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

Assignment: Working with Feedback

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.

SUBJECT: REPORTS

FROM: [email protected]

TO: YOU

Hello,

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

Director

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.

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The data science team decided to deploy its models and

visualizations to regional offices in the U.S. as well as to two national

directors in São Paolo, Brazil, and Mexico City, Mexico. After

deploying the models, the data science team reviews the e-mail

feedback it received from different teams.

Email 1 of 4

From: Rogelio Nirenberg

Regional Director of Education Programming and Outreach

Southwest Regions

San Antonio, TX

Dear Susana and Jonathan,

First of all, thank you for the opportunity to have our office review the models you created. Our

team definitely sees this information’s utility, and we have already started discussing how this

data relates to our current projects here. So here is what we found to be the most useful:

• Being able to see how national trends and data translate to the local level was a great

benefit. The states where we have programs (Texas, New Mexico, and Arizona) all have

very different rates of poverty, diabetes, and obesity in adults and children, just to name a

few. And the information at the county level helps us understand with greater clarity. Right

now, we are looking at how our current efforts in education programs and outreach align

with the data we have now.

• We really got into comparing some of our counties and states with others in the U.S. to

see who our “peers” are, data-wise. I would like to be able to partner with similar areas

across the nation so that we can learn from each other a bit more.

Here is what we would like to see in the way of improvements:

• We would love for Karen or Jonathan to come to town and show us how to play with the

data sets they created so that we can easily create charts for specific outreach to our

elementary school programs and city and county governments.

• We would love it if Nutri Mondo could compare this data with more current trends so that

we could project what might be happening to our areas in 5–10 years.

Thanks for all your work on this!

Email 2 of 4

From: Angela Watkins

Regional Director of Education Programming and Outreach

Southeast Regions

Atlanta, GA

Hi Susana and Jonathan,

You asked for our feedback by today, so here we go:

We were aware of Georgia’s ranking in terms of health-related diseases, but it is excellent to

have government, public data organized in this way. Our outreach teams have already started

drafting press releases so that our local media markets can focus more attention on the issues

these models present. So here are what we like:

• The visualizations you created are not static. We can drill down into them to get more

specific

• We need more of this! We are looking to train someone here to use Tableau and other

tools, so we can crunch some of these numbers, too. Will your team be able to help us?

• This has brought up some a real question for us here in Georgia and the Southeast

region. How should we divide our resources on education activities that teach nutrition

and cooking healthy food versus the advocacy efforts to bring fresh food closer to the

populations that need it most in our region? We will be exploring this further, and this data

supports that effort.

What we think could be improved:

• Some of the visualizations are really small, and we wish we could easily enlarge some of

them.

• Some of our staff had challenges understanding why the percentages are presented the

way they are. Can that be clarified?

• We would like a visit from the team (or I can go to São Paolo) to walk us through how to

present this in different ways. Here in Georgia, we would like to recreate some of your

mapping—but at the county level rather than the state level.

• One issue that you do not explore in these visualizations is access to electricity and gas

for cooking. For our populations living with poverty, they cannot always pay for electricity

or gas and their power eventually will be turned off. When this happens, they rely on

processed or canned foods because fresh food will spoil and they can’t use a refrigerator

or an oven. Being able to see this issue in the data might change how we approach the

issue of healthy eating in some of our communities.

Email 3 of 4

From: Javier Ochoa Guardado

National Director

Mexico City, Mexico

Hola Susana and Jonathan,

Thank you for sharing these models with me. I shared them with some of the staff here, and they

would definitely appreciate it if we could pursue a similar project here for Mexico. We need to

have national and local data here to help us visualize our challenges with diet-related issues and

access to fresh food. Mexico has one of the highest rates of adult obesity in the world. Here is

some of the feedback we have:

• Without good data sets here to compare them too, it will be difficult to know how similar or

different things are here in Mexico with what is happening in the U.S. Certainly, we might

expect our communities close to the U.S. border to share some more similarities with communities in California, Arizona, New Mexico, and Texas.

• We would like to know how the regional offices in the U.S. might use this to support their

program and outreach decisions. I think once we begin implementing programs based on

the data here, then how those programs work would be of more interest to us.

Email 4 of 4

Email by Gregor Cresnar from the Noun Project

Conversation by Adam Stevenson from the Noun Project

From: Ana Julia Pitanga da Silva

National Director

São Paolo, Brazil

Hello Jonathan and Susan,

You are probably aware that here in Brasil, we have seen an alarming rise in obesity and the

other health-related issues that come with it: hypertension, diabetes, and heart disease. Poverty

has been a problem here in Brasil as well, and, historically, we are accustomed to seeing health

issues related to under-nourished citizens and malnutrition. However, as personal incomes have

really improved here nationally over the last decades, we are now seeing diet-related issues

like diabetes and obesity that seem similar to what your data is showing for the United Stated.

And so, we have this odd combination of under-nourishment from a lack of income to buy food,

along with an increase in diet-related issues related to an abundance of unhealthy eating habits.

I commend this effort to present data models that help explain some of the issues. So here is my

feedback:

• Based on the issues presented in the data here, I would like to compare our program

efforts with what is happening in the U.S. How have classes helped the families in your

communities address diet and nutrition? We are really dealing with similar issues and will

likely learn from each other.

• I would love to explore how migration has changed diets. We have many communities in

São Paolo that have moved here from the countryside in this past generation. The people

used to have gardens in the front yard, but now they spend too much time at work or

commuting. They just don’t have the time to get fresh food like they used to. Has internal

migration in the U.S. created similar issues?