Analyse Data BSBDAT501 - Knowledge Questionnaire and Techniques for Data Analysis

This assessment task is a written questionnaire with a mix of objective and subjective questions designed to meet the knowledge required to meet the unit requirements safely and effectively. It covers key details of datasets and techniques for synthesising data, organisational policies and procedures relating to accessing information, recording and reporting outcomes of analysis, requirement for data analysis, key features of industry standards and techniques relating to data analysis, potential data sources and factors that impact on reliability of data, methods of data analysis, statistical analysis, and key legislative requirements relating to data analysis.

Assessment
Task 1
Analyse data
BSBDAT501
Student Declaration
To be filled out and submitted with assessment responses
◻ I declare that this task and any attached document related to the task is all my own work and I
have not cheated or plagiarised the work or colluded with any other student(s)

◻ I understand that if I am found to have plagiarised, cheated or colluded, action will be taken
against me according to the process explained to me
◻ I have correctly referenced all resources and reference texts throughout these assessment
tasks.
◻ I have read and understood the assessment requirements for this unit
◻ I understand the rights to re-assessment
◻ I understand the right to appeal the decisions made in the assessment
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Question Marking Sheet - Assessor to complete.
Did the student satisfactorily address each question as
instructed:
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Comments
Question 1
Question 2
Question 3
Question 4
Question 5
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Table of Content
Student Declaration 2
Task 1 – Knowledge Questionnaire 5
Question 1 6
Question 2 7
Question 3 7
Question 4 8
Question 5 8

Task 1 – Knowledge Questionnaire
Task summary and instructions
What is this
assessment task
about?
This assessment is a written questionnaire with a mix of
objective and subjective questions.
The questionnaire is designed to meet the knowledge
required to meet the unit requirements safely and
effectively.
The questions focus on the knowledge evidence required for
this unit of competency:
 key details of datasets and techniques for synthesising
data
 organisational policies and procedures relating to:
o accessing information
o recording and reporting outcomes of analysis
o requirement for data analysis
 key features of industry standards and techniques
relating to data analysis
 potential data sources and factors that impact on
reliability of data, including timeliness, authority,
audience, relevance and potential for bias
 importance and value of data analysis
 methods of data analysis
 statistical analysis
 key legislative requirements relating to data analysis
 methods of reporting analysis.
Your assessor will be looking for demonstrated evidence of
your ability to answer the questions satisfactorily, follow
instructions, conduct online research and review real or
simulated business documentation as instructed.
What do I need to
do to complete this
task satisfactorily?
 submit your answers to the questions within the set
timeframe,
 answer all questions as instructed,
 answer all questions using your own words and reference
any sources appropriately,
 all questions must be answered satisfactorily.
It is advisable to:
 review the questions carefully,

Task summary and instructions
 answer the questions using online research and the
learning material provided for the unit and by reviewing
real or simulated relevant business documentation (such
as policies and procedures),
 further research the topics addressed in each question.
Specifications You must submit to GOALS the
 assessment coversheet,
 answers to all questions,
 references.
Resources and
equipment
• computer with Internet access,
• access to Microsoft Office suites or similar software,
• learning material.
Re-submission
opportunities
You will be provided feedback on your performance by the
Assessor. The feedback will indicate if you have satisfactorily
addressed the requirements of each part of this task. If any
parts of the task are not satisfactorily completed, the
assessor will explain why, and provide you with written
feedback along with guidance on what you must undertake
to demonstrate satisfactory performance. Re-assessment
attempt(s) will be arranged at a later time and date. You
have the right to appeal the outcome of assessment
decisions if you feel that you have been dealt with unfairly or
have other appropriate grounds for an appeal. You are
encouraged to consult with the assessor prior to attempting
this task if you do not understand any part of this task or if
you have any learning issues or needs that may hinder you
when attempting any part of the task.
Answer all the questions below:
Question 1
Answer the following questions.
Question Answer
Define data. Data can be generally defined as numeric element of
information that has been gathered through observation (Bansal
and Srivastava, 2018). However, data can be related to both

Question Answer
quantitative and qualitative variables associated with an object
or person.
What is a data set? What are
the key details of datasets?
Mention two examples as
part of your answer.
(50-100 words)
Data set is a gathered data or it can be defined as a gathered
values and numbers associated with a particular subject.
Key details of data set are as follows:
 The set of data so gathered has been presented in a
tabular form.
 The two components of table indicating data set are rows
and columns.
 To analysis the characteristics of data set, various
statistical tools such as median, mean and mode are
used.
For example: Score obtained by students of a particular class in
a test. Another example of data set is height and weight of
students who are participating in a school tournament.
What is a database?
List three (3) examples of
databases as part of your
answer.
(50-100 words)
Database is a gathering of structured information which is
organized and has been stored generally on a computer system
in an electronic form. It is being controlled by Database
Management system and with the help of which data can be
managed, accessed, updated and modifies in an easier manner.
Three examples of databases are MySQL, Microsoft Access and
Oracle Database.
Describe two (2) techniques
for synthesising data (one for
qualitative and one for
quantitative data).
(50-100 words)
Techniques for synthesizing data are as follows:
Meta analysis: It is a statistical analysis which undertakes to
integrate the outcomes of number of scientific studies that aims
to address the same question (Ayesha, 2017). With the help of
this analytical technique, a quantitative estimate for the

Question Answer
phenomenon of the study can be generated. Accordingly, this
technique is useful for synthesizing quantitative data.
Meta ethnography: It involves a set of techniques for the
purpose of synthesizing qualitative studies. It includes selecting,
comparing and analysing to generate new concepts and
interpretations. Stages involved in this are reading previous
studies to determine how they are linked with the current studies
by listing concepts and performing comparison and contrast
among studies. Also, by translating studies into one another new
concepts and interpretations are generated.
Question 2
Think about policies and procedures relating to:
 Accessing information
 Recording and reporting outcomes of the analysis
 Requirement for data analysis
What should the policies and procedures include?
Accessing
information
For this the policy should include accessing only quality and authentic resources
for the purpose of the study. Also, policy should be such that ensures relevancy
and accuracy. With regard to procedure, it should be such that will not harm the
interest of any human being or involve inhuman behaviour.
Recording
and reporting
outcomes of
the analysis
While recording and reporting the outcomes of the data analysis, the policy
should involve the way in which outcomes are to be recorded and to whom it
should be reported (Siew, Rosli and Yeow, 2020). The procedures should ensure
that the right procedure for recording and reporting must be in place to avoid
any mistakes and missing of facts.

Requirement
for data
analysis
With respect to the requirement of analysing data, the policy should include the
correct use of analytics, data protection and limiting the analysis to accomplish
just the legitimate business objectives. Procedures related to the requirement of
data analysis include the statement of techniques and methods such as time
series analysis, sentiment analysis, Monte Carlo simulation and regression
analysis.
Question 3
Answer the following questions.
Question Answer
Outline three (3) key features
of industry standards and
techniques relating to data
analysis.
(30/50 words/feature)
Feature of industry standards and techniques associated with
data analysis are as follows:
The techniques or industry features that are being used in
analysing data should clearly and correctly state the research
question and also refine it for better understanding.
The techniques must allows for exploring the data, so that
deeper insights can be developed for the data so collected.
The standards and techniques must provide for scope of
interpreting and communicating results, so that the research
objectives can be achieved in desired manner.
List five (5) potential data
sources.
List of five potential data sources are as follows:
Surveys and questionnaire
interviewing focused group
Government publications
reports of renowned organisation

Question Answer
published sources of literature
What is statistical analysis?
Provide two examples of
statistical analysis methods
in your answer.
(50-100 words)
Statistical analysis refers to the process of gathering, exploring
analysing & presenting data in order to determine trends and
patterns existing within the data set (Walia and Kalia, 2020).
Scientific decisions can be made in industrial and governmental
concerns with the application of statistics, so that analysis can
be performed accordingly.
For example, calculation of mean provides with average value
of the large amount of data to make effective decisions. Another
example of statistical analysis methods is hypothesis testing.
What are the key legislative
requirements relating to data
analysis? List 4-5 in your
answer.
Consent: Fair practices related to data collection and analysis
require that information associated with an organization and
individual must be gathered and analysed after informing people
about how it be used.
Business purpose must be legitimate: There must be an
establishment of legitimate business purpose by an organization
and individual who are involved in data analysis.
Purpose specifications: The purpose behind collecting and
analysing data must be specified for which it will be used by the
analysts (Ahmad and et.al., 2017).
Data minimization: Many laws and policies made it compulsory
for organizations to restrict personal data collection just to fulfil
their necessity and which is relevant for accomplishing specified
purpose.
Outline three (3) methods for
reporting analysis.
(80-120 words)
In a business context whatever analysis of the financial data is
performed can be reported in the following manner.
Financial statements: All the data pertaining to the business

Question Answer
entity in terms of financial transaction that take place during the
given period can be summarised in a financial statement and
accordingly financial performance can be reported to
shareholders and management.
Budgetary reports: Budgetary reports are prepared by
management to report to top management and executives. These
are prepared on the basis of past performance and predictions
made accordingly for the future performances.
Text mining: It is the process through which examination of
large volume of gathered documents are done in an attempt to
discover new concepts and addressing the research questions.
Question 4
Describe the following factors that impact the reliability of data.
Factor Description
(30-50 words/factor)
Timeliness Timeliness: The data so collected for the purpose of addressing
research question must be an updated one. It should not be an
outdated data. There must be a consistency in availability and
accessibility of data.
Authority Authority: The reliability of data will also be depended upon the
authority it needs to access the data. When data are not be
available for general use, then it enhances its quality and
reliability both (Chishtie and et.al., 2020). Authority indicates
who is the author or sponsor or publisher of an information and
the source from which information are gathered always helps in
enhancing the reliability of data. The qualification of the author

Factor Description
(30-50 words/factor)
and affiliation of an organisation are considered as their
credentials who are involved in presenting the data, thus
enhances data reliability.
Audience Audience: Audience from whom data are collected is known as
sample of the research and the type and nature of sample is the
factor affecting the data reliability. Audience may also be those
for whom the data are gathered and therefore, the reliability
depends upon their needs and expectations.
Relevance Relevance: The data so gathered for the purpose specifies in
advance must be such that leads to addressing the research
question or topic (Peddoju and Upadhyay, 2020). The
specifications of the intended audience and the level of
appropriateness of the information determines how reliable the
data is. Also, the sources from which the data is collected
determines its relevancy and reliability.
Potential bias Potential bias: When there is any kind of deviation from the
truth while collecting and analysing data, then it leads to false
conclusion and accordingly affects the reliability of data.
Biasses can take place at any time and known as selection bias
and publishing bias.
Question 5
Outline three (3) methods for data analysis.
Method Outline
(40-80 words/method)
Regression analysis This type of analysis is helpful in determining what kind of
relationship exists between the set of variables. With the help of
regression analysis, it can be identified that is there any

Method Outline
(40-80 words/method)
correlation exists between dependent and independent variables
and how independent variable are affecting dependent variable.
Time series analysis It is a statistical technique through which cycle and trends
existing in the data over the time are identified (Sapountzi and
Psannis, 2018). Time series provides for sequence of data points
and with the help of it the movement within the same variable is
measured over a certain time period.
Monte Carlo Simulation It is another statistical or mathematical technique which is used
while analysing data in order to estimate or predict the probable
outcome of an uncertain event. It considers many possibilities to
reduce uncertainty. In real life, it is used in governmental and
organizational concern to identify, is there any possibility exists
of going over the budget, etc.
Question 6
Research 3 different analytics tools/software. Describe each tool’s features and
benefits
Analytics tools/software Features/benefits
(40-80 words/method)
SAS It is a widely used tool for analysing data in both academia and
industry. It is used for data visualization and statistical analysis
(Pai, 2017). It stands for Statistical Analytics Software and it
aims to retrieve, report and also analyse the statistical data.
Tableau It is an easy to learn tool for data analytics whose job is of
slicing and dicing data in order to create dashboards and
visualization. It allows anyone to understand and see through
their data. It is helpful for business intelligence & analytics for

Analytics tools/software Features/benefits
(40-80 words/method)
making decision on the basis of variety of data available.
Excel It is also a widely used software for data analytics and generally
meant for non-analytics professionals who are not having access
to SAS or tableau. It is a spreadsheet developed by Microsoft
office for Windows, Android, iOS and macOS. It is commonly
used for performing calculations, creating graphics and pivot
tables.

REFERENCES
Pai, T., 2017. Big data new challenges, tools and techniques. International Journal of Engineering
Research and Modern Education (IJERME), ISSN (Online), pp.2455-4200.
Sapountzi, A. and Psannis, K. E., 2018. Social networking data analysis tools & challenges. Future
Generation Computer Systems, 86, pp.893-913.
Peddoju, S. K. and Upadhyay, H., 2020, March. Evaluation of IoT data visualization tools and
techniques. In Data Visualization (pp. 115-139). Springer.
Chishtie, J. A., and et.al., 2020. Visual Analytic Tools and Techniques in Population Health and Health
Services Research: Scoping Review. Journal of medical Internet research, 22(12), p.e17892.
Ahmad, M., and et.al., 2017. Hybrid tools and techniques for sentiment analysis: a review. Int. J.
Multidiscip. Sci. Eng, 8(3), pp.29-33.
Walia, N. and Kalia, A., 2020. Tools in Data Mining A Comparative Analysis. vol, 12, p.6.
Bansal, A. and Srivastava, S., 2018. Tools used in data analysis: A comparative study. International
Journal of Recent Research Aspects, 5(1), pp.15-18.
Ayesha, N., 2017. A study of data mining tools and techniques to agriculture with applications. Spec.
Issue Publ. Int. J. Trend Res. Dev, pp.1-4.
Siew, E. G., Rosli, K. and Yeow, P. H., 2020. Organizational and environmental influences in the
adoption of computer-assisted audit tools and techniques (CAATTs) by audit firms in Malaysia.
International Journal of Accounting Information Systems, 36, p.100445.