Data science encompasses a set of principles, problem definitions, algorithms, and processes for ... more Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious 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.
Data science is best understood as a partnership between a data scientist and a computer. In chap... more Data science is best understood as a partnership between a data scientist and a computer. In chapter 2, we described the process the data scientist follows: the CRISP-DM life cycle. CRISP-DM defines a sequence of decisions the data scientist has to make and the activities he should engage in to inform and implement these decisions. In CRISP-DM, the major tasks for a data scientist are to define the problem, design the data set, prepare the data, decide on the type of data analysis to apply, and evaluate and interpret the results of the data analysis. What the computer brings to this partnership is the ability to process data and search for patterns in the data. Machine learning is the field of study that develops the algorithms that the computers follow in order to identify and extract patterns from data. ML algorithms and techniques are applied primarily during the modeling stage of CRISP-DM. ML involves a two-step process.
Data science encompasses a set of principles, problem definitions, algorithms, and processes for ... more Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious 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.
Data science is best understood as a partnership between a data scientist and a computer. In chap... more Data science is best understood as a partnership between a data scientist and a computer. In chapter 2, we described the process the data scientist follows: the CRISP-DM life cycle. CRISP-DM defines a sequence of decisions the data scientist has to make and the activities he should engage in to inform and implement these decisions. In CRISP-DM, the major tasks for a data scientist are to define the problem, design the data set, prepare the data, decide on the type of data analysis to apply, and evaluate and interpret the results of the data analysis. What the computer brings to this partnership is the ability to process data and search for patterns in the data. Machine learning is the field of study that develops the algorithms that the computers follow in order to identify and extract patterns from data. ML algorithms and techniques are applied primarily during the modeling stage of CRISP-DM. ML involves a two-step process.
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