models. Chapter 5 focuses on linking machine learning expertise with real-world problems by reviewing a range of standard business problems and describing how they can be solved by machine learning solutions. Chapter 6 reviews the ethical implications of data science, recent developments in data regulation and some of the new computational approaches to preserving the privacy of individuals within the data science process. Finally, Chapter 7 describes some of the areas where data science will have a significant impact in the near future and sets out some of the principles that are important in determining whether a data science project will succeed. Chapter 1 What is Data Science? Data science encompasses a set of principles, problems, definitions, algorithms, and processes for extracting non-obvious and useful patterns from large data sets. Many of the elements of data science have been developed in related fields such as machine learning and data mining. In fact, the terms data science, machine learning, and data mining are often used interchangeably. The commonality across these disciplines is a focus on improving decision-making through the analysis of data. However, although data science borrows from these other fields, it is broader in scope. Machine learning, ML, focuses on the design and evaluation of algorithms for extracting patterns from data. Data mining generally deals with the analysis of structured data and often implies an emphasis on commercial applications. Data science takes all of these considerations into account but also takes up other challenges such as the capturing, cleaning, and transforming of unstructured social media and web data, the use of big data technologies to store and process big unstructured data sets, and questions related to data ethics and regulation. Using data science, we can extract different types of patterns. For example, we might want to extract patterns that help us to identify groups of customers exhibiting similar behavior and tastes. In business jargon, this task is known as customer segmentation, and in data science terminology it is called clustering. Alternatively, we might want to extract a pattern that identifies products that are frequently bought together, a process called association rule mining. Or we might want to extract patterns that identify strange or abnormal events, such as fraudulent insurance claims, a process known as anomaly or outlier detection. Finally, we might want to identify patterns that help us to classify things. For example, the following rule illustrates what a classification pattern extracted from an email data set might look like. If an email contains the phrase, make money easily, it is likely to be a spam email. Identifying these types of classification rules is known as prediction. The word prediction might seem an odd choice because the rule doesn't predict what will happen in the future. The email already is or isn't a spam email. So it is best to think of prediction patterns as predicting the missing value of an attribute rather than as predicting the future. In this example, we are predicting whether the email classification attribute should have the value spam or not. Although we can use data science to extract different types of patterns, we always want the patterns to be both non-obvious and useful. The example email classification rule given earlier is so simple and obvious that if it were the only rule extracted by a data science process, we would be disappointed. For example, this email classification rule checks only one attribute of an email.