Mathematics 2

This section includes data science and analytics as interdisciplinary fields that use scientific methods, processes, algorithms, and systems to extract knowledge and insights from unstructured and structured data. The knowledge gained from data science and subsequent analytics may be highly focused or wide-ranging, crossing multiple disciplines and informational domain inquiries. This science is often associated with data mining and machine learning activities.

Data science may be referred to as a process of unifying statistics, data analysis, and informatics, given the application of specific empirical methods, trials, and processes of investigation to develop a clearer understanding of a phenomenon using data that represents a series of measured or estimated sample outcomes or data.  

The science uses techniques and theories drawn from many fields within mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. For example, Turing Award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational, and data-driven). Gray added that "everything about science is changing because of the impact of information technology" and the data deluge.

This section also covers analytics, representing an intergal component of data science specific to the systematic computational analysis or assessment of data and statistical outcomes. Analytics is used to discover, interpret, and communicate meaningful patterns in data. It also entails applying data patterns toward effective decision-making. The process is valued in areas that are rich with recorded or historical information. Analytics relies on the simultaneous application of mathematics, statistics, computer programming, and scripted or coded operations to assist in research that seeks to identify, qualify, and quantify performance. Data science and analytics can be deployed as tools in mixed-method studies that apply qualitative and quantitative analysis approaches. 

Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to various fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (as with big data), the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.

Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data. Data analytics is often used to formulate more significant organizational decisions.

Finally, not unlike data science, data analytics is a multidisciplinary field. The practice of data analytics involves the extensive use of computer skills, mathematics, statistics, descriptive techniques, and predictive models to gain valuable knowledge from data through analytics. Advanced analytics, typically used to describe the technical aspects of analytics, is rapidly emerging as the use of machine learning techniques ever increases. This combination of sciences includes the adaptation of neural networks, decision trees, logistic regression, linear to multiple regression analysis, and descriptive classifications to do predictive modeling. This process also includes unsupervised machine learning techniques like cluster analysis, Principal Component Analysis, segmentation profile analysis, and association analysis.