Data Science and Machine Learning

Data Science

Data science is a field of study that uses scientific methods and processes, algorithms, and systems to extract knowledge from noisy, structured, and unstructured data in order to apply knowledge from data across a broad range of application domains. It is closely related to data mining, machine learning, and big data analysis. Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyze actual phenomena" with data. It uses techniques drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Data scientists are concerned with the development of theories that relate to different types of data sets measured on different scales or levels; they combine skills from various fields including biology and medicine with those traditionally associated with computer science or mathematics such as statistical modeling, and pattern recognition or machine learning algorithms. However, data science is different from computer science and information science because it focuses on understanding real-world phenomena using empirical evidence instead of through experimentation.

Foundations

Data science is an interdisciplinary field that focuses on the analysis of data and its application to a variety of problems. The field encompasses preparing data for analysis, formulating data science problems, analyzing data, developing data-driven solutions, and presenting findings to inform high-level decisions in a broad range of application domains. In 2015, the American Statistical Association identified database management, statistics, machine learning, and distributed and parallel systems as the three emerging foundational professional communities.

Relationship to statistics

The field of data science is considered by some to be a new field, while others believe that it is a continuation of the field of statistics. There are also those who argue that data science should not be seen as a branch of either statistics or computer science because it focuses on problems and techniques unique to digital data. Vasant Dhar writes that statistics emphasizes quantitative data and description. In contrast, data science deals with quantitative and qualitative data and emphasizes prediction and action. Andrew Gelman of Columbia University has described statistics as a nonessential part of data science. Stanford professor David Donoho writes that data science is not distinguished from statistics by the size of datasets or use of computing and that many graduate programs misleadingly advertise their analytics and statistics training as the essence of a data science program. He describes data science as an applied field growing out of traditional statistics.

Machine learning

Machine learning is a field of artificial intelligence devoted to understanding and building algorithms that can learn from data. These algorithms use sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine and speech recognition, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is a type of artificial intelligence that focuses on making predictions using computers. Machine learning is closely related to computational statistics, which focuses on making predictions using computers but is not all machine learning. The study of mathematical optimization delivers methods, theory, and application domains to the field of machine learning. Data mining is a related field of study focusing on exploratory data analysis through unsupervised learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its application across business problems, machine learning is also referred to as predictive analytics.