Unit 4D: Laboratory Techniques and their
application
Understand how scientific information may be
stored and communicated in a workplace
laboratory
Nikki Moslares
BTEC Level 3 Applied Sciences
, In the context of scientific and medical industries, acquiring big data poses
great significance in their daily operation. The research that is done in getting
specific and confidential information has helped companies assist other clients or
patients. In enables development towards the modern world where innovation of
technology increases which requires huge data researching and information to
assist industries into creating an advance industry.
How scientific data is obtained from large data sets?
Scientific data is defined on the ways where information and knowledge are
taken from the data. Data is found anywhere and it can be established on large and
expanding quantities. Scientific data shows the technique where data was
discovered, prepared, extracted, assembled, processed, examined, explained,
modeled, envisioned, disclosed and presented despite the size of data getting
processed. When it comes to scientific data, it usually integrates with statistics,
mathematics, programming, computer science, artificial intelligence and many
more. It is then greatly suited to bounteous fields such as medicine, social sciences,
social media, finance, economics and so on. A particular application of scientific
data is what is known as big data.
Big data, as it is described ‘’big’, this is a specialized application of scientific data
wherein the data sets tend to be humongous and demands overwhelming
operational challenges in handling them. The primary consideration when it comes
to big data is handling and managing information. The process and analysis of the
huge data sets are usually not attainable due to computational and physical
restrictions. Special methods and tools such as parallel programming, algorithms,
etc.) are then required.
In order to obtain and use scientific data from the big data, it is firstly collected,
pre-processed and stored. After the process is done, then the data can be processed,
specified, evaluated and worked with in manufacturing models that will be
predictive and descriptive. Descriptive statistics is a term that will provide
description of the application of statistics to a data set which will interpret and
recap the information it contains. This basically includes data description in
context of classification which includes a mean, median, mode, standard deviation,
etc... While inferential statistics and data modeling are more dynamic tools that is
used in obtaining a deeper understanding of data, at the same time conclude results
and meaning on conditions outside the collected data. With the use of various
methods, prototypes and resolutions can be made effectively based on the data
