How To Do Data Analysis On Tableau Assignment

Introduction

The dataset are taken from school location and profile a complete perspective on the education sector, including an array of variables and elements that are crucial for understanding and assessing the educational environment. The dataset includes data pertaining to staffing patterns, school sectors, school types, campus types, student enrollments, and various demographic factors etc (Akhtar et al., 2020).The dataset provided below is a significant asset for educational administrators, policymakers, researchers, and other relevant stakeholders seeking to get an in-depth knowledge of the intricacies inherent in the education system. By using data analysis and visualization technologies like Tableau, this dataset provides an approach to convert unprocessed data into significant and practical findings. By participating in this practice, it facilitates the process of making decisions based on data, allocating resources effectively, and developing policies in the realm of education. Within this particular context, the dataset assumes a crucial role in enabling data-driven improvements within the field of education, thereby guaranteeing optimal learning environments for students, educators, and staff members.

The purpose of key visualizations is:

• Interactive map
• Bar chart
• Butterfly chart
• Scatterplot
• Pie-chart

1.1 Interactive Map

The collection includes comprehensive information pertaining to various educational institutions located in distinct geographical regions across Australia. In addition, it includes corresponding geolocation data, including latitude and longitude coordinates. The dataset includes schools that have been categorized based on their geographic location into distinct groups, are Inner Regional, Major Cities, Outer Regional, Remote, and Very Remote. Each category is associated with specific coordinates.

The dataset contains multiple educational institutions; nevertheless, for the purpose of analysis and interpretation a concentrate on a specific school. One example of a school is Hebel State School, which is located at a latitude of -28.971085 and a longitude of 147.790929 (Allison, 2023). The positioning of this school in multiple geographical categories, such as Inner Regional, Major Cities, Outer Regional, Remote, and Very Remote, implies that it caters to a heterogeneous array of areas characterized by different degrees of urbanization and availability of facilities.

1.2 Bar Chart

The offered data relates to educational establishments, particularly schools, located in the state of Queensland (QLD), Australia. The dataset includes three main columns, are "State," "School Type," and "School URL," along with a unique identifier referred to as "Acara Sml Id." It relates to educational establishments situated in the Australian state of Queensland offering valuable information. The dataset is organized in a tabular format, with each row representing a distinct school identifier referred to as "Acara Sml Id." The columns in the dataset provide information about the "State," "School Type," and "School URL" associated with each educational institution.

The column labelled "State" indicates the geographic placement of these educational establishments, namely within the jurisdiction of Queensland. This information plays a crucial role in comprehending the regional context of the institutions being evaluated.

The column plotted "School Type" categorizes the institutions according to their basic educational concentration or the range of educational levels they provide. This classification yields four separate categories, are "Combined," "Primary," "Secondary," and "Special." This analysis gives a comprehensive examination of the varied academic establishments that are contained within the dataset (Bakhuys Roozeboom et al., 2020). Within educational frameworks, "combined" schools are characterized by the inclusion of a wide range of educational levels, while "primary" schools primarily focus on the fundamental phases of education. Secondary schools often emphasize the academic development of students in higher grade levels, whereas special schools are intentionally structured to address the distinct needs or demands of particular student populations. The process of classifying the educational environment in the state of Queensland is improved through this classification.

1.3 Butterfly Chart

The information gathered from the data offers significant contributions to the understanding of personnel trends in various educational sectors, specifically those classified as "Catholic," "Governmental," and "Independent." (Batt, 2020). The rows of the dataset correspond to the "School Sector," while the columns contain the "Full-Time Equivalent Non-Teaching Staff" and "Full-Time Equivalent Teaching Staff." The dataset is structured in this manner. After analyzing the dataset, it becomes obvious that the personnel compositions of the three educational sectors—"Catholic," "Governmental," and "Independent—are remarkably comparable to one another. An estimated 26,494.9 full-time equivalent non-teaching personnel and 54,507.8 full-time equivalent teaching personnel are utilized in each sector. The consistency in employment levels across different sectors suggests that the workforce is relatively homogeneous, specifically in terms of non-teaching and teaching staff.

These statistics are of significance as they highlight the similarities in the employment of teaching and non-teaching personnel in the specified educational sectors. This observation suggests that these sectors exhibit a generally stable proportion of non-teaching to teaching staff in order to adequately address the educational requirements of their respective institutions.

1.4 Scatterplot

The dataset given gives an in-depth examination of staffing patterns across different educational sectors, concentrating primarily on the composition of full-time equivalent non-teaching and teaching staff members. The dataset displays an organized format wherein each row corresponds to a distinct educational institution and its respective attributes, while the columns serve to demarcate pertinent details, including "School Sector," "Year Range," "School Name," "Full Time Equivalent Non-Teaching Staff," and "Full Time Equivalent Teaching Staff."

The dataset within the Catholic school sector demonstrates a range of year levels, including "4-7," "5-12," "8-9," "8-10," "8-12," "9-12," "10-12," "Prep-5," "Prep-6," "Prep-7," "Prep-9," and "Prep-12." The staffing data related to this particular sector includes Full Time Equivalent Non-Teaching Staff and Full Time Equivalent Teaching Staff, which incorporates the workforce engaged in both administrative and instructional capacities. The dataset demonstrates a significant disparity in staffing numbers throughout the Year Ranges, highlighting the unique staffing requirements associated with various educational levels within the Catholic school sector.( Espi, 2019).

 The sector of schools operated by the government offers a variety of year ranges, such as "5-12," "6-12," "7-9," "7-10," "7-12," "8-12," "10-12," "Prep-7," "Prep-9," "Prep-10," "Prep-12," "U," and "U, Prep." Within this setting, the dataset provides information on the number of Full Time Equivalent Non-Teaching Staff and Full Time Equivalent Teaching Staff for each specific year range.

1.5 Pie-chart

In the "School Head Campus" category, the dataset includes enrollment for "Combined," "Primary," "Secondary," and "Special" schools. The student population at central campus or head schools spans a wide range of educational levels and learning contexts. Significant disparities in in general enrollments between these school categories reflect their different sizes and scopes (Hedin et al., 2021). "Combined" and "Primary" schools have the greatest student counts, demonstrating their broad educational reach, while "Secondary" schools focus on middle and high school education. "Special" schools for special needs pupils have a lower yet considerable enrollment.

The "School Single Entity" category includes "Combined," "Primary," "Secondary," and "Special" schools' student populations, yet they run separately. This group highlights the variability of single-entity schools' enrollment statistics. The high registrations in "Combined" and "Primary" schools show their popularity. "Secondary" schools have an extensive student physique, and "Special" schools educate a smaller yet significant population.

Conclusion

In conclusion the dataset provides insight into the various staffing structures within the education industry, with a particular focus on the makeup of Full Time Equivalent Non-Teaching and Teaching Staff across various school sectors and Year Ranges (Milligan et al., 2022). The data shows the significant variation in staffing needs, which is influenced by the distinct demands of different educational levels and the services offered by Catholic, Government, and Independent schools. These findings have the potential to guide resource allocation, policy-making, and workforce planning, ultimately leading to the enhancement of educational services in line with the distinct requirements of different school sectors and Year Ranges.

Reference

Akhtar, N., Tabassum, N., Perwej, A., & Perwej, Y. (2020). Data analytics and visualization using Tableau utilitarian for COVID-19 (Coronavirus). Global Journal of Engineering and Technology Advances.

Allison, J. (2023). Fragmentation or focus? The precarious nature of initial teacher training within the english further education sector. Practice , 5 (1), 27-40.

Bakhuys Roozeboom, M. C., Schelvis, R., Houtman, I. L., Wiezer, N. M., & Bongers, P. M. (2020). Decreasing employees’ work stress by a participatory, organizational level work stress prevention approach: a multiple-case study in primary education. BMC Public Health , 20 (1), 1-16.

Batt, S., Grealis, T., Harmon, O., & Tomolonis, P. (2020). Learning Tableau: A data visualization tool. The Journal of Economic Education, 51(3-4), 317-328.

Espi, G., Francis, D., & Valodia, I. (2019). Gender inequality in the South African labour market: Insights from the Employment Equity Act data. Agenda , 33 (4), 44-61.

Hedin, F., Konstantinou, M., & Cosma, A. (2021). Data integration and visualization techniques for post‐cytometric analysis of complex datasets. Cytometry Part A, 99(9), 930-938.

Loth, A. (2019). Visual analytics with Tableau. John Wiley & Sons.

Milligan, J. N., Hutchinson, B., Tossell, M., & Andreoli, R. (2022). Learning Tableau 2022: Create effective data visualizations, build interactive visual analytics, and improve your data storytelling capabilities. Packt Publishing Ltd.

O'Brien, A. D., & Stone, D. N. (2020). Yes, you can import, analyze, and create dashboards and storyboards in Tableau! The GBI case. Journal of Emerging Technologies in Accounting, 17(1), 21-31.

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