Arden University
BSc (HONS) Computing
Data
Analysis
Visualisation
Data Analysis and Visualisation
168523
Rajeswari Matam
and
Table of Contents
Introduction ........................................................................................................... 3
Task 1: Data Import and Cleaning ........................................................................ 5
Data Importation from Excel ............................................................................. 5
Handling Missing Values ................................................................................... 6
Data Security and Integrity ................................................................................ 7
Task 2: Descriptive Statistics & Hypothesis Testing ............................................ 8
Descriptive Statistics ......................................................................................... 8
Calculating Key Statistics .............................................................................. 9
Hypothesis Testing........................................................................................... 10
Formulating Hypotheses .................................................................................. 10
Conducting the Hypothesis Test................................................................... 11
Report to CEO ................................................................................................. 12
Task 3 Dashboard Creation and Data Visualization ........................................... 12
Dashboard Configuration ................................................................................ 13
Designing Visualizations: ................................................................................ 13
Insights from the Dashboard ........................................................................... 17
Justification for Dashboard Design ................................................................. 21
Discussion and Recommendations ..................................................................... 22
Summary of Key Findings............................................................................... 22
Recommendations for the Organization .......................................................... 22
Improvements and Future Work ...................................................................... 24
Conclusion .......................................................................................................... 25
1
References ........................................................................................................... 27
Table of Figures
Figure 1: Cleaned Dataset Screenshot .................................................................. 7
Figure 2: Descriptive Statistics Screenshot ........................................................ 10
Figure 3: Hypothesis Testing by using ANOVA data table Screenshot .............. 11
Figure 4: Hypothesis Testing data table Screenshot ........................................... 12
Figure 5: PIVOT Table 1 Screenshot .................................................................. 14
Figure 6: PIVOT Table 2 Screenshot .................................................................. 15
Figure 7: Custom Chart Screenshot .................................................................... 16
Figure 8: Insert Slicers Screenshot ..................................................................... 17
Figure 9: Wages by Occupation chart Screenshot .............................................. 18
Figure 10: Wages by Education Level Screenshot ............................................. 19
Figure 11: Average wages by each country ........................................................ 19
Figure 12: Wages by Ethics origin ...................................................................... 20
Figure 13: Employees Counted by Education level and Occupation Screenshot
............................................................................................................................. 21
Figure 14: Dashboard Screenshot ....................................................................... 21
2
Introduction
In the present business world, organizations require means and approaches that
shall improve their capabilities of developing systems to analyze data for use in
decision-making and organizational and operation change. The requirement for
business adoption for data analysis is felt most greatly in HR as data such as
wage and compensations, bring out key facets such as equity, performance, as
well as wage lag. Proper collection, storage, processing, and evaluation of data
ensure a stable basis for wage Discrimination, determination of an ideal
employment structure as well as the search for the best talent in per efficient
resource. Before engaging in the analytical discussion of the obtained results,
there is a need to point out that this report aims to analyze the employee wage
data gathered from various sources of a global company. Data about a subject
could be and is not restricted to the employee’s position at the moment, their
academic achievements, the country of residence, sex, and salary. Based on the
principles of using data analysis, the report shall respond to the following major
questions as follows; On wage disparity by employees, gender, job type,
education as well as region and, with a focus on wages paid to employee’s
categories. This report is to keep a general archive of all the processes that are
undertaken while carrying out the data analysis and viewing the results of the
data cleaning and transformation, such as detailed Coverage Analysis to find out
the dirty data types that need to be cleaned, Hazard Analysis to infer higher
wage equality, Hypothesis Testing, and lastly, to create an interactive wage
dashboard for the company. Each part of the report plays a role in forming a
perspective on the dataset that may prove helpful in decision-making about
employees’ compensation in business.
This report is divided into the following key sections.
 Data Import and Cleaning: The section explains importing the raw data
and cleaning it by making replacements and keeping it secured.
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 Descriptive Statistics and Hypothesis Testing: The report will analyze the
essential measurements of the dataset such as mean and standard deviation to
present a summary of wage patterns. Then a hypothesis test will be
conducted to find out if there are major differences in wages between the
USA and other areas.
 Dashboard Creation and Data Visualization: This section features setting
up a dynamic dashboard using visualization features in Excel to showcase
important wage data within various categories. The dashboard contains pivot
charts and slicers for interactive analysis of wage information.
 Discussion and Recommendations: In this section are found practical
guidelines for minimizing wage disparities and refining the company's
payment systems.
 Conclusion: In the concluding section the report's main insights are outlined
and the results are evaluated in light of their impact on the organization.
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Task 1: Data Import and Cleaning
Data cleansing and modification are essential actions for organizing any dataset
before analysis. Before undertaking statistical analyses or visualizing data it is
essential to confirm accuracy and completeness, it increases the requirements
for data cleansing significantly, in large size of datasets. After loading the raw
dataset with wage details in Excel the data underwent several cleaning and
transformation activities to enhance usability. Addressing missing or
inconsistent values presents a main obstacle while working with real datasets.
Issues may surface from differing causes including inadequate data gathering
and errors made in data input. To achieve this goal, we dealt with the dataset by
removing redundant information and assigning proper values to gaps according
to its context. In addition to that various categories (for example occupations or
genders) were translated for consistency throughout the dataset. This portion
outlines the process of importing data and cleaning it while making sure the
cleaned dataset stays secure.
Data Importation from Excel
The data cleaning effort is started by importing the collected data inside the
Data sheet that features different employee characteristics and wage
information. The standard data importing methods were followed to guarantee
the correct data upload to Excel while checking for column consistency. Several
Excel routines were applied to set up and organize the data. The Text-toColumns tool divided fields with multiple data into individual columns for later
analysis. The format of wage-related numbers was adjusted to preserve a
uniform representation for correct wage assessment at a later point. It confirms
that the organized datasets are organized in a meaningful and its well enough for
performing visualization, hypothesis testing, or creating dashboards.
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Handling Missing Values
In the data modification stage, it was necessary to treat missing or unnecessary
values in the dataset for example missing wage details. In cases of missing wage
data points were substituted with randomly appropriate values based on the
average wage for similar positions. This preserved completeness in the dataset
while supporting a better wage analysis. To remove inconsistencies review and
consolidation were used to standardize values including country gender and
occupation. A common issue in large amounts of datasets is missing data with a
block of concurrently lost as a result of data integrity for consistency and to
reduce biased analysis results job titles for the same occupation are merged into
a single category. The gender values were transformed to Male and Female to
correct discrepancies brought on by different abbreviations or designations.
Such techniques favored a truer and clearer evaluation of compensation trends
found in real-world data sets. Some of the categorical values are replaced by
random categorical values as, in the employee table, the random categorical
value uses like Private, Local-gov, state-gov, etc., on the other hand, there are
few changes are performed inside the occupation column for replacing the
random values by using some random categorical words like other-services,
Adm-clerical, sales, etc.,
There are some basic steps after following this to effectively and efficiently
manage the handling of missing values:
 Handling Missing Numeric Values: The data gap created by missing wages
was bridged by utilizing the average salary for people in related occupations
or countries. This approach made certain that the values imputed were
substantial and unbiased in the dataset.
 Standardizing Categorical Values: Dataset consistency was secured by
checking the occupation and gender classifications. Consistent data was
fixed and alike categories were merged.
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 Relevance Checking: Data points irrelevant to the company’s workforce
were discarded from the dataset. This directed the analysis toward important
employee segments.
Figure 1: Cleaned Dataset Screenshot
Data Security and Integrity
To maintain the accuracy and reliability of the dataset the next action is to
safeguard the cleaned dataset. The main aim of performing data cleaning and
handling missing values is to improve the integrity, availability, consistency
privacy of the dataset (Cinà et al., 2023, p.8). Unauthorized or accidental
modifications may damage the accuracy of the results and the provided results
are found inaccurate or improper manner.
 Cell Locking: When the data was cleaned and transformed the important
cells held the data to avoid accidental updates. Excel’s Cell Locking function
allowed us to secure specific cells until the worksheet was unlocked.
 Worksheet Protection: Every cell of the worksheet holding the cleaned
dataset was safeguarded by a password. This further safeguard guarantees
that illegal users are unable to amend the data without the needed access.
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 Data Validation: By implementing validation criteria on particular columns
in the dataset consistency was maintained as it restricted numeric entries to
genuine inputs and categorized gender fields into defined options.
Task 2: Descriptive Statistics & Hypothesis Testing
In an analysis of data structures, descriptive statistics and Hypothesis testing
play pivotal roles in the era of data analysis. Visual representations of crucial
aspects in datasets are the chief role of descriptive statistics. These figures offer
essential data regarding significant patterns and anomalies in the data allowing
researchers to locate important events and unusual highlights. Usual techniques
in descriptive analysis are mean and standard deviation which facilitate data
review. Analyzing a smaller data set enables analysts to draw insights and make
forecasts regarding a larger audience using inference metrics. Researchers
examine if the detected differences in the data are genuine or simply accidental.
We aim to compute descriptive statistics from the employee wage dataset and
test the hypothesis to see if wage differences exist between workers in the
United States and other countries.
Descriptive Statistics
The dataset was examined using descriptive statistics regarding employee
wages in different categories like occupation and education level. The included
Excel file's Descriptive Statistics sheet provided the main data for our analyses.
The average wage and standard deviation were obtained along with the median
and mode among various occupations and education levels. Descriptive
statistical analysis is a statistical tool whose function is described and provides
an overview of the given dataset such as Mean, Mode, Median, Standard
Deviation, and Range, etc.
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Calculating Key Statistics
 Mean Wage: For each job position a mean wage was determined to reveal
average incomes linked to particular roles. For example, $60,000 was the
average annual wage for Software Engineers and the average for Marketing
Managers came to £55,000. The median furnishes a significant sign of
central tendency and enables us to recognize the standard wage amounts per
occupation.
 Median Wage: In datasets with anomalies this median wage proves
beneficial since it remains unaffected by outlier values. For staff members
across multiple professions, the median pay amounted to £50K revealing that
50% received lower wages and the other 50% had higher earnings.
 Mode Wage: The mode represented as the wage appearing most often in
employees was determined to reveal the most typical salary for each
occupation. Many new employees in the dataset achieve an annual salary of
£30.000 from the outset.
 Standard Deviation: Employee salaries within a single occupation exhibit
considerable variation according to the standard deviation in wage data. A
large standard deviation shows major differences in the Sales department
since it was £15,000.
 Range: Calculating the variation in wage levels among occupations revealed
the differences in compensation among staff. The wages for Healthcare
Professionals ranged from £30.000 to £120.000 showing significant
differences in payments due to experience and specialty.
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Figure 2: Descriptive Statistics Screenshot
Hypothesis Testing
The dataset was received for performing descriptive evaluation and hypotheses
were tested to see if there are considerable gaps in wages among US workers
and global staff. To build the framework p-value is a decision indicator obtained
via NHST, in a p-value is the probability of rejecting the null hypothesis (H0)
when it is true and erroneously adopting an alternative hypothesis (H1), and
statistical analysis was conducted to test them (Stunt et al., 2021, p. 49).
Formulating Hypotheses
 Null Hypothesis (H0): The average earnings of US staff and international
employees are closely matched. In mathematical language, this is written as
H0 representing the equation µ_USA = µ_Other.
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 Alternative Hypothesis (H1): Average wages differ greatly between
workers in the USA and those in other nations. In mathematical terms, this
may be shown as H1 does not equal to µ_Other.
Figure 3: Hypothesis Testing by using ANOVA data table Screenshot
Conducting the Hypothesis Test
To evaluate the mean salaries of US employees relative to other country's
workers the study adopted an ANOVA technique that employed the Hypothesis
Testing ANOVA sheet in an Excel format while following the outlined process.
 Data Preparation: The dataset was organized into appropriate wage
information for workers in the USA and around the world giving a total
sample size of 500 U.S. staff and 1.200 employees coming from other
nations.
 Calculating Mean: The wages of employees in the United States were
found to be £65,000 on average; in contrast, the average wage for global
workers stood at £50,000. Calculating values was done through the Excel
AVERAGE function which produced a fast and precise summary of wage
distributions for both groups.
 ANOVA Calculation: An ANOVA experiment was carried out to establish if
the group means showed a statistically meaningful difference. The in-built
data analysis feature of Excel was employed to run the ANOVA test that
delivered the F-statistic and linked p-value in the results.
 Interpreting Results: The research showed a notable gap in earnings for
workers from the USA compared to others around the world with US
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employees earning much higher wages on average than those in foreign
nations. Consequently, this brings up alarm over wage disparities within the
organization.
Figure 4: Hypothesis Testing data table Screenshot
Report to CEO
According to the hypothesis testing, a summary report was crafted for the CEO
to present the findings and these results partially supported the hypothesis
(Jensen et. al., 2020, p. 16). The results demonstrated that employees in the
USA attained much larger average wages than elsewhere. These results point
out critical issues about pay justice and highlight the necessity to explore further
the organization's compensation approaches. The investigation noted that
securing uniform and competitive pay for each branch is essential to sustain
both employees and attract great talent. Actions were proposed to explore the
elements affecting wage inequality including market conditions and differences
in living standards.
Task 3 Dashboard Creation and Data Visualization
In data analysis, the presentation of information helps decision-makers analyze
complex datasets effectively created by dashboards are effective means for
presenting vital performance metrics (KPIs), data trends, and common
characteristics that permit businesses to oversee their operations and take
thoughtful actions grounded in real-time findings. By crafting an engaging
dashboard user can gain insights into data in motion and improve their
awareness of low-level trends and links. This project emphasizes the relative
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proportions of the construction and setup of a dashboard in Excel that includes
employee earnings metrics for various categories within occupation and gender
to collect the data on current employment status and occupational information.
With slicers and pivot charts applied to the dashboard users can actively review
and categorize data for enhanced engagement. This chapter will detail the
approach used to build the dashboard and present the visualizations featured.
Dashboard Configuration
It has been developed using data submitted in the Dashboard sheet of the Excel
file provided earlier. This sheet served more or less as a dashboard where other
information from the cleaned and analyzed output dataset was displayed. It was
necessary to factor in these key variables when setting up the dashboard.
 Data Source Integration: This used web linking, of the dashboard to the
cleaned-up data set, to complete the action of creating the first step (Cappuzo
et al., 2020, p. 1336). This involved the utilization of, all related tables and
all pivot tables that contained the aggregated wages data categorized based
on the different parameters that the dashboard had been pivoted on to display
the most current figures.
 Creating Pivot Tables: Pivot tables were designed to perform a
summarization of the wage data across the chosen categories of interest
(Bartram et al., 2021). For instance, the income pivot table was developed to
identify the average wages of various occupations by ordinary job titles. The
other pivot table displays matched wages by education level as one of the
avenues of depicting the level of education being indicative of one’s
earnings.
Designing Visualizations:
The dashboard included three main visualizations to convey key insights.
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 Pivot Chart 1: A bar chart showing the total wages paid out based on the
various occupations. That visualization helped, as one immediately could
identify which job was paying more or less than others. For example, it
established that while executives are paid £100, 000 on average, interns the
least endowed employees are paid £25, 000 on average.
Figure 5: PIVOT Table 1 Screenshot
 Pivot Chart 2: A bar chart of the employee distribution by their level of
education. This chart also explained how many employees in the company
had formal education as follows; 40 % of the employees were said to possess
a bachelor’s degree while 30% had a master’s degree. Such insights are quite
helpful for gaining the about the educational profile of the workforce.
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Figure 6: PIVOT Table 2 Screenshot
 Custom Chart: A line chart showing gaps in wages between male and
female employees of various countries. This chart was created with data
from the ‘Wages by Ethnic Origin’ column and the Average Wages by Each
Country chart. These findings were evidenced by depicting in the line chart
that though wage gaps persist in some countries, there has been significant
progress in wage equality in other countries (Qin et al., 2020, p. 103).
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Figure 7: Custom Chart Screenshot
 Incorporating Slicers: For increased interactivity, new panels called slicers
are incorporated into the dashboard. Purchasers, on the other hand, make it
easier for users by allowing them to choose data that has to be displayed in
the pivot charts depending on their current needs. For instance, it was
possible to choose individual subcategories such as occupations or levels of
education for the comparison of wages. This feature is useful for
investigating wage issues because it provides more detailed results than
regular data analysis.
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Figure 8: Insert Slicers Screenshot
 Arranging the Dashboard Layout: To achieve this type of interactivity, the
framework needed to be coherent and simple with the boards that are
developed, and there must be the option of direct access to the most
important indicators of the model. It includes charts’ titles, and axis labels at
well-chosen areas in each visual, and color coordination to represent a neat
look and enhance interpretability.
Insights from the Dashboard
The finalized dashboard offered important recommendations for better
understanding current wages and pay gaps among employees, by presenting the
data according to occupation, education, and gender.
 Distribution of wages by Occupation: The pivot chart shows the disparity
in compensation for different positions such as Prof-specialty considered for
£60.000 and Tech Support who earns £30.000. With this knowledge on hand
HR and leadership can evaluate the pay for roles that might need changes to
match market trends.
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Figure 9: Wages by Occupation chart Screenshot
 Impact of Education on Wages: The educator's data illustrated a strong
relation between salary and academic backgrounds for workers’ career
decisions as a function of wage compensation and on-the-job well-being.
The dashboard showed that people with a master's degree receive higher
wages than those who only completed high school. As a result, this discovery
emphasizes how important it is for education to impact job progress and
wealth possibilities.
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Figure 10: Wages by Education Level Screenshot
 Average Wages by Each Country: The chart shows that the USA and UK
earn more than Eastern Europe and Asia. The variation in wages and job
access affects this situation. Over time the data indicates that income
increases in Germany and China. This line chart displays the downfall of
Hungary in the prospects of wages.
Figure 11: Average wages by each country
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 Wages by Ethnic Origin: In the organization's framework that is both
conservative and comprehensive, Black individuals contribute around 40%
of the total earnings. A need exists for a thorough examination of
employment categories and the way promotions are distributed.
Figure 12: Wages by Ethics origin
 Employee Count by Education Level and Occupation: The Education
Level and Occupation Display illustrates who holds what jobs by their levels
of education. In high-skill fields like Engineering and Management expertise
often comes from professionals with higher education qualifications.
Positions at the beginning of the career often cluster in lower education
levels.
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Figure 13: Employees Counted by Education level and Occupation Screenshot
Justification for Dashboard Design
The elements in the dashboard follow a specific design to present the insight.
All illustrations were selected to emphasize particular elements of wage data for
stakeholders to observe the essential details immediately. Pivot charts reduced
extensive data into smaller figures while slicers encouraged involvement from
users who could choose the relevant data they wanted.
Figure 14: Dashboard Screenshot
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Discussion and Recommendations
Summary of Key Findings
The employee wage dataset reveals key information about how wage levels
vary and what factors shape these variations. By performing data preparation
and generating descriptive statistics we uncover the patterns of wages in a
company.
 Wage Disparities by Occupation: The findings showed significant
differences in median pay across different job titles. Roles like Software
Engineers and Executives earn much greater salaries than beginning jobs
such as Administrative Assistants and interns. The importance of a job role
and connected skills play a key role in wage determination.
 Impact of Education on Wages: Results indicated that there was a strong
relationship between qualification and pay. Workers who earned master's
degrees typically took home more income than those with merely a high
school education. Education provides professional opportunities and raises
potential earnings. Therefore, its relevance for improving employee
qualifications is emphasized.
 Gender Wage Disparities: The study showed that gender wage disparities
persisted in the organization and were particularly noticeable in the USA
where male staffers receive 15 percent more than women. The salary gaps in
nations like Sweden are more limited than those found elsewhere. The
importance of assessing compensation policies further is emphasized (Budig
et al., 2021, p. 7).
Recommendations for the Organization
 Review Compensation Policies: A detailed investigation into the
compensation policies is necessary for the organization to confirm their
correspondence with market developments and support equity for all staff.
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Ongoing comparisons to market benchmarks enable the recognition of likely
inequalities and support required refinements. Priority must be given to vital
roles to secure pay arrangements that invite and hold onto top talent.
 Implement Pay Equity Initiatives: The organization ought to highlight
efforts that seek to lessen differences in wages, particularly the managerial
discretion that may facilitate the gender pay equity linked to gender and
education (Ugarte and Rubery, 2021, p. 1). By performing ongoing pay
reviews companies can find shortcomings and set transparent salary
frameworks that encourage equality. Such actions develop a more accepting
work culture and improve both staff happiness and retention.
 Invest in Employee Development: The company must fund professional
growth initiatives that enable staff to earn more money. By providing these
opportunities for training and guidance in addition to education assistance
employees can improve their career paths and enhance their income.
Supporting ongoing learning can close the pay difference and establish a
qualified team.
 Enhance Transparency in Salary Discussions: Introducing frameworks
that support honest dialogue about salaries can clarify compensation
procedures empower workers and enhance relationship trust inside the
group. This means informing employees about pay ranges for jobs and
offering conclusions from salary measures.
 Leverage Data for Continuous Improvement: Employing the information
derived from the dashboard along with analysis will allow the organization
to always examine wage trends and discrepancies. A data-focused method of
decision-making permits the organization to adjust compensation plans as
required. Maintaining the dashboard current with updated information will
accelerate real-time assessment and stimulate proactive measures upon the
discovery of inequalities.
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Improvements and Future Work
 Expanding the Dataset: The analysis stands to gain by having a larger
dataset that incorporates age and job performance metrics. With the enlarged
dataset available now we gain a clearer view of the factors contributing to
wage inequalities and conduct better analyses.
 Utilizing Advanced Analytical Techniques: In upcoming analyses,
advanced statistical models such as machine learning algorithms which are
applied to an alumni impact on analyzing the dataset or regression analysis
could uncover predictors of wage gaps. These methods may reveal a more
profound understanding of how distinct factors influence employee pay and
support targeted interventions.
 Conducting Qualitative Research: Quantitative methods give practical
knowledge; however, research through employee interviews and focus
groups allows a deeper understanding and reveals hidden problems in equity
and job satisfaction.
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Conclusion
The review of the employee wage dataset was crucial not only in illustrating
wage distribution trends but also in revealing critical points of focus related to
wage equity. The data clarified the significant aspects that determine wage
inequalities across multiple employee groups through descriptive analysis and
hypothesis testing. Upon completion of data preparation, it showed that
parameters including industry role and educational status are major
determinants of wages within the company. A significant result of the analysis
revealed a large wage difference between skilled positions in technology and
management and unskilled roles for new hires. Individuals occupying
technology and management positions often take home more money than their
peers with no experience or skills. The disparities reveal how important both
skills and experience are for compensation but they also provoke concerns
about the equity of wage disparities for those in low-profile jobs who can still
be vital to achieving success. The level of education turned out to be a
significant element in wage determination in contrast to employment roles. Staff
with advanced degrees earned more money than those with only high school
qualifications. The findings highlight the relationship between educational
status and wage potential showing how work values formal education and
professional expertise. It reveals that the corporation can profit by providing
more pathways for advancement within skills and education for those making
lower wages. Significant variations in wages emerged after the testing of the
hypothesis. In the USA company hires fetched 30% higher pay than those from
other countries which brings significant doubts to the organization's worldwide
compensation schemes. The difference prompts an evaluation of how well the
company's salary systems correspond to local market norms. The analysis also
reveals a significant problem with the ongoing gap between male and female
wages where male staff generally make more than their female counterparts.
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This evidence points out the requirement for the business to rethink its wage
policies and pursue steps to fully eliminate the gap between men's and women's
salaries. Tackling this problem will guarantee justice while also enabling the
firm to draw in and keep a more varied workforce. A unique dashboard formed
as part of this analysis allows stakeholders to actively explore wage data using
an engaging method. Employers utilize this dashboard to check wage trends in
real-time and support the use of data to inform compensation strategies
effectively. To keep its compensation policies up to date and competitive the
organization analyzes wage data continuously through this dashboard.
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