Your Task In This Assignment Is To Aggregate The Data Found ✓ Solved
Your task in this assignment is to aggregate the data found
Before You Begin this assignment will be saved to your Tableau Public account rather than GitHub. If you haven't already, be sure to create a Tableau Public account. The free tier of Tableau only lets you save to their public server. This means that each time you save your file it will be uploaded to your Tableau Public profile. You are able to load and continue working on the same workbook.
When you are finished with your assignment, you will turn in the URL to your Tableau Public workbook along with any additional files used for your analysis.
We will be analyzing different traits in both white and red wine such as alcohol content, sugar levels, acidity, color, etc. to gain a better understanding on how each contributes to the quality.
Task
Your task in this assignment is to aggregate the data found in the Wine Quality dataset from Kaggle. Analyzing different traits in both white and red wine such as alcohol content, sugar levels, acidity, color, etc. to gain a better understanding on how each contributes to the quality. Design 2-5 visualizations for each discovered phenomena (4-10 total).
Next, as a chronic over-achiever: Use your visualizations (does not have to be all of them) to design a dashboard for each phenomena. The dashboard should be accompanied with an analysis explaining why the phenomena may be occurring. A link to your Tableau Public workbook that includes: 4-10 Total "Phenomenon" Visualizations, A Dashboard, A text write up: analysis on the phenomenons you uncovered from the data.
Sharing Your Work
In order to share your work, we are asking that you will save your workbook as a .twbx file so that your TA's can grade them. To save your workbook as a .twbx file, you will just need to select "Save As..." from the "File" dropdown. Then, select the .twbx option.
Paper For Above Instructions
Wine has long been a staple at social gatherings, particularly during festive occasions like holiday parties. It enhances flavors and creates a delightful atmosphere. However, the challenge lies in selecting the right wine to complement different foods, thus making the pairing critical for a memorable experience. This assignment focuses on the quality of wine, specifically utilizing data analysis to understand the factors contributing to quality in both white and red wines using the Wine Quality dataset from Kaggle.
The Wine Quality dataset contains various features that can be analyzed to reveal insights into wine quality. These features include but are not limited to alcohol content, sugar levels, acidity, and color. The analysis aims to aggregate these data points and visualize them to discern patterns and relationships that affect the quality of wines.
Data Preparation
To begin, obtaining the Wine Quality dataset from Kaggle, which is freely available, is essential. The dataset features several attributes including fixed acidity, volatile acidity, citric acid, residual sugar, chlorides, free sulfur dioxide, total sulfur dioxide, density, pH, sulphates, alcohol content, and quality ratings. The preparation of this dataset includes cleaning the data to remove any null values or inconsistencies, ensuring that subsequent analyses lead to accurate conclusions.
After data cleaning, the next step is to explore the dataset through various visualizations. Utilizing Tableau, visualizations can easily be created to represent correlations and trends among the different attributes. These visual representations help in understanding the data better and also play a critical role in identifying phenomena related to wine quality.
Visualization Analysis
The first phase of the assignment involves creating 4-10 visualizations based on the identified phenomena. For instance, one can create a scatter plot to illustrate the relationship between alcohol content and quality. Such a plot may reveal that higher alcohol levels often correlate with higher quality ratings. Another viable visualization could be a bar chart comparing the average acidity levels with quality ratings across wine types, thereby establishing how acidity affects perceived quality differently between red and white wines.
Other visualizations might include line graphs showing changes in sugar levels versus quality ratings or histograms that depict the frequency distribution of wine qualities in accordance with characteristics like pH levels. Each visualization is designed to uncover unique insights into the roles these various traits play in assessing wine quality.
Dashboard Design
The next step is the creation of dashboards that compile these visualizations. A well-designed dashboard offers a comprehensive overview of the findings and enhances the interpretability of the data. Each dashboard should present key visualizations that correlate with a specific phenomenon. For example, one dashboard might focus on the correlation between alcohol content and overall quality, featuring relevant visualizations side by side for easier comprehension. This contextual layout not only facilitates exploration but also aids in drawing conclusions swiftly.
Accompanying each dashboard, a written analysis is crucial. The analysis should offer insights into why certain phenomena are observed, including explanations for trends noted through the visualizations. For instance, discussing the reasons why higher alcohol content often leads to higher quality ratings could involve exploring the characteristics of the grapes used or the fermentation processes that contribute to these alcohol levels in wines.
Final Submission
Upon completion, the final step involves packaging the workbook as a .twbx file for submission. This includes all visualizations, dashboards, and analyses created during the assignment. Sharing the URL of the Tableau Public workbook is necessary for reviewers to access the work and provide feedback. Ensuring that the visualizations are clear and the analyses are insightful will not only enhance the understanding of wine quality but also effectively showcase the work completed during this project.
In conclusion, this assignment emphasizes the importance of data analysis in understanding wine quality. By employing visualizations and interactive dashboards, we can glean significant insights into how various traits influence the quality of wines. It is an exciting avenue for both wine enthusiasts and professionals, providing a sophisticated approach to pairing wines with food for future gatherings.
References
- UCI Machine Learning Repository. (n.d.). Wine Quality Dataset.
- Boulton, R. (2001). The winemaker's answer book: Solutions to every problem; answers to every question. University of California Press.
- Jackson, R. S. (2020). Wine science: Principles and applications. Academic Press.
- Vinha, A. F., & Ferreira, D. A. (2019). Wine quality assessment: Taste and sensory evaluations versus chemical analyses. Beverages, 5(1), 18.
- Houlton, B. L. (2020). The importance of wine quality: A review. Wine Economics and Policy.
- García-Esteban, M. (2014). Quantitative analysis of wine as an engineering approach. Journal of Chemical Education.
- Tomasi, D., et al. (2008). One hundred years of wine research in the laboratory: After Pinot Noir. Acta Horticulturae.
- Terra, A. A. D., & Pino, H. R. (2012). Evaluating wine quality through sensory analysis and wine chemical composition. International Journal of Wine Research.
- Margalit, Y. (2021). The art of wine tasting and pairing. Wine Enthusiast Magazine.
- Parker, R. (2019). Wine advocacy: Understanding the market. Wine Business Monthly.