From Philosophy to Data: A Career Shifter's Reflections on Demo Day

Hi, I am Vince, a hopeful career shifter into the field of data. I am a philosophy major, and I am interested in the intersection between data and philosophy. I have presented at philosophy conferences and taught undergraduate students in argumentation and logic. I love cats, dogs, and data in that order. Check out my LinkedIn profile here if you are interested in connecting with me!

Why Did I Shift Careers? 

After my thesis, I was faced with the greatest existential question given to fresh graduates -  “Where will I get my money?” When I mentioned the course I was taking to family, friends, and strangers, I always get questions such as “Why did you not go to law school?,” “When will you go to graduate school to teach?,” or my personal favorite, “Isn’t a philosophy degree just a road to unemployment?” With that said, this is how I felt going to job fairs:

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This is how employers saw my degree: 

After my inbox flooded with emails that started with “We regret to inform you that…,” I decided it was time to attempt a career change into something that is more palatable to employers but with skills that are based on my training in logic and are enjoyable to learn. This is when I found out about the Data Analytics Bootcamp by Eskwelabs through a fellow career shifter. After the extensive 9-week program where I learned industry tools such as SQL and Power BI, I was invited to be an industry apprentice after the bootcamp, as well as to attend the demo day of the Data Science Fellowship. And boy was I in for a treat.

What is Demo Day?

The Demo Day is the final presentation of the Data Science Fellowship participants. This last project gives learners the chance to share their work on projects that may be implemented for the betterment of the Filipino community. These projects are done with the guidance of data professionals to ensure the quality and relevance of their output. 

As a philosophy major, I was taught to ask questions and break them down into component parts using logic, then find a debate and argue for my philosophical position. 

Data is the same way, but with numbers and different mathematical models for analysis. I myself am just starting out as a data analyst, so while I found the projects extremely interesting and relevant, my brain cells looked like this during the discussion on the methodology: 

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Nevertheless, I found myself enjoying and learning so much about data science and its applications, and I would love to share these with you. Don’t worry if you need more help with the technical aspects because so do I, but here we are, trying to learn together. So here is my summary of the projects from the perspective of a philosophy major and a Data Analytics Bootcamp graduate. 

The projects below are from Data Science Fellowship Cohort 10 Fellows.

Group 1- Chavez, Gilay, Alawi, Nuqui, Tulsa 

Have you ever been stuck in traffic and imagined all the other productive things you could have been doing? Such as working for a living? Or petting your favorite furry animal? On net, the economic productivity loss totals Php 4.3 billion. Group 1 may have a solution to make that commute more tolerable and efficient. Their capstone project titled Data-Driven Insights for Local Government Traffic Management attempts to create more efficient pathways to alleviate traffic congestion. This can be done through the use of a dashboard to map out possible pain points in routes that lead to congestion due to the influx of vehicles. 

Key Insights 

• Traffic congestion leads to a large loss of economic productivity. 
• This can be alleviated by mapping out more efficient routes that can avoid overuse hence leaving more space for vehicles to maneuver. 

Group 2 - Bien Aculan, Iggy Franco, Rex Feuntes, Keith Yadao

This group’s project titled “Risk Forecast from Projected Precipitation in Manila in 2023” featured flood prediction and seasonal patterns to better prepare local government units to evacuate or be more aware of nearby places of interest in case of emergencies. The capstone project created a dashboard with different districts showing important landmarks such as hospitals and shelters near the area.

Key Insights 

• Data tools can be used to create predictive models for relevant events e.g. floods 
• These models can be used in conjunction with local government units for implementation.

Group 3 - Mike Arosolon, JA Chua, Andre Dometita, Chris Pantoja, and Tristan Tiu

This project was the most captivating one for me personally as their target audience are career shifters. And if you are interested in joining the bootcamp, then we’re probably in the same boat as well. This project is titled “The Data Journey: Archetypes of Data Professionals” and it creates archetypes to guide fellow shifters. They categorized the archetypes as the following: 

• The shifter, as the name implies, is someone who shifted out of their current roles. 
• The architects are those who specialize in data engineering roles and were already working in the field but changed trajectory later on.
• The purists are those who have formal education in a data field and have work that is in line with their educational training.
• The veteran are individuals who have no formal training in data but graduated in relevant fields such as physics and learned skills through work. 
• Lastly, the domain expert is a person with a high level of education but not specifically in data. 

These archetypes help data professionals and career shifters understand the journey and that there are different pathways to breaking into data. 

Key Findings

• Data is a fairly welcoming industry and there are many ways to break into the industry.
• People with diverse backgrounds are able to enter into the field. 
• Formal education of some sort whether Eskwelabs Bootcamps or a degree is likely necessary. 

Group 4: Div Alan, Sofia Calvo, Pim Cabanlit, R Didal, Chiara Perez

This team’s project titled “Let Local Lead: Improving Products and Increasing Awareness of PH Skin Care Brands” showed the potential of the skincare industry in the country is projected at Php 75 billion by 2026. The Philippines already makes up around 11% of the skincare market, thus there is a lost opportunity to improve the economy of the Philippines. From this, the team attempted to create a recommender engine to advocate for local products to increase the use of local brands in lieu of foreign brands. 

Key Findings 

• There is a clear missed market opportunity for local skincare products given that it is overshadowed by foreign skincare. 
• Local skincare companies need to compete with foreign skincare brands for resources while having fewer resources, hence supporting local brands may be important.

Group 5: Enrico Asuncion, Karen Bioy, Karla Conception, Andre Pardillo, Andres Saluta

This team’s project titled “Travelog: Harnessing the Power of Data for Business Decisions in the Hotel Industry” presented a guest experience classifier web app to understand the factors by which individuals rated a hotel experience. The rationale for this is to aid the hotel industry given that the COVID-19 pandemic caused this industry to struggle. 

Key Findings

• Staff, Room Location, and Facilities matter the most to guests.
• The room review score is not a determiner of room prices, this can be further explored for hotel staff to change prices accordingly to increase revenue.

How the Demo Day Changed My Perspective on Data 

When I imagined data before the bootcamp, I thought that data was merely numbers with theoretical uses. 

What I learned through the Demo Day and the Data Analytics Bootcamp is that data can be used for good to provide tangible solutions to real-world problems. 

This process is in fact similar to philosophy. All these projects seem to start with a question rather than a problem that needs to be solved using sound logic and tools. 

What I appreciate the most from this Demo Day are the judges. Other than being quite intimidating, they were also asking pertinent questions that pertain to data privacy, methodology, and applicability. So while the judges probably looked like this to the Fellows: 

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This is what they were like after the presentation:

This type of collaboration and reexamining biases and methods with the attitude of improving processes for the sake of finding the truth about problems with effective solutions is the heart of some philosophical thought. I still have a lot to learn to fully understand the statistical methods employed, but I am hopeful that with time, I will be able to understand and apply these to transition to a more technical data science role from my analyst one in the future. 

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What Now? 

My background in philosophy is atypical for a person in data, I do not have a STEM background either, so some procedural aspects are difficult to grasp at times. 

But what Eskwelabs has shown me is that data is a tangible way to provide solutions. When I feel the imposter syndrome creeping up in my mind telling me I am not fit to be in data, I just remember my favorite Pixar movie and its motto…

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And I have been living by this mantra ever since because I too know that anyone can be in data, and that includes you! 

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