Day in the Life

March 5, 2025 In Day in the Life, Service, Social Impact

A Day in the Life as an FAO Schwarz Fellow at the Museum of Science, Boston

During my time as the FAO Schwarz Fellow at the Museum of Science in Boston, I have learned that no two days are ever the same. It is something I quite enjoy about working within an informal education setting. There is an exhilarating energy of finding out if I will be facilitating my favorite hands-on activities or if the Museum will be hosting a dance battle competition (which has happened)! The overview that follows is just one of many combinations of what my day as the FAO Schwarz Fellow can look like at the Museum of Science. 

Early morning (9:00 – 9:30):

Morning check-ins are crucial, on a personal and professional level. Just like breakfast is an important meal of the day, our team’s morning check-ins are just as important. Not just because it helps us know who is out for the day, but because it gives us time to connect as we chat about what we did after work the day before or express what we are excited about for the day!

Morning (9:30 – 12:30): 

As part of my direct service, I am regularly scheduled to do live presentations and facilitate hands-on activities throughout the Museum to engage and educate visitors. On this specific day, I was on the schedule for the morning In-Gallery Learning shift which consists of conducting hands-on activities in three of our gallery spaces. We usually start the shift off with a check-in meeting with our weekly volunteers and a fun question of the week! (This week was: “What’s your favorite type of donut?”) From there I spent my first hour in our Hall of Human Life encouraging visitors to use their knowledge of the human body and how their joints work to put together a mystery skeleton. My second hour of the shift was in our Engineering Design Workshop where visitors were challenged to make a miniature trampoline that makes a golf ball bounce as high or as low as possible.

Midday (1:00 – 1:30):

Lunch, of course! 

Afternoon (2:00 – 3:30):

Another part of my direct service is working with our partners at Fenway High School to host a cohort of Juniors who volunteer for an hour and a half every week at the Museum. This year, our students have been focused on exploring different careers within a museum setting. Additionally, they are involved in a volunteer project that is related to one of my special projects, High School Science Series (HSSS). This is a series of STEM related events throughout the school year that are free for high schools to attend. We are working with our Fenway High School volunteers to plan the Environmental Science HSSS in April 2025 so that there is youth influence and feedback involved in the process. This day the students worked to compile an extensive list of organizations that they thought would be a good fit and wanted to invite to the event in April.  

Late Afternoon (4:00 – 5:00):

Throughout the day, I like to take advantage of any open time I have in my schedule to complete any urgent tasks, this was one of those times. This can range from replying to emails, updating my current live presentation shows, clerical work related to upcoming projects or events, and more.

Overall, my day was quite exhilarating! I feel grateful for days like these as they feel productive and impactful in several ways, whether that is in one-off experiences with visitors or with the youth that come in seeking exciting opportunities every week.

 

Picture of Jocelyn Poste

Jocelyn Poste

Jocelyn (she/her) is the Youth Programs, Community Engagement Department FAO Schwarz Fellow at the Museum of Science in Boston.

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October 2, 2024 In Day in the Life, Special Projects

As Told by a Nonprofit Data Wrangler

Data wrangling is the conventional term for the process of transforming raw data into a usable form. Suppose your database stored date records as “YYYY-MM-DD.” If your analysis focused on the calendar month, extracting “MM” (and maybe converting it to an actual name) might be part of your analytical workflow. Though not holistically representative, that’s an easy example of data wrangling.

Many data analysts—in the interest of showing off their competency and technical prowess—paint the process as elegant, smooth, and consistent. Of course, those are the qualities stakeholders desire for the end product. In fast-paced, evidence-based settings, insights and reporting should not only be actionable, usable, and sustainable, but they should also come in pretty packaging.

"...data wrangling can be a fairly indirect process that lands you somewhere you don’t expect. The same can be said of your early professional journey."

The spaghetti code amassed between their PostgreSQL, Pandas, and Matplotlib workflows be damned! Those data scientists can make a good visualization.

As a junior data analyst over a year into the field, I take a lot of pride in producing accessible insights for dozens of different stakeholder groups. In the first half of my fellowship, this has ranged from facilitating market-wide data democratization workshops to running Social Work-focused data trainings to the ever-cliché PowerBI dashboard. Some of this data comes from my original consumer research with participants, but the bulk of it comes from a highly mature and curated system of data collection at Year Up United. If the first year was spent exploring, the second year has so far been spent taking action.

Thus, it becomes a game of making all that raw data usable for study.  According to my coworkers, it’s a game I’m good at. “Avery’s the guy for all things data,” some say! I want to say that the game’s always easy, too. Yet, Truth would come out of her well to shame the consumer insights analyst.

Frequently in my work, Truth does not come out the data well on her own—given the usual conditions of organizational data storage, she can’t. Unconventional formatting, inconsistent styles of data entry, and deprecated categories don’t make data wrangling easier. Rather, data specialists come at Truth, buried somewhere in over a hundred relational tables of data, with mining explosives. And though some data professionals will tell you that Excel is archaic and limited, sometimes Truth doesn’t start upward vertical movement until you break out PivotTables.

Overwrought metaphors aside, the following is a 0 to 1 example of my own analytical workflow, going from an ad hoc stakeholder request to end-product visualization. Keep in mind that this can vary from project to project. This account is slightly fictionalized! The following data, though, is all real:

1.

I get a request to pull and process data from a stakeholder. This might be someone higher up in my department (in this case, the Director of Program), or it might be a stakeholder from another functional team (like the Director of Student Services, Site Director, or even a national Growth & Strategy Lead). Here, Wilfrid Velazquez wants to explore some data regarding “firings” for a debrief meeting. “Firings” are Year Up United’s historical term for attrition—a participant leaving the program, whether intentionally, willingly, or by regulatory necessity. For the purposes of this demonstration, we’ll wrangle only two selections of data into a singular visualization.

2.

I write a relevant SQL query to pull the data from a relevant relational database. In this case, I’m using SOQL (a proprietary dialect of SQL) to rapidly export a table from Salesforce, a CRM (customer relationship management) Platform. I’m specifically looking for participants who have left program—that is, count as attrition.

3.

I export the resulting array from this query into a usable format. (Note: I’ve removed confidential, identifying information important to my typical workflow.)

4.

I load the resulting file (in this case a CSV, a comma-separated value file) into a data manipulation tool. For many, this can be as simple as plopping it into Excel! That is a totally valid avenue of explanation. In this specific case, however, I’m using “pandas”, a data analysis library written in Python (a general purpose programming language) within the program VSCode. If you have the prerequisite knowledge, you can perform the same actions as in Excel with extreme efficiency and relative ease. And all these resources are free!

5.

Some exploratory data analysis is good for you. In this case, I’ve calculated the relative percentages of Attrition Reasons (referred to here as “Reasons for Status Change” and historically referred to as “Firings”). These numbers should add up to approximately 100: that is, a whole population.

6.

It’s become an organizational priority to investigate how our programming may impact different demographics– including age. This analysis sees how many participants of a particular age ultimately left the program. You can definitely see the higher numbers here, but it might be a little difficult to see what’s happening just from this.

7.

Woah! It looks like 19 years take the lead in attrition. It’s much easier to discern from this bar graph. In a Statistics course, we might describe this distribution as a bit “right-skewed”—attritions lean towards younger participants!

8.

That’s all good, but let’s see how this stacks up against the general population distribution of age. That is: maybe this is just proportional to how many 19 year olds were in this particular cohort? Let’s export another CSV using SQL, this time incorporating the entire population regardless of program status. The difference here is that I haven’t specified for “Status__c = ‘Fired’”, which would otherwise limit me to Attrition data. We’re no longer looking at a specific sample: we are looking at the true population.

9.

I’ve now loaded the CSV into my Python environment. You might be curious what those “NaN” values mean for the general population. In its literal most meaning, NaN means “Not a Number.” Contextually, it’s a “null” value: there’s absolutely nothing there. It’s an utter blank space where data could go. There isn’t any attrition data associated with that particular individual’s file because the individual completed the program in full!

10.

I’ll use the pandas library’s native support for Matplotlib, just as I had for the Attrition data. Huh, seems like there’s an entirely different distribution—with the exception of a singular (incorrectly entered) outlier calculated as 0 years old at enrollment!

11.

We’re getting more advanced now. I’ve super-imposed the age distribution of Attrition against the General Population. 19 year-olds are uniquely overrepresented here! I’ve normalized the y-axis—in Statistics, that means I’ve transformed the data in such a way that it’s easier to compare the values presented. The practice of steps 8 to 11 are commonly referred to as “exploratory visualization.” I’m not making any definite claims yet, beyond an observable instance of correlation.

12.

And now we export and save as a “.png” image file. It’s now time to send our end-product to our stakeholder to see if they think this topic is worth further investigation. In a social impact context, such phenomena might indicate a gap in service or the presence of something particular to this age group. As in all statistical investigation—no matter how simple—it’s important to remember that correlation is not causation. Higher attrition correlates with 19 year olds in this particular cohort: there might be any number of factors actually causing this. Still, it’s worth flagging for our stakeholder!

13. 

Don’t forget to tell your stakeholder what exactly they’re looking at if you ever try this yourself! I don’t consider reports—even if they’re small, ad hoc things—to be one-and-done deals. Be ready to explain your process and what the body of data you’re working with actually is. Soft skills are part of data analytics—there’s always going to be someone with a follow-up question.

As you might gather from this 13-step process—whether data layperson or not—data wrangling and ad hoc reporting can be a fairly indirect process that lands you somewhere you don’t expect. The same can be said of your early professional journey, at any organization—whether for-profit or nonprofit, social impact-oriented or driven by sales. “Indirect” doesn’t have to mean bumpy, but many challenges are meant to be transformed into victories.

When I first came into my role at Year Up, I imagined myself as a predominantly qualitative researcher who could flex some mixed methods muscle. Now, I understand myself as an adept data analyst ready to tackle both qualitative and quantitative data to extract whatever story they might tell. As I reflect on my options following my two-year opportunity with the FAO Schwarz Fellowship, I find myself saying what I’ve said before: I don’t just get one “year up,” the fellowship has given me two.

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Avery Trinidad

Avery Trinidad (he/him) is the Research & Insights FAO Schwarz Fellow at Year Up in New York City.

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March 8, 2024 In Day in the Life, Social Impact, Special Projects

Talking with Kayla Johnson, FAO Schwarz Fellow at The Clay Studio

A second Fellow will join The Clay Studio in 2024. Thanks to the team at The Clay Studio for creating and sharing this video with us! 

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Kayla Johnson

Kayla (she/they) is the After-School Program Coordinator & FAO Schwarz Fellow at The Clay Studio in Philadelphia.

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March 6, 2024 In Day in the Life, Social Impact, Special Projects

Day in the Life: Teaching Artist

In my role as an FAO Fellow here at The Clay Studio in Philadelphia, I get to enjoy lots of variety in my day-to-day experience. My direct service work in the fellowship is working with Claymobile, our mobile engagement community program. The majority of this work is centered around teaching ceramic classes in schools, but I also get to teach in community centers, libraries, and farms. I began in this role acting as a teaching assistant, which means that I worked with the lead teacher in setting up the materials necessary for the class and supported students in their creative process. More recently, I have begun lead teaching in classrooms; it has been a wonderful process starting from having no knowledge about ceramics, to learning how to assist, then to leading a residency. Last week, I had my first class lead teaching at Steel Elementary. I started my day at the studio packing all of the necessary materials and then driving to the school. On arrival, we bring all of the materials to the classroom and introduce ourselves to students. 

Each day at work is different and I wouldn’t have it any other way!

As a lead teacher, one of the main components is teaching a demonstration of the ceramic project we will be completing. For this particular class, we made coil bowls which are made of small ceramic spirals using a plastic bowl as a mold. During my demonstration, I focused on the feeling of the clay as it was many student’s first experience with it. Teaching how to make a coil, how to smooth the inside of the bowl, and glazing are some of the main focuses for this project. After the demonstration, the rest of the class is spent moving around the classroom, checking in with students and answering any questions that arise. Many students shared ideas of projects they want to make in the future, what they might eat out of their bowls and share information about themselves and their families. The classroom teacher took pictures of each student with their finished bowls and we said goodbye, all in under two hours. 

After a Claymobile class, I’ll switch over to my special project work which is teaching in our new after-school program. The after-school program started last year, and we are now working with four schools in the area. After unpacking and taking lunch, I’ll set up our studio with the tools and demonstrations necessary for our class. Then, I walk to the school that we work with that day to pick up students at dismissal. After all of the students arrive, we walk to the studio together. Once we arrive, we have snacks and homework help which is necessary time for students to relax and connect with each other. Then we switch to studio time. Teaching in the after-school program can be similar to the style of Claymobile teaching, but as it is a smaller group who have had practice in multiple making techniques, it runs more independently. Some days we offer a full demonstration, others are free-making days where students choose what they want to make, but most days fall somewhere in between. This is a long-term program for students, so the curriculum is built particularly for the students from each school. Connecting with students on a weekly basis is one of my favorite parts of my job, and being able to provide a fun and safe space for students to make art and chat with friends. Working within the after-school program lends itself to a stable consistent schedule which is nice for me and Claymobile gives my schedule some flexibility and fun. Each day at work is different and I wouldn’t have it any other way!

Picture of Kayla Johnson

Kayla Johnson

Kayla (she/they) is the After-School Program Coordinator & FAO Schwarz Fellow at The Clay Studio in Philadelphia.

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Natalia works with children and their families at an event at the Museum of the City of New York
September 19, 2023 In Day in the Life, Service, Special Projects

A Day in the Life of a Fellow at MCNY

9:00-9:15 – Grounding 

  • I start my mornings by checking my email, responding to any messages, and planning my schedule for the day. I find this routine really grounding because I know exactly what I need to do and when to do it throughout the day.

 

9:15-9:45 – Field Trip Prep Time 

  • Most, if not every morning, I will be leading field trips for K-12 audiences as part of my direct service work. After I’ve grounded myself in my goals for the day, I will take time to prepare materials that students will engage with throughout the experience. Our field trip materials include a wide range of historical objects, iPads with videos, and arts and crafts materials. 
  • I will also review the information about the group to best tailor the experience to their needs. Sometimes the teacher let us know ahead of time that the students are studying a specific topic related to the gallery, so I will make sure to include that topic as a discussion point in the tour. 
  • I will often teach more than one field trip in a day during the academic year, so I will prep materials for my second field trip at this time if needed so that I am not rushing later on.

 

10:00 – 1:00 – Field Trips 

  • Depending on the time of year, I will teach one or two field trips a day. Each trip is an entirely different experience. Some of the variables that make each field trip unique include the student’s prior knowledge and interests, their grade level and previous museum experience, the gallery we are visiting, the time of day they visit, or even the weather. Developing my arsenal of teaching strategies has taken lots of practice, experimentation, and collaboration with other facilitators on the Education team. 

 

12:45 – 1:00 – Clean Up! 

  • Once all of my field trips for the day are done, I will put away materials that I took out for the day. I will also make note if we are running low on supplies and replenish them so that they’re prepared for the other museum educators who may need them.

 

1:00 – 2:00 – Lunch

  • When the weather is nice, I will eat lunch in Central Park! The Museum is across the street from the Conservatory Gardens, so I will often sit on a bench in that area. Now, having worked at the Museum for a year, it’s been really fascinating to see and learn how the gardeners change the landscape over the seasons. 

 

2:00 – 5:00 PM – Special Project Work Time 

  • In the afternoons I have dedicated time to work on my special projects. These projects change throughout the year depending on the upcoming programs and gallery rotations. Much of the special project work I do is collaborative, and I really appreciate the opportunities to work and learn from my colleagues!  
  • Field Trip co-development – One of the special projects I have been working on is co-developing a field trip for our upcoming exhibit People, Place and Influence: The Collection at 100. Part of this work includes selecting objects that students will interact with, identifying the types of engagements we will have students participate in, and selecting the main concepts we want students to take away from the field trip. 
  • FAO Foundation work – I will also work on projects for the FAO Foundation. The projects I have worked on this year include creating a graduation book to celebrate the second-year fellows upon the completion of their fellowship, preparing to present for prospective students, planning for the upcoming New York City retreat, and more.

 

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Natalia Wang

Natalia (she/they) is the FAO Schwarz Fellow at the Museum of the City of New York.

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