Data Science and Prediction

The world's data is growing more than 40% annually. Coupled with exponentially growing computing horsepower, this provides us with unprecedented basis for 'learning' useful things from the data through statistical induction without material human intervention and acting on them. Philosophers have long debated the merits and demerits of induction as a scientific method, the latter being that conclusions are not guaranteed to be certain and that multiple and numerous models can be conjured to explain the observed data. I propose that 'big data' brings a new and important perspective to these problems in that it greatly ameliorates historical concerns about induction, especially if our primary objective is prediction as opposed to causal model identification. Equally significantly, it propels us into an era of automated decision making, where computers will make the bulk of decisions because it is infeasible or more costly for humans to do so. In this paper, I describe how scale, integration and most importantly, prediction will be distinguishing hallmarks in this coming era of Data Science.' In this brief monograph, I define this newly emerging field from business and research perspectives.NYU Stern School of Business, NYU Stern Center for Digital Economy Researc

Data Science and Prediction

The use of the term 'Data Science' is becoming increasingly common along with 'Big Data.' What does Data Science mean? Is there something unique about it? What skills should a 'data scientist' possess to be productive in the emerging digital age characterized by a deluge of data? What are the implications for business and for scientific inquiry? In this brief monograph I address these questions from a predictive modeling perspective.NYU Stern, IOMS Department, Center for Business Analytic

Application of data science to reduce employee attrition

Retaining valuable employees and preventing their resignation is a matter that can make a company save a considerable amount of time and money. Traditionally, this task had been carried out by the Human Resources department of the companies, who would regularly conduct interviews among the employees in order to subsequently analyse them and try to extract conclusions and patterns that could help them understand the reasons why employees leave and thus, prevent the resignation of other employees in the future. Nowadays, with the existence of Data Science and prediction techniques, this task can be automatically done, which allows the managers of the companies to obtain the information they require from the employees in a much faster and efficient way than it was obtained in the past when the task was done manually by the Human Resources department. This results in a significant decrease of the costs associated with employee attrition, maximizing the revenue of the company.Ingeniería Telemátic

A Data Science Course for Undergraduates: Thinking with Data

Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly sophisticated array of data available in many settings. These data tend to be non-traditional, in the sense that they are often live, large, complex, and/or messy. A first course in statistics at the undergraduate level typically introduces students with a variety of techniques to analyze small, neat, and clean data sets. However, whether they pursue more formal training in statistics or not, many of these students will end up working with data that is considerably more complex, and will need facility with statistical computing techniques. More importantly, these students require a framework for thinking structurally about data. We describe an undergraduate course in a liberal arts environment that provides students with the tools necessary to apply data science. The course emphasizes modern, practical, and useful skills that cover the full data analysis spectrum, from asking an interesting question to acquiring, managing, manipulating, processing, querying, analyzing, and visualizing data, as well communicating findings in written, graphical, and oral forms.Comment: 21 pages total including supplementary material

Data driven wireless network design : a multi-level modeling approach

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