Big Data


Growth of and digitization of global information-storage capacity[1]
Big data is a term used to refer to data sets that are too large or complex for traditional data-processingapplication software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2]Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.[3] Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) [4] and value.[5]
Current usage of the term "big data" tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[6] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[7] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[8]connectomics, complex physics simulations, biology and environmental research.[9]
Data sets grow rapidly- in part because they are increasingly gathered by cheap and numerous information- sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[10][11] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[12] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[13] Based on an IDC report prediction, the global data volume will grow exponentially from 4.4 zettabytes to 44 zettabytes between 2013 and 2020.[14] By 2025, IDC predicts there will be 163 zettabytes of data.[15] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[16]
Relational database management systems, desktop statistics[clarification needed] and software packages used to visualize data often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[17] What qualifies as being "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."[18]

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Natural science

The natural sciences seek to understand how the world and universe around us works. There are five major branches (top left to bottom right): Chemistry, astronomy, earth science, physics, and biology.

























Natural science is a branch of science concerned with the description, prediction, and understanding of natural phenomena, based on empirical evidence from observation and experimentation. Mechanisms such as peer review and repeatability of findings are used to try to ensure the validity of scientific advances.

Natural science can be divided into two main branches: life science (or biological science) and physical science. Physical science is subdivided into branches, including physics, chemistry, astronomy and earth science. These branches of natural science may be further divided into more specialized branches (also known as fields).

In Western society's analytic tradition, the empirical sciences and especially natural sciences use tools from formal sciences, such as mathematics and logic, converting information about nature into measurements which can be explained as clear statements of the "laws of nature". The social sciences also use such methods, but rely more on qualitative research, so that they are sometimes called "soft science", whereas natural sciences, insofar as they emphasize quantifiable data produced, tested, and confirmed through the scientific method, are sometimes called "hard science".[1]

Modern natural science succeeded more classical approaches to natural philosophy, usually traced to ancient Greece. Galileo, Descartes, Bacon, and Newton debated the benefits of using approaches which were more mathematical and more experimental in a methodical way. Still, philosophical perspectives, conjectures, and presuppositions, often overlooked, remain necessary in natural science.[2] Systematic data collection, including discovery science, succeeded natural history, which emerged in the 16th century by describing and classifying plants, animals, minerals, and so on.[3]Today, "natural history" suggests observational descriptions aimed at popular audiences.[4]

Applied science

pplied science is the application of existing scientific knowledge to practical applications, like technology or inventions.

Image result for Applied science

Within natural science, disciplines that are basic science, also called pure science, develop basic information to predict and perhaps explain and understand phenomena in the natural world. Applied science is the use of scientific processes and knowledge as the means to achieve a particular practical or useful result. This includes a broad range of applied science related fields from engineering, business, medicine to early childhood education.


Applied science can also apply formal science, such as statistics and probability theory, as in epidemiology. Genetic epidemiology is an applied science applying both biological and statistical methods.


Health sciences

Health sciences – are those sciences which focus on health, or health care, as core parts of their subject matter. Because these two subject matter relate to multiple academic disciplines, both STEM disciplines as well as emerging patient safety disciplines (such as social care research) are relevant to current health scientific knowledge.


Image result for health sciences

Health sciences knowledge bases are currently diverse, with intellectual foundations which are sometimes mutually-inconsistent. There is currently an existing bias in the field, towards high valuation of knowledge deriving from controlling views on human agency (as epitomized by the epistemological basis of Randomized Control Trial designs); compare this against the more naturalistic views on human agency taken by research based on Ethnography for example).

Next Generation Science

Artificial Intelligence / Robotics / Big Data / Personalized Medicine / Synthtic Biology / DNA Computing Follw as Next Generation Science.

Artificial Intelligence:
Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the naturalintelligence displayed by humans and other animals. In computer science AI research is defined as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solvingRelated image

Robotics:

Robotics is an interdisciplinary branch of engineering and science that includes mechanical engineering, electronic engineering, information engineering, computer science, and others. Robotics deals with the design, construction, operation, and use of robots, as well as computer systems for their control, sensory feedback, and information processing.

Big data:

Big data is a term used to refer to data sets that are too large or complex for traditional data-processingapplication software to adequately deal with. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.[2]Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity.[3] Other concepts later attributed with big data are veracity (i.e., how much noise is in the data) [4] and value.

Personalized medicine:

Personalized medicine, precision medicine, or theranostics is a medical model that separates people into different groups—with medical decisions, practices, interventions and/or products being tailored to the individual patient based on their predicted response or risk of disease.[1] The terms personalized medicine, precision medicine, stratified medicine and P4 medicine are used interchangeably to describe this concept[1][2] though some authors and organisations use these expressions separately to indicate particular nuances.


Synthetic biology:


Synthetic biology is an interdisciplinary branch of biology and engineering.

The subject combines disciplines from within these domains, such as biotechnology, genetic engineering, molecular biology, molecular engineering, systems biology, membrane science, biophysics, chemical and biological engineering, electrical and computer engineering, control engineering and evolutionary biology. Synthetic biology applies these disciplines to build artificial biological systems for research, engineering and medical applications.



DNA computing:


DNA computing is a branch of computing which uses DNA, biochemistry, and molecular biology hardware, instead of the traditional silicon-based computer technologies. Research and development in this area concerns theory, experiments, and applications of DNA computing. The term "molectronics" has sometimes been used, but this term had already been used for an earlier technology, a then-unsuccessful rival of the first integrated circuits;[1] this term has also been used more generally, for molecular-scale electronic technology.