Data Analysis of Postgraduate Applicants Using Serverless Cloud Tech

This project focuses on developing a data analytics solution for universities to predict the behavior of postgraduate applicants. It addresses the challenges universities face in managing large applicant datasets, especially after the introduction of PG loans. The project aims to build a data analytics dashboard that integrates data from sources like UCAS, HESA, and universities, using a non-relational architecture and serverless cloud technology (AWS). Key objectives include developing a visual environment for predictive analysis, utilizing algorithms to understand applicant demographics, and recommending recruitment strategies. The project explores the advantages of serverless technology and cloud computing for data storage, processing, and real-time analytics. It also covers research questions related to data analytics tools, AWS, and the relationship between data analysis and applicant prediction. The project includes a literature review covering cloud computing, data analysis, and machine learning techniques like supervised learning. The goal is to create a dashboard that enables universities to make data-driven decisions to improve postgraduate recruitment outcomes.

Postgraduate potential Applicants on Data
Analysis using (AWS) Server less Cloud
Technology
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ABSTRACT
It has summarised about the unprecedented growth of information or data whether they are
centralised of oncoming for handling huge data set through relational database management
system. The project has concerned about the big data technology such as Amazon web server,
which are collecting or gathering large amount of data on cloud server. In order to increasing the
storage processing of enterprise and also derived the value from using big data analytics.
However, it has rapidly changing the demand of enterprise in global marketplace, because it has
automatically flowing large data or information. Therefore, it obvious to bring new challenges
but it could be managed or handled by analytical tool or platform. Afterwards, it has planned or
selected an appropriate architecture to storage of infrastructure, processing and visualize in
proper manner.
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Contents
ABSTRACT....................................................................................................................................2
CHAPTER-1 INTRODUCTION.....................................................................................................4
CHAPTER-2 LITERATURE REVIEW AND THEORETHICAL BACK GROUND..................6
CHAPTER-3 METHODOLOGY, TECHNOLOGY AND OUTCOME......................................18
CHAPTER-4 DESIGN, IMPLEMENTATION AND TESTING.................................................24
CHAPTER-5 CONCLUSION AND RECOMMENDATION......................................................40
REFERENCES..............................................................................................................................42
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CHAPTER-1 INTRODUCTION
Data analysis is based on the process or method which mainly consider for purpose of
analytics. Sometimes, it is composed with process which help for performed different
functionality (Trudgian and Mirzaei, 2012). Data analytics is a practice concerned about the
entire business because this process will help for managing and controlling behaviour or data
generated by different activities.
In this report, it will be concerned about the different functions that performed by Data
analytical technology such as Amazon web service. Furthermore, it is a proper modelling of
information or data. The project will help for identifying the specific rate of potential applicants
on analysis the data by using server less cloud technology.
Aim
Aims to provide a solution on a data analytics dashboard that universities can use to
predict and analyse the behaviour of postgraduate applicant by gathering dataset from different
organisations such UCAS, HESA and Universities and integrate with non-relation architecture.
Back ground
Big data is based on the modern technology in term of data analytics. It consists of large
amount of information or data. Therefore, it is able to collect, store or maintain the data and then
processing software. Initially, it has identified the various type of challenges factors that always
support for collecting data and also sharing files between one or more devices. Through big data
technology, it can easily modify, structure and maintain the specific privacy or security aspects.
This modern technology will support for involving the all unstructured or structure data but
arranging through big data into structure format.
Server less technology is mainly used for identifying the potential applicants which
always supports for maintain the scalability and flexibility in data management. AWS is consider
an appropriate server less technology that applicable in the different organizations. Furthermore,
server less technology will be performed the process and also represented through use cases.
AWS can be used the effective components in achieving the accurate result or technology
(Backes and et al., 2019). Generally, this technology has been considered the Python
programming language. By using server less technology, it has been reducing the risk, threat
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within data management in order to maintain a proper security as well as privacy. Furthermore,
different organizations that has been adopted the data protection regulation (GDPR) which
always help for providing the better way to strengthening all essential provision in regards of
information.
Problem statement
The problem has been occurred when government introduced new PG loan in 2016. Its
primary aim is to influence or encourage towards the postgraduate enrolment number. In order to
show the evidence in regards of domestic recruitment. On the other hand, it has identified the
universities face issue because they are able to follow the proper threat within postgraduate
applicants (BaiJhaney and Wells, 2019). Therefore, it is very difficult for managing the large
amount of data in regarding the recruitment. That’s why, universities are planning to use server
less modern technology which always supports for analysis large amount of data or information.
Course leader
The research project is mainly focused on the improve the necessary requirement for
business so that it should be identified the problem at different level. The majority is that when
overseas applications but they do not materialize. The problem has been occurred when receiving
the application throughout the years. It would be better for considering the offer on the basis
annually. When resolved the problem so that overseas application and also accumulate the
multiple application in order to create the attention at the time of advertisement.
Objective
 To build a data analytics visual environment to predict postgraduate applicants based on
previous data that can be used for management decision-making.
 Using algorithm analysis to understand the demographics of applicants and
recommendations on where to focus on recruitment and advertisement for postgraduate
applicants.
 Review best Technologies AWS platform or tools and infrastructure to use for data
analytics.
 To develop postgraduate recruiting dashboard to collect, transform, store, and process
data streaming using latest technologies Amazon web server.
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 To build non-relational architecture on a real-time predictive analytics system to identify
the applicants before they become a student using previous data statistics.
 Integrate data between the various system and store data on cloud system for processing
and streaming.
 The aim is to develop a dashboard for universities to predict student’s behaviors on real
time to allow management teams to make effective decisions based on those insights.
 Develop and design data Analytics dashboard on cloud service environment using a cloud
company such as Amazon Web Service or Azure platform.
Research questions
 What are different type of data analytical tool or platform used?
 What do you understand concept of AWS modern technology?
 What are advantage and disadvantage of server less technology in organization?
 What is the relationship between data analytics and predicting the potential applicants?
CHAPTER-2 LITERATURE REVIEW AND THEORETHICAL BACK
GROUND
Theme: 1 cloud computing
Church, Schmidt and Ajayi (2020) Cloud computing is based in the on-demand
availability of service from different application to storage and processing power, especially used
for data storage without establishing the direct management by user. It is the most effective
conceptual innovation in which describing the data centers available to different users over
internet. Moreover, Cloud computing service is mainly covered wide range of options which
always giving the basic storage, networking and process by using natural language processing.
There are various fundamental concept behind the cloud computing. It is mainly storage data
from particular location of service. Many other detail includes as operating system, hardware. In
this way, it become easier for running the entire cloud services in proper manner. Universities or
educational institution also focused on adopting the cloud computing service. This will provide
the better way to manage or store large amount of data. It is very effective in term of security or
privacy so that large number of institution create their own cloud environment.
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Over the past decade, it has been concluded that interest in adoption of cloud computing
within organization. It always supports for promises the potential to reshape the different way of
acquire or manage need for different efficient resources. In particularly, the line with notion of
different shared services so that cloud computing is consider as innovative model in term of IT
infrastructure. Usually, it enabled to focus on the specific core requirement of enterprise
activities. Thus, it support for increasing the productivity as well as profitability in global
marketplace. The adoption of cloud computing means that automatically improve the simplicity,
scalability and flexibility. Afterwards, it provide the better offers to increase demand of cloud
computing in global marketplace. In recently, it has been reported that 76% of large corporation
are adopting the cloud services whereas 60% of SME are also adopting cloud computing.
Moghaddam (2020) there are lot of definition of cloud computing that has been relevant
to the AWS technology. In order to provide the efficient IT sourcing model for improving the
performance of enterprise application on regular basis. it became wider as internet based service
offering while comprised large amount data storage, hosting network infrastructure. Moreover, it
also added the cloud service consumer which despite that there might be offering better services.
Usually, it is depending the enterprise need and requirement whereas considerable as Saas. It
help for improve the capability of consumer when they can enterprise applications. These are
running on the cloud infrastructure and performed the multiple task.
The cloud service model has deployed in term of different private cloud whereas it
provision for exclusive use by enterprise, which also comprising the multiple clients. By using
cloud computing platform, enterprise may be managed, owned and operated with effectively. On
the other hand, community cloud, where it applicable within infrastructure that provisioned for
exclusive used by single community. In this way, different enterprise should be adopting the
cloud infrastructure and provide the service in different manner. Large Corporation have a
specific slack resources in context of both technical as well as financial manner. It also becoming
affordable to easily deploy the private SaaS, IaaS and PaaS.
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Theme: 2 Analysis
As mentioned in the context of literature summary of report in this paper, which is known
as analysis. The author may describe about how individual people understand the concept of
cloud computing. Sometimes, it has become consider as source of targeting the large number of
organizations. For instance, it also analysed the individual opinion or view point in term of data
mining. The business decision has considered one of the strongest strategic overview, which
always helping the decision development (Buure, 2020). When SME have focused on the
adopting the innovation in the business so that they can identify the opinion about data mining of
individual people. Implicit way to gain the demand, trends and insight, covering the new
opportunities from various perspectives.
In order to achieve the specific goal and objective in relevant to the data mining. Initially,
it is important for predicting the behaviour when it can use an appropriate algorithms. In
particular areas which required to relate with different algorithms which called as computing
thinking. As per considerable as computational thinking algorithms that will help for using in
project. For purpose of applying the abstraction, algorithmic thinking to resolve the problem in
regards of data application problem or issue.
In context of supervised learning, user may define the specific set of input and always
expected the accurate outcome. That’s why, they must adopt the effective computational
algorithm. Therefore, computer may have learned about the specific input which automatically
convert into reproduce the outputs. The supervised learning that will be identified to achieve the
analysis process. Initially, it is mainly focused on the determined features, labels in concept of
large datasets (Ochara, 2020). Moreover, it has determined the sentiments scores which are given
in the text format. Afterwards, it will be assigned the specific score value, which carried the
positive or meaning result. Furthermore, Machine learning process always compute that generate
both negative as well as positive result. Therefore, it is helping to achieve the supervised learning
need or requirement. Usually, there are lot of features that must be categorised into data type.
Each and every character that show unique value so that machine will unable to interpret in step
by step manner. In machine learning, the feature may need to be converted into different vectors
on the basis of requirement. Afterwards, machine can easily compute.
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Secondly, it also considered the various combination of label as well as feature that will
be generated new dataset. It also trained on the basis of specific machine, large number of data
set that will split into different set such as training and prediction. Usually, supervised learning
should include the logistic regression, Bayes classifiers. These are considered as appropriate
method which mainly used as learning algorithm. These are applicable for performing the
different event and then help for predicting the binary value. It also understand that logistic
regression is the most appropriate technique which is becoming suitable to determine whether
particular item will receive in both positive as well as negative manner.
Munir and Jami (2020) The Bayes theorem can use the various assumption that establish
the strong interdependence between features as well as label in context of modelling. This type
of theorem will help for formulating the probability and easily classified in different manner.
Moreover, it has been outlined the actual image of big data analytics and their application. On
the basis of analysis, it also support for analysing the appropriate data, information and their
nature. It may use the multiple processing to maintain the overall architecture, which will
become scalable, fault tolerant and also available within distributed environment.
Unsupervised learning that should not be considered pre-define variables so that
computer have a left on its own to determine the different patterns of threats in large collection
or data. On the other hand, Reinforcement learning is capable for sense the current hardware
environment where how they learn from its interact with other dynamically (Coleman, Secker
and Birch, 2020). Furthermore, it is mainly focused on the predication model which applied large
amount of dataset to computer with final prediction. It may consider the supervised learning
algorithms that can be applied to the text mining such as logistics regression, decision tree,
regression and other type of Naïve Bayes classifiers. Generally, it is concerned about the
logistics regression or Naïve Bayes classifiers. Logistic regression is known as supervised
learning algorithm that identified the probability of particular event or program which occurring
on the basis of binary predicator.
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X is based on the independent variable and F(x) is dependent variable
By applying the supervised learning technique. It is based on the Bayes theorem, identifying the
probability formula which concluded the classification. In order to outline the big data analytics
application.
Theme: 3 the big data Architecture
According to (Basu, Hamdullah and Ball (2020) Big data is based on the modern
technology that always contain huge amount of data or information. It also beyond the modern
technology capabilities which can easily store, manage and process the information effectively.
The accurate data which scale, diversified the complexity require algorithms, architecture and
modern technology (Coleman, Secker and Birch, 2020). This will help for manage and extract
the new value which are hidden to acknowledge from it. Big data is a field that basically treat the
way to analyse or extract information from or otherwise deal with large amount of set. Usually, it
may offer the greater statistical power which information with maintain the high complexity,
lead to higher false discovery rate.
Big data analysis in term of privacy and security
Moreover, big data has been characterised by five different elements such as variety,
value, volume and veracity. Generally, volume may describe the particular size of data that may
continuously keep growing in global marketplace. The large amount of data come from various
resources such as transactions, sensors, relational database management system and other type of
historical information. RDBMS and other data technologies support for maintain privacy as well
as security
By using different sources that may consider historical data which bring large volume
about considered an effective challenges in business perspectives. In order to calculate, manage
and process large amount of data. The major challenge is that when motivating towards
innovation from big data paradigm.
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Apart from that describe the structure of big data and organise into structure format so
that it become easier to access information or data. The relational database management system
like SQL server, oracle database and BD2. These are basically used to maintain or collect
information while reducing the redundancy and also ensure the consistency of data. In most of
cases, the large amount of data can exist within unstructured manner. That’s why, it is an
essential for handling the large by using NoSQL database system and help for manipulating or
processing the different type of data. Another challenge aspects is that when consider single data
model so that they can easily handle large amount of information effectively.
Microsoft Azure
Big data on Microsoft Azure which providing the robust service for analysing, evaluating
large amount of information. It is consider one of most effective way to store or collect large
amount of data. Generally, it can be used Azure data lake storage and then process through spark
on Azure Databricks. Moreover. Azure stream analytics is defined as MS for real time data
processing (Coleman, Secker and Birch, 2020). It mainly used for stream based analytics query
language such as T-SQL. It means that allows to easily understanding the time learning.
There are large number of data sets, which often a big data solution that must process
with the help of long-running batch activities. In order to filter and aggregate and prepare for
purpose of data analysis. When transform the large amount of data or information by using
Microsoft Azure because it help for converting into actionable insights and allows for combing
with large amount or data at certain scale.
Azure analytics service which enable to use full breadth of large data assets, helping to
build the transformative or secure the better solution at business scale (Coleman, Secker and
Birch, 2020). It is fully managed by Azure data lake storage and other type of azure analytics. It
is useful for deploying the better solution and transform data into visualization.
Amazon web services
It is based on the modern technology that mainly evolve cloud computing tool or
platform provided by Amazon. This technique is based on the combination of infrastructure as
service and platform as service. These are providing the better quality of service to organization.
In order to storage large amount of data or information within database management system.
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AWS offers variety of tool and solution for organizations or other developers that can be
used in the data centers. Amazon web service can be categorised into different manner. Each can
be configured in different way on the basis of user’s requirements. It should be able to see
specific configuration options and individual server to map with AWS service.
The Amazon web services are provided from large amount of data centers which spreading
across the availability zone. It may contain the different physical data centers so that many
enterprise select the particular zone for particular reason (Choudhary and et.al., 2020). This type
of technology will be providing the scalable object storage for data backup. Within enterprises,
IT professional can store data and files as S3 objects and its current range up to 5 Giga bytes. In
this way, organization can save money with s3 through its infrequent access storage tier.
AWS Architecture
It is the basic structure of AWS EC2 which stands for elastic compute cloud. It always
support for maintain or use the virtual machine of different configuration according to
requirement of enterprise (Coleman, Secker and Birch, 2020). Usually, it allows for handling the
configuration options, mapping individual server.
Figure 1 Amazon web service (AWS)
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