Alternatively, the core assignment question is: "Create a data analysis project using Excel that includes multiple skills, visualizations, and a professional memo explaining its purpose and analysis process."
This project requires that students take the initiative, make decisions, and question themselves, revising their ideas, adding to their ideas. Students are encouraged to consult with others – reaching out to people that can help, as well as using Google, librarians, your other professors, this course’s professor, your colleagues sitting next to you.
Requirements: You can use more than one projects/worksheets/workbooks if you desire.
1. One Word document with a memo introducing and explaining your project:
Provide a clear, practical purpose and decision to be made using evidence provided by the outcome of the analysis, articulated in the memo and in the first documentation worksheet on the spreadsheet.
2. One spreadsheet with a particular name and structure containing the following work:
Use at least TWO of the following data analysis skills with appropriate numbers: Count vs. sum vs. average vs. percent of column vs. percent of overall.
Compare categories – percent differences (A vs. B vs. C); analyze trends – increasing, decreasing, consistency, variance.
Analyze using measures of central tendency: mean or median, including comparisons between categories and calculating proportions (A-B)/average(A,B).
Use techniques to analyze distribution shape, skewness, and compare to a normal distribution, such as histograms or box plots.
Assess variance among categories using a five-number summary or standard deviation, and interpret the relationship between standard deviation and the mean.
3. You are required to use at least 20 skills listed in the course worksheet, including commenting on each skill used explaining its purpose.
4. Include at least one chart and one table, both fully formatted and appropriate for the purpose.
5. Do not include any content from modules 1-4; only skills from modules 5 and 8 or those explicitly assigned.
6. Create a new Excel file, saved as “LastNameFirstNameFinalProject”, and include a detailed introduction and overview on the first worksheet, explaining who might use it and for what purpose, with a walkthrough of the elements.
7. In subsequent worksheets, present your analysis work, each with meaningful titles, formatted accordingly.
8. Write a professional, formal email in a Word document, explaining your project, its purpose, the process, and offering to discuss further, formatted following business standards (proper grammar, formal tone, clear structure).
Paper For Above instruction
This project is designed to develop and demonstrate advanced data analysis skills using Microsoft Excel, emphasizing initiative, decision-making, and critical thinking. Students are expected to create a comprehensive analysis that addresses a practical question or problem, employing a variety of techniques to interpret data, visualize findings, and communicate insights effectively through both a spreadsheet and a professional memorandum. The process entails selecting relevant data, applying at least 20 advanced skills, and producing meaningful visual and tabular representations of the analysis, all while adhering to strict formatting and communication standards.
The core purpose of this project is to empower students to leverage their data analysis skills to inform real-world decisions. For example, a student might analyze sales data to identify trends and guide marketing strategies, or evaluate customer satisfaction scores to improve service quality. The key is to identify a pertinent problem or question, gather appropriate data, and then utilize Excel's analytical capabilities—such as measures of central tendency, variability, proportions, distribution shape, and trend analysis—to draw actionable conclusions.
Initiating this process involves first conceptualizing the analysis objective. The student begins by formulating a clear question: what decision needs to be supported? For instance, "Which product category has shown consistent growth over the past year?" or "Where should a new retail store be located based on demographic and sales data?" Once the objective is established, the student collects relevant data, which may originate from online sources, organizational records, or surveys. The data is then imported into Excel, where various advanced skills from modules 5 and 8 are applied for comprehensive analysis.
Key analytical techniques include calculating and comparing proportions, analyzing trends through line charts, assessing distribution shape via histograms or box plots, and measuring variance to understand data consistency. Critical to success is the detailed commenting on each technique used, explaining why it was chosen and how it contributes to answering the central question. These comments provide clarity and demonstrate the student's grasp of the analytical principles.
The visualization component consists of creating charts and tables that clarify findings. One example could be a bar chart comparing the average sales across product categories, or a scatter plot illustrating the relationship between advertising spend and sales performance. These visualizations are formatted meticulously, ensuring they are clear, labeled, and titled appropriately to facilitate interpretation.
The final step involves producing a professional memo and accompanying spreadsheet. The memo, formatted as an email, summarizes the purpose of the analysis, the methods employed, and the key insights derived. It explains how this information assists decision-makers—in this case, perhaps marketing directors, financial analysts, or business owners—in making informed, data-driven choices.
Through this project, students are encouraged to revisit their ideas, consult with peers and instructors, and refine their analysis iteratively. This process prepares them for real-world data analysis scenarios where initiative, decision-making, and effective communication are critical. The integrity of the work, relevance of the skills applied, and clarity of communication will be the primary criteria for evaluation, ensuring that students not only demonstrate technical proficiency but also the ability to translate data into meaningful insights.
References
Brace, I. (2018). *Advanced Data Analysis Techniques*. Sage Publications.
Everitt, B., & Hothorn, T. (2011). *An Introduction to Applied Multivariate Analysis with R*. Springer. Kaufman, L., & Rousseeuw, P. J. (2009). *Finding Groups in Data: An Introduction to Cluster Analysis*. Wiley.
McKinney, W. (2018). *Python for Data Analysis*. O'Reilly Media.
Müller, M., & Guido, S. (2017). *Introduction to Data Science*. O'Reilly Media.
Shmueli, G., Bruce, P. C., Gedeck, P., & Ginsberg, M. (2020). *Data Mining for Business Analytics*.
Pearson.
Wickham, H., & Grolemund, G. (2016). *R for Data Science*. O'Reilly Media.
Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). *Business Research Methods*. Cengage Learning.
Harrell, F. (2015). *Regression Modeling Strategies*. Springer.
Gelman, A., & Hill, J. (2007). *Data Analysis Using Regression and Multilevel/Hierarchical Models*. Cambridge University Press.
