Data Handling And Business Intelligence (Assessment 2)

This assessment covers the use of excel in pre-processing, analysing and visualizing the data, numbers of customers as male and female, number of customers eating rice, mean and median of the ages, clustering and data mining methods. It explains how excel is used for data pre-processing and analysis, and how charts and graphs can be created. It also includes a cluster analysis and discusses data mining methods such as clustering and regression.

ASSESSMENT 2

Table of Contents
PART-1............................................................................................................................................3
Use of excel in pre-processing, analysing and visualizing the data............................................3
PART-2............................................................................................................................................6
Numbers of customers as male and female.................................................................................6
Number of customers eating rice.................................................................................................7
Mean and Median of the ages......................................................................................................8
Mean and Median of participants that eat rice.............................................................................8
Clustering...................................................................................................................................10
2.2 Data mining methods...........................................................................................................15
2.3 Advantages and disadvantages of SPSS over Excel............................................................15
REFERENCES................................................................................................................................1

PART-1
Use of excel in pre-processing, analysing and visualizing the data
Data refers to individual facts, statistics, information which is being collected from the
observation or the research of the specific aspects. It can be a set of qualitative and quantitative
value that may be related with respect to a person, or object (Vassakis, Petrakis and Kopanakis,
2018).
As the data are usually present in raw and unstructured mode that may need to be
integrated and combined into under-stable mode for making understanding. Excel is one of the
best tool that is being used with regard to making pre-processing and analysing the data. Pre-
processing refers to a data mining process under which a transformation of raw data into under-
stable mode is being performed (Baviskar and et.al., 2021). This will lead to have better
understanding of the data. Excel is one of the major tool with respect to this aspect. This is
because excel involves the collection of raw data and making its conversation into under-stable
mode. Under excel the data is filled in rows and columns in such a mode that it will lead to make
establishment of the relationship between them. This is being performed with the help of various
tools of excel including pivot tables, charts and graphs and various others. Excel is one of the
highly used tool with respect to data pre-processing because it will lead to make the data
conversion into under-stable mode.
Along with pre-processing, excel will also be used to make the analysis of the data. As
per this aspect excel through its tools say charts and graphs make the conversion of data into
analytical mode so that the trend and the related information and meaning of the data will be
determined. This means through the tools of the excel the data can be made analysed and its
concerned trends and meaning can be determined. It is also to be noted that as excel enable the
making of graphs and charts so it will automatically lead to visualization of data in such a mode
that with the help of prepared charts and graphs from the gathered information, visual effect in
terms of under-stable charts will be presented (Evergreen, 2019).
However, it is also to be noted that although with the help of excel, pre-processing of data
will be enabled but it also include the risk of occurrence of errors. This is because under excel
data need to be placed in the rows and the columns but while inserting the data in it there are
chances that the human error may occur and affect the extraction of the data and pre-processing
of data (Tsou, 2019). Likewise, it is also to be noted that it is also a difficult aspect to make

insert of the vast value of raw data in excel rows and columns because it will raise the chances of
occurrence of human errors in terms of escaping the data or the wrong putting of data.
But on a wider scale excel is one of the major data mining tool with regard to making
pre-processing of data and its analysis. Along with excel its associated tools including pivot
tables, bars and graphs and others also plays an important role ith respect to the data processing
and its analysis. This is because when data will be arranged in rows and columns from raw mode
and when charts and graphs will be prepared from arranged data then all the aspects including
data processing, analysing and visualization will be able to performed in an effective manner
(Hossain, 2021).
In addition, of this it is also to be noted that graphs and charts is one of the major feature
of excel under which with the help of data, graph and charts will be drawn. With respect to the
current data the graphs and charts are used for the data pre-processing and analysing. Through
the adoption of the following steps charts and graphs can be created:
 The process of integration of this technique starts with the application of filter function
which will lead to make emergence of relevant data and figures.
 After the application of filter, range will be applied and selected so that the data of the
sales will be collected.
 With the following of the rage of particular year, the sum formula is being applied so that
the sum total of sales of the specific year will come. In this way the sum total of sales of
2009, 2010, 2011 and 2012 will be gathered.
 Similar steps starting from application of filter till the selection of range and imply sum
formula are also applied with respect to determination of the trends of profits of 2009,
2010, 2011 and 2012.
 Later the range of the data will be selected followed by the selection of the graph and
chart function. This will lead to have a formation of the graph. Same practice will be
implied in both the aspects of discount and profit.
 With the preparation of graph it is being analysed that there is an existence of direct
relation between sales and profit which means that with a rise in the value of sales the
profit proportion also raises.

 From the above steps and the following by the data presentation it is being cleared that
there is an existence of positive relationship between the sales and profit aspects which
means that with a rise in the value of the sales the profit will also raise and vice versa.
Graphical representation:
As per the above graphical representation it can be analysed that the sales and profit are
directly related with each other. This is because with the fall in sales figures the profit also falls.
As the sales in 2009 was 1751774 which falls in 2010 and reaches to 1316936. With such fall it is
also noticed that the profit also falls which can be analyzed as profit in 2009 was 152253 which

falls in 2010 and stuck at 132154. This means with a fall in sales the value of profit also falls.
The same case occurred in subsequent years wherein in 2011 the sales raised and reaches to
1472060 which is further followed in 2012 where the sales strikes to 1601552.126. With a
growing value of sales, the profit also raised as in 2011 it raised and stuck at 161414 from
132154 of 2010. However, falls in 2012 and stuck at 130967.
As per this analysis it can be evaluated that with a rise in the value of sales, profit value
also raised and when sales decline then the value of profit also falls. This means there is an
existence of positive relationship between sales and profit.
It is also evidenced form the literature with the views of Dijkhuizen and et.al., (2018)
which states that there is a positive and direct relationship between sales and profit value. As sale
value is the price which include the value of profit and cost of the operation. This means with a
rise in the proportion of sale the cost of operation are being covered and the profit value will
raised. Since sales refers to the selling of product of the company and if the sales will have raised
then this will lead to bringing of cash in the company which will further assist in the rise in the
value of profit.
Lutilsky, Liović and Marković, (2018) also states that sales include the sale of
commodity and bringing of sales revenue. However, profit is that amount which is being left
over after the incubation of all the expenses and operation cost. This also state a direct
relationship between sales and the profit. This is because with the sales of the product, sales
revenue will come. From that revenue all the cost will be covered and expenses will be bear
which will lead to enable the profit. This means with a rise in sales the profit will also raise and
vice versa too.
On a contra dictionary note Chen, Harford and Kamara, (2019) also state that although
sales and profit are directly linked with each other but at a certain point when the percentage of
cost and expenses will raise then the value of the profit is being affected. This means the profit
which is being earned after the coverage of expenses will be affected when the expenses will
start rising. This means that the along with sales, an analysis of the sales and making changes in
the sale price is also essential in order to establish the same positive relationship.
From the above analysis and the literature, it is cleared and analyzed that there is an
existence of direct relation between the aspects in the context of the organization. This is also
evidenced from the sales and profit trends of the Superstore data which shows a clear aspect that
the fall in sales value in 2009 to 2010 shows a subsequent fall in profit of 2009 and 2010. Thus,
it can be right to said from the data of superstore and the prepared graph of the data that the sales
and profit are directly linked with each other and a rise in one aspect will lead to rise in the other
also.
PART-2
Numbers of customers as male and female

Gender
Frequency Percent Valid Percent Cumulative
Percent
Valid
Male 50 50.0 50.0 50.0
Female 50 50.0 50.0 100.0
Total 100 100.0 100.0
Interpretation: The above data shows that 50 percentages are male and 50 percentages are
female, which shows equal participation of gender is there, which shows smile clinic has equal
number of genders. It also enables to highlight importance on fact that there are equal number of
customers who come at clinic, and gender are male as well as female which shows efficiency in
system.
Number of customers eating rice
Rice
Frequency Percent Valid Percent Cumulative
Percent
Valid
No 40 40.0 40.0 40.0
yes 60 60.0 60.0 100.0
Total 100 100.0 100.0

Interpretation: From the above data, it can be interpreted that people who eat rice are 60, and
who do not eat rice are 40 which shows that maximum number of people are those who prefer to
eat rice. 60 percentage of people prefer to eat rice and 40 percentages do not eat rice, which is
also clearly depicted by graph.
Interpretation:
Mean and Median of the ages
Statistics
Age
N Valid 100
Missing 0
Mean 20.35
Median 19.00
Interpretation: The mean of age is 20.35, which shows that age factor holds high scale
importance where data findings are further found to be attained in proper structured format
variedly. Median, explains that age determinant is found to be equal and at stable highest value
growth parameter for attaining proper structured aspect.
Mean and Median of participants that eat rice

Statistics
Age Rice
N Valid 100 100
Missing 0 0
Mean 20.35 .60
Median 19.00 1.00
Interpretation: The above aspect of data explains that mean of people who prefer to eat rice is
20.35 which shows stable functional parameters and to be further evolved on for getting accurate
data. Median is 19, which also explains focus on fact that people who prefer to eat rice are
maximum as compared to people who not eat.
Clustering

Step1

Step 2:

Step 3:

Step 4:

Results are..
Quick Cluster
Initial Cluster Centers
Cluster
1 2
Gender 1 2
Age 13 26
Rice 1 0
Iteration Historya
Iteration Change in Cluster Centers
1 2
1 4.489 2.731
2 .228 .384
3 .000 .000
a. Convergence achieved due to no or
small change in cluster centers. The
maximum absolute coordinate change
for any center is .000. The current
iteration is 3. The minimum distance
between initial centers is 13.077.

Final Cluster Centers
Cluster
1 2
Gender 1 2
Age 18 24
Rice 1 1
Number of Cases in each
Cluster
Cluster 1 56.000
2 44.000
Valid 100.000
Missing .000
Interpretation:
The above cluster analysis is multivariate method which aims to classify focus on sample
among subjects that has measured variables into number of groups, where similar subjects are
being place. Clustering is also one of the most advanced format for gaining productive analysis
from research, where results provide detailed accurate analysis of all data for further research.
2.2 Data mining methods
Data mining methods are based on various working scenarios, where clustering and
regression are most commonly used effective methods within research, which further strengthens
results (Denis, 2021). Clustering and regression aspects, are the two most effective data mining
methods, used up for enhancing research results with real world examples.
 The most common data mining methods which can be used in businesses within real
world can be analyzed on basis of efficacy within methods that enables stringent rise
diversely on working advancement. The method of clustering can be understood as group
of different data objects classification as similar objects, which enables similar context of

data to be classified in one group. After classification of data into variable groups, label
is assigned to group where best clustering method enables to bring on high quality
clusters where similarity is high which are further understood to be intra class. Quality of
clustering result also depends on both the similarity measure used by method and it
accurate implementation, for obtaining correct data for usage.
 Regression is another used data mining technique used up to predict range of numerical
values, where significant particular dataset is used variedly for obtaining range of
efficiency within research. For example, Regression will be used to predict cost of
product and service, given other variables for obtaining accurate results within complex
data parameters. Linear regression is mostly used for purpose of modelling relationship
between two given variables, and done by fitting linear equation for perceiving the data It
enables to find mathematical relationship between variables, which further strengthens
accuracy of findings for attaining larger scale functional rise diversely on working
paradigms dynamically (Chen and et.al, 2021).
2.3 Advantages and disadvantages of SPSS over Excel.
Excel is spreadsheet software, SPSS on other hand is statistical analysis software widely
evolving in recent arenas with its effective utilization goals significantly. Excel as compared to
SPSS, lacks functional scope to significantly gain best working results for numerical results to be
obtained innovatively. It can be understood that Excel and SPSS, both are methods used for
scaling up resources in advanced format variedly on wider domains. The excel results lack
accuracy, and productive efficiency in gaining data data findings worked on as compared to
SPSS where results are widely evolved in advanced format. SPSS is highly powerful as every
column is one variable and excel dose not treat, where it enables to enhance functional rise on
working operational aspects. Advantages of using SPSS over Excel are based on aspects, that
SPSS is more accurate technically and brings on detailed analysis worked on for strengthened
efficacy within results. SPSS which enables to get determinants organized in proper structured
format. However, disadvantages, on other hand is based on fact that SPSS is time taking and
highly complicated method of research used which further strengthens efficiency in systematic
research parameters. SPSS needs trained individuals, and high advanced instrumental research
method for gaining accurate data. SPSS format though little typical to understand and grasp data
findings, there is structured functional advancement attached for obtaining data results widely on

terms of efficacy within information. SPSS can handle large amounts of data and is easy, quick
to be learned and gas greater user interface, which makes it highly potential method as compared
to Excel (Abu-Bader, 2021).

REFERENCES
Books and journals
Abu-Bader, S. H., 2021. Using statistical methods in social science research: With a complete
SPSS guide. Oxford University Press, USA.
Baviskar, and et.al., 2021. A Survey of Data Science Techniques and Available
Tools. International Research Journal of Engineering and Technology (IRJET) e-ISSN,
pp.2395-0056.
Chen, T. and et.al, 2021. Facile synthesis of magnetic CS-g-SPSS microspheres via electron
beam radiation for efficient removal of methylene blue. Journal of Saudi Chemical
Society. 25(8). p.101299.
Chen, Z., Harford, J. and Kamara, A., 2019. Operating leverage, profitability, and capital
structure. Journal of financial and quantitative analysis. 54(1). pp.369-392.
Denis, D. J., 2021. Applied Univariate, Bivariate, and Multivariate Statistics: Understanding
Statistics for Social and Natural Scientists, With Applications in SPSS and R. John
Wiley & Sons.
Dijkhuizen, and et.al., 2018. Well-being, personal success and business performance among
entrepreneurs: A two-wave study. Journal of Happiness Studies. 19(8). pp.2187-2204.
Evergreen, S.D., 2019. Effective data visualization: The right chart for the right data. Sage
Publications.
Hossain, E., 2021. Graphing and Charting. In Excel Crash Course for Engineers (pp. 19-115).
Springer, Cham.
Lutilsky, I.D., Liović, D. and Marković, M., 2018. Throughput accounting: Profit-focused cost
accounting method. Interdisciplinary Management Research Xiv (Imr 2018). 14.
pp.1382-1395.
Tsou, M.C., 2019. Big data analysis of port state control ship detention database. Journal of
Marine Engineering & Technology. 18(3). pp.113-121.
Vassakis, K., Petrakis, E. and Kopanakis, I., 2018. Big data analytics: applications, prospects and
challenges. In Mobile big data (pp. 3-20). Springer, Cham.
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