Data Analytics Terminologies pdf

DATA ANALYTICS
TERMINOLOGIES

Table of Contents
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
1.1 Common terminology in ‘data analytics’..............................................................................1
1.2 Critical evaluation of the use of data analytic methods........................................................2
1.3 Summarising the importance of data analytics for businesses..............................................4
TASK 2............................................................................................................................................6
2.1. Evaluate analytical model data preparation processes.........................................................6
2.2. Critically evaluating potential issues in the preparation of data for use in an applied
analytical model..........................................................................................................................7
TASK 3............................................................................................................................................7
3.1 Assessing methods to visualise the output from an applied analytical model......................7
3.2 Application of an appropriate programming language or tool to demonstrate how
descriptive analytic techniques contribute to decision-making..................................................7
3.3 Applying an appropriate programming language or tool to demonstrate how predictive
analytic techniques are used in forecasting future events...........................................................8
3.4 Employing an appropriate programming language or tool to demonstrate how prescriptive
analytic techniques are used to find the best course of action in a situation...............................9
CONCLUSION..............................................................................................................................10
REFERENCES..............................................................................................................................11

INTRODUCTION
In present-day scenario, the utilisation of machines and technologies have gained
prominence so much so that the organisations have become heavily reliant on its mechanisms for
deriving valuable insights in order to maximise their goals (Andrejevic and Gates, 2014). At its
core, this field is established on the principles of statistics which cater to the needs of
government and organisations alike. Data Analysis is crucial for the organisations as it facilitates
prompt and hassle-free solutions to a given set of challenges that arise either on a day-to-day
basis or occasionally. Today, this area has evolved in the form of Artificial or Business
Intelligence as well as Machine Learning thaty is helping companies worldwide to fulfil client
needs in a significant manner.
The following report aims to provide a detailed account on the terminology used in the
field of Data Analytics. Additionally, the given project report also includes various analysis such
as Exploratory, Predictive and Descriptive along with their application by way of employing a
certain programming language or tool. Also, various issues related to data preparation,
visualisation as well as assumptions regarding sampling and statistical inferences have been
discussed in detail.
TASK 1
1.1 Common terminology in ‘data analytics’
Essentially, the concept of 'data analytics' can be defined as a process of collecting and
examining a large pool of information so as to uncover hidden patterns which enable the
business enterprises to gain valuable insights regarding organisational operations. Through this,
the business manager is able to draw valuable inferences and gain insights in relation to a
particular problem or situation. Since its a broad term, this area of analytics is constituent of wide
array of elements. These have been explained as under:
 Population:
A pool of information which includes specific sets of variables that have similar characteristics
from which a statistical sample is drawn for the purpose of analysing and gaining insights
regarding a particular hypothesis (Chen, Chiang and Storey, 2012).
 Sample:
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It is a set of data which is chosen from a larger pool of information, usually known as
'population', that is representative of all the variables present in such information. It can said to
be a fraction of population which is chosen by a researcher for further analysis of given dataset.
 Categorical Data:
It includes a certain set of variables which are grouped in the form of multiple categories on the
basis of their characteristics or features such as race, sex and age group among others. Thus, it
requires the utilisation of data tables.
 Ordinal Data:
A type of quantitative, statistical as well as categorical data wherein the distance between the
variables is unknown even though such data is ordered as naturally occurring class. Hence, it is
usually found in the form of an order or scale (Gandomi and Haider, 2015). For instance,
measuring level of happiness on a scale of 1 to 10.
 Nominal Data:
A type of data which can neither be ordered nor be measured and is usually employed for the
purpose of labelling variables without giving them any actual quantitative unit of measurement.
For instance, male and female is a prominent example of Nominal Data.
 Continuous Data:
A set of infinite quantitative information, except for whole numbers, which can be measured as
well as subdivided in a meaningful manner. For instance, temperature, height and weight among
others.
 Discrete Data:
A type of finite statistical information which includes variables in the form of integers with a
possibility of categorization. Also, it is not achievable to subdivide such information
meaningfully and are usually in the form of numericals (Hazen and et.al., 2014).
1.2 Critical evaluation of the use of data analytic methods
With the advent of technologies in the form of Internet of Things (IoT) as well as
Integrated Communications, it is easy to retrieve information, either primary or secondary,
through content which is readily available online all around the world. However, in the language
of statistics, not all information retrieved is usable for the research purposes. Hence,
confirmation of relevancy, reliability and authenticity of such data collected is of paramount
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