Restricted Practical Project - Spring 2023 Course Codes

Restrictedpractical Project 2yearspring 2023course Codesec210course N

Develop a comprehensive MS Excel dashboard based on multiple external data sources related to your area of interest. The dashboard should visualize data through charts, graphs, PivotTables, and incorporate Excel’s Data Model along with dynamic features such as slicers. The project entails identifying relevant datasets, cleaning and preparing the data, creating a data model, designing an interactive dashboard, and producing a supporting video presentation. All files, including external workbooks stored in a Datasources folder, should be zipped in a specific naming convention and submitted by the deadline. Adherence to instructions regarding file naming, structure, and data visualization is essential. The project assesses your ability to manage data lifecycle stages, utilize Excel features for data analysis, and create user-friendly, interactive dashboards aligned with your study area.

Paper For Above instruction

The practical project is designed to assess students' proficiency in data handling, visualization, and dashboard creation within Microsoft Excel, focusing on their specific area of academic or professional interest. This comprehensive task involves multiple steps, beginning with the selection of a relevant domain, such as employment trends, health metrics, or environmental data. The choice of the area sets the foundation, enabling students to identify and source at least four to five external datasets that provide meaningful insights into their chosen topic. For instance, a student interested in labor market analysis would seek datasets on unemployment rates, employment-to-population ratios, industry-specific employment figures, wage growth, and job vacancies.

Once the datasets are selected, the critical phase of data cleaning begins. This process ensures data accuracy and consistency by removing duplicates, addressing missing values, standardizing data formats, and identifying outliers or errors. Data validation is crucial to maintain the integrity of the analysis, as inaccuracies can lead to misleading conclusions. After cleaning, students must import and link the datasets within a single Excel workbook, employing primary and foreign key principles to establish relationships via Excel’s Data Model feature. Proper linkage facilitates seamless data analysis and visualization, progressively constructing the backbone of the dashboard.

The core component of the project is dashboard creation. The dashboard must reside on a single worksheet, labeled as "Dashboard," and feature a clear, intuitive layout. Visualizations such as charts, graphs, and PivotTables should be carefully chosen to highlight key performance indicators (KPIs) and trends in the data. These visual elements must be interconnected through Excel slicers to enable interactive filtering, allowing users to explore data dimensions dynamically. The dashboard must be user-friendly, with an intuitive design guiding users to relevant insights swiftly, fulfilling criteria for clarity, interactivity, and accessibility. This step demands skillful integration of Excel’s visualization tools and data modeling capabilities to produce an impactful presentation.

Complementing the dashboard is a six-minute video presentation. The student must narrate the process, demonstrating each step—from data sourcing and cleaning to model creation and dashboard design—while sharing the screen to illustrate the work. This video serves as a reflection of understanding, technical proficiency, and communication skills, emphasizing the logical progression and key features of the project. The presentation should be comprehensive, covering all directives to maximize scoring potential.

The final submission comprises a ZIP file containing the Excel dashboard, external data files stored within a "Datasources" folder, and the video presentation. Files should follow strict naming conventions, such as "Dashboard_StudentID#" and "Presentation_StudentID#," and be uploaded by the specified deadline to avoid late penalties. The project is divided into assessment criteria including adherence to instructions, file structure, data importation, data cleaning, data modeling, dashboard design, and presentation quality. Each component is graded across performance levels, encouraging meticulous execution and demonstrating mastery of data visualization and management principles in Excel.

Throughout the project, students are reminded to maintain academic honesty, citing all external sources, including the use of AI tools like ChatGPT, appropriately. Mastery of this project demonstrates an integrated understanding of the data life cycle, technological tools in security contexts, and effective communication, all vital for careers in systems analysis, cybersecurity, or emergency management fields.

References

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