Data Visualization Model Report

Posted on: 14th May 2023


In this week, you continue to play the role of an intern at Nutri Mondo, an organization that uses data science to address issues related to food insecurities and other food-related issues. Read the message from the director of Nutri Mondo, Susana Maciel, to set the context for your assignment.


You have been doing a great job of keeping me up to date on this project. I really think you have a knack for this! I sure hope you have enjoyed your internship. I will need to lean on you a bit more at this next stage of the project. I would like your views on how we should deploy the visualizations created by the data science team with the departments who will be using them in their work with communities. The team will give you some options, and I would like you to let me know what you think. Whatever we decide, make sure the team covers how they use feedback to improve their visualizations. This is where our local teams’ understanding of how we work with communities can educate our data scientists on how we can apply what they are providing to us.

It has been a real pleasure working with you on this project, and I hope you found it fruitful. Who knows, after you get your certificate, maybe you will come work for us! I look forward to reading your final reports.

Best wishes,

Susana Maciel
Nutri Mondo

To prepare for this assignment, review this week’s Learning Resources. Then write a report for your director to provide the following:
Explain the different ways that the data science team at Nutri Mondo could deploy what they have found in the data?
If the decision were yours to decide, explain how you would deploy the data. You may combine or edit the options presented Nutri Mondo for you answer. Explain your reasoning.
Summarize the feedback the data science team are receiving from others in the organization. Include how the feedback is providing insights for the data science team to refine their model.
Your report should be 4–6 paragraphs in length.

The World of Data Science Miami is a link.

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Data Visualization Model Report

Nitro Mondo is an organization that is more determined in implementing modern data science strategies to address the ever-existing challenges associated with food insecurities and other problems related to food distribution. Therefore, it is important to recognize the importance of data science in Nitro Mondo's ability to handle food insecurity concerns. As a result, the organization's data scientists may use data visualizations such as maps, infographics, and dashboards to guarantee that people are aware of the variables contributing to food insecurity (Claes et al., 2018). More significantly, this data visualization model will enable the science team to collaborate with other data suppliers to get new insights about data deployment. As a result, the IT department and other staff have played a bigger role in the model's effectiveness.

Being one of the workforces has allowed me to utilize my academic knowledge in a real word situation. This report will discuss ways that the data science team can effectively put the model in action and ensure that the organization's clients are satisfied. Also, I will review the community's feedback regarding the model and evaluate how their feedback positively impacts the perfection of the visualization model. I used questionnaires, interviews, observation and revisited the feedback desk to gather the required information for the report. Questionnaires were filled by the organization's workforce and made observations on how the model impacted the organization's operations. I also held open interviews with the data science team, questioned the organization's partners, and reviewed the community's feedback and comments on refining the model for a better experience.

Specifically, I would use this data visualization model to assist current projects by collaborating with comparable organizations. In addition, I'd also use data visualization to disseminate information on Nitro Mondo's role in alleviating food insecurity in the community to all partners and stakeholders. The reasoning for choosing this model is to examine how global patterns and data transfer to the community level, guaranteeing that Nitro Mondo reaps the advantages of the chosen data rollout method (Po et al., 2020). Furthermore, the data deployment technique chosen would enable Nitro Mondo to analyze more states across the United States. Moreover, using this technique, Nitro Mondo will determine states that have similar data science capabilities; hence collaborating these states with would be critical in discovering new concepts that the organization can apply to address the food insecurity concerns.

According to the feedback, places like New Mexico, Texas, and Arizona, are enthusiastic about Nitro Mondo's initiatives. Obesity, food insecurity, poverty, and diabetes affect children and adults differently in various regions. Apart from these difficulties, these regions are influenced by a variety of additional variables. As a result, Nitro Mondo will identify how these challenges influence the community by distributing data. Nitro Mondo can utilize the input to assess the efforts required to harmonize the current statistics via community engagement campaigns and education programs. The feedback additionally reveals many strategies for improving Nitro Mondo's effectiveness (Xu et al., 2021). A representative from the organization should show how to interpret and analyze statistics in the data visualization model, such as generating charts and maps to show various initiatives being executed by the organization.  The organization could also compare the data to existing trends and patterns.



Claes, S., Coenen, J., & Moere, A. V. (2018, September). Conveying a civic issue through data via spatially distributed public visualization and polling displays. In Proceedings of the 10th Nordic Conference on Human-Computer Interaction (pp. 597-608).

Po, L., Bikakis, N., Desimoni, F., & Papastefanatos, G. (2020). Linked data visualization: techniques, tools, and big data. Synthesis Lectures on Semantic Web: Theory and Technology10(1), 1-157.

Xu, L., Francisco, A., Taylor, J. E., & Mohammadi, N. (2021). Urban Energy Data Visualization and Management: Evaluating Community-Scale Eco-Feedback Approaches. Journal of Management in Engineering37(2), 04020111.

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