The #IAMRemarkable workshop challenged me to think more intentionally about how I talk about my accomplishments. In this post, I’m reflecting on six strengths and experiences I want employers to know about me.
11-minute readIn this post
- #IAMRemarkable Workshop
- Courage to Change Direction
- Continuous Learning
- Teamwork and Collaboration
- Clear Communication
- Neural Networks in Practice
- End-to-End Project Development
- Keeping the Momentum
#IAMRemarkable Workshop
It was a long two-plus hours to Southfield, but luckily I had my chauffeur drive me through the rain. The #IAMRemarkable workshop, part of the MCWT Reignite! program, was well worth the trip.
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| #IAMRemarkable Logo |
We also talked about bias and how difficult it can be to share your accomplishments when you’re worried about being belittled or dismissed. Joy and Mukta from #IAMRemarkable explained how stereotypes can influence whose abilities we recognize and who gets credit for their contributions. Bias doesn’t have to be intentional to have an impact.
Being mindful of bias also means looking at ourselves. We all have assumptions we need to recognize and question. One suggestion from the workshop was to ask, “What do you mean by that?” Asking someone to explain their comment can encourage them to pause and reflect on the assumptions behind it. It’s a small way to start a conversation and support someone whose accomplishments are being dismissed.
During the workshop, we filled a page with statements beginning with “I am remarkable because…” I enjoyed this activity because it gave us time to recognize accomplishments we might otherwise overlook. Reflecting on what we’ve done and learning to share it takes practice. Even though the workshop ran late, I loved hearing everyone’s statements. I left inspired by their stories and ready to keep working on this skill myself.
To keep practicing, I’m bringing that exercise into this post. These are six things I want employers to know about me, along with the experiences and projects that show what I can contribute.
Courage to Change Direction
I am remarkable because I had the courage to pursue a new career. I grew up loving math and being in the classroom, so pursuing teaching felt natural. Along the way, I developed confidence speaking in front of people, stronger time management skills, and a deeper understanding of mathematics.
After three long-term substitute positions, I found my first full-time teaching role in November. I stepped into an Algebra II classroom after the previous teacher left “to go sailing,” with little guidance about what students had already covered. I had to figure things out as I went. It was a rocky start, but I enjoyed teaching that class. The student I remember most struggled with the concepts but kept putting in the effort. He came after school almost every day and earned an A on the final. I was so proud of him.
Unfortunately, the school was downsizing, and as the newest teacher, my position was cut. I transferred to another school in the district to teach Geometry. With the summer to prepare, I had high hopes. However, accepting an extra class meant teaching around 200 students and losing my prep period every other day. I was working 10 to 12-hour days and weekends to keep up. I cared about my students and wanted to meet my own standards as a teacher, but the workload became unsustainable, and I burned out.
After a break and a move to Michigan, I became a tutor. I could continue helping students learn without the demands of managing a full classroom. After several years without opportunities to advance, I was ready for another change. This time, I took a risk and pursued data science, bringing my love of math, graphs, and problem-solving into a different kind of work.
For me, courage has meant recognizing when I needed a different path and being willing to pursue it. My experience in education still shapes how I learn, communicate, and approach challenges. I’m bringing those strengths into data science with eagerness for what comes next.
Continuous Learning
I am remarkable because I am always growing and learning. I have always enjoyed being a student, and the UTeach program reinforced the importance of continuing to learn throughout my career. As a teacher, summers included professional development and opportunities to strengthen my knowledge. That mindset has followed me into data science.
Right now, I’m focusing on clean code and testing as I develop my software engineering skills. I’m applying those lessons to my hospital readmissions project, including testing the ETL process to verify data integrity. Studying these practices while using them in a project gives me a clearer understanding of why they matter. I’m also more confident writing modular Python and working with APIs than I was when I started.
I recently completed the fundamentals course in the Google Business Intelligence Professional Certificate. One takeaway was the importance of asking SMART questions to understand stakeholder needs and guide the work. I chose BI because I wanted to strengthen the connection between technical analysis and business decisions. It complements my data science background while giving me another possible career direction to explore.
Choosing what to learn next can be difficult, so I use employer feedback, recommended books, and what I see other professionals doing on LinkedIn to identify useful areas to study. For example, employer feedback pointed me toward plotnine, a Python data visualization library, as another tool to explore. When I encounter something unfamiliar, I often start with AI or YouTube for an introduction, then follow up with deeper study and practice when it fits my goals.
I want that learning to continue after I find my first position. I look forward to learning from coworkers and mentors who can share their practical experience and help me develop better judgment. Eventually, I would also like to pursue a master’s degree in data science part time, ideally after gaining a year or two of professional experience. Over time, I hope to grow into leadership and help others develop their skills.
Learning is an adventure of discovery for me. There is something rewarding about reaching the point where I truly understand an idea, can use it, and can explain it to someone else. That feeling keeps me curious and gives me a reason to keep going.
Teamwork and Collaboration
I am remarkable because I work well with others. Throughout my working life, I have collaborated with customers, coworkers, students, and classmates. Each experience has helped me become a better listener, understand different perspectives, and find solutions that work for the people involved.
In customer service, I started by helping callers resolve problems. As I took on floor support and assistant team lead responsibilities, I also answered teammates’ questions and handled escalated calls. When someone was upset or exhausted, I first tried to understand the situation. I reviewed their account, checked what had already been attempted, and explored the available options. Putting myself in their shoes helped me explain those options and work toward a solution that made sense for both the customer and the company.
As a teacher, collaboration meant sharing materials, dividing responsibilities, and coordinating lesson plans. After studying project-based instruction, I proposed a geometry project where students would connect geometric terms to real-life objects. My goal was to make the concepts more concrete and give students a fun way to apply what they had learned. My colleagues and I worked together to develop the plan and grading rubric.
At CMU, my Applied Analytics II team analyzed wine quality using regression and classification methods. We divided the work by modeling approach, keeping related tasks together so each person could focus on a coherent part of the project. We also met regularly to check progress, help one another, and stay accountable for our contributions. That coordination helped us complete the final project and presentation, earning an A.
My approach to teamwork starts with listening, understanding what needs to be done, and helping the group make a plan. I’m comfortable taking the lead when needed and learning from someone else’s ideas. I want everyone to have a fair opportunity to contribute and feel that their work is valued.
Clear Communication
I am remarkable because I can explain complex ideas clearly. I first began developing that skill in high school, when I helped a friend with precalculus homework and prepared with her for exams. Working through her questions taught me that sometimes an idea needs to be explained in more than one way before it clicks.
My first customer service job gave me a chance to build on that skill in a very different setting. I learned how to connect with strangers, understand what they needed, and explain their options in a way that made sense to them. Those conversations made me more confident adapting how I communicated to different people, a skill I later carried with me when I went to college to become a teacher.
In the UTeach program, I remember an activity where my professor acted as a student struggling with an algebra problem. I asked her to explain her thinking as she worked through the steps, using guided questions to find where the confusion began. Her positive feedback focused on how I helped the student identify that point of confusion and work through it.
That approach still shapes how I explain things. I try to meet people where they are, use familiar terms, and follow a logical path through the problem. If an explanation isn’t working, I try a simpler example. I also want people to feel comfortable expressing confusion. That might mean giving someone time when they are frustrated or addressing a question privately when they feel embarrassed.
In data science, class presentations gave me practice explaining both how I approached a project and why I made particular decisions. My blog gives me another opportunity to develop this skill. Writing helps me reflect on what I’ve learned and organize my thoughts clearly enough to share them with someone else.
The same project may need a different explanation for a teammate, a stakeholder, or a friend. Understanding where someone is coming from helps me decide what context they need and which details will be useful. I want people to feel comfortable asking questions and leave with an understanding they can use.
Neural Networks in Practice
I am remarkable because I have experience building neural networks. I first became interested in them during my introductory Python class, when my instructor showed me the Keras website and its examples. I also watched 3Blue1Brown’s neural network series to understand backpropagation, where my calculus background helped. I found the topic fascinating and spent three days exploring it before putting that knowledge into practice at HackDearborn (Github).
During the 24-hour hackathon, I adapted a Keras cats-and-dogs example into a convolutional neural network for “Is It a Car?” Using approximately 17,000 images from Kaggle, I trained the model to distinguish between vehicles and non-vehicles. I worked in Google Colab and also helped my teammate get started with Python.
I divided the data into training, validation, and test sets using a 70/15/15 split. The validation set helped me tune the model and monitor for overfitting before evaluating it on the held-out test set, where it achieved 98.7% accuracy. Training also made computing constraints very real: I hit Colab’s usage limits twice while learning and rerunning experiments. I had to think about the time and resources each training run required.
The project also showed me a gap in what I had built. My model worked in a notebook, but a judge pointed out that I didn’t have a proper demonstration. An interface where someone could upload an image would have made the project easier to explore. That feedback gave me something concrete to improve in future projects.
The following semester, Applied Analytics II took me deeper into the mathematics of neural networks. We even worked through some calculations by hand. I learned more about padding and pooling, encountered recurrent neural networks for the first time, and applied the concepts in labs.
I also used a neural network in our wine-quality project, where it performed worse than the other approaches we explored, including Lasso and Ridge regressions, random forest, and support vector machines. For that problem, the added complexity did not produce better predictions. It was a useful reminder to compare models and let the results guide the choice.
I now understand the components of CNNs and RNNs and have experience applying neural networks in Python and R. What I’m most proud of is my willingness to tackle a difficult topic independently, work through the mathematics and code, and learn from both the results and the limitations.
End-to-End Project Development
I am remarkable because I build end-to-end projects. My experience at HackDearborn showed me how much work remains between building a model in a notebook and creating something other people can explore. Since then, I have focused on connecting the different stages of a project, from the initial question to a finished report or application.
My individual COVID-19 capstone (Blog) (GitHub) brought together the full data science process. Using Mexican government data, I explored how demographic factors, health behaviors, and pre-existing conditions were associated with severe outcomes across age groups. I also compared logistic regression and random forest models. My biggest accomplishment was creating a single R Markdown document that handled data cleaning, exploratory analysis, modeling, evaluation, and generation of a clean Word report.
One of the hardest parts was deciding how much exploratory work belonged in that report. I had to think carefully about what readers needed to understand the findings. After presenting to three professors and my graduating class, I used feedback from my presentation to improve the final report. I’ve written more about the project in the blog post linked above.
What’s UP Outdoors (Blog)(GitHub) gave me a different kind of experience: building something I could personally use to find hiking trails. It pushed me further into ETL, modular programming, and application development. When our plans changed from a four-day Upper Peninsula trip to a one-day Lower Peninsula trip, I was able to adapt the pipeline and refresh the data within a few minutes. Having that process in place made the project much easier to change when my needs changed.
Building with Streamlit also made me think about how an application responds to user interactions and how to make my work accessible to others. I enjoyed applying those ideas again in Zombie Carlo Simulator (Blog)(GitHub), where users could explore the results through interactive controls and graphs.
I’m now beginning my hospital readmissions project (Github), exploring whether we can predict readmission within 30 days among patients with diabetes. I’m using it to practice the full analytical process in Python while continuing to develop modular code and add tests.
To keep these projects manageable, I set goals at the beginning and focus on a minimum viable product before adding more features. Each project gives me practice connecting the question, data, analysis, and final deliverable into something that serves a purpose.
Keeping the Momentum
Saying “I am remarkable because…” felt a little awkward at first. While others shared major accomplishments and personal growth stories, I started with “I build neural networks.” It felt a little like I was writing a list of accomplishments on a chalkboard instead of actually telling my story. Hearing everyone else’s statements encouraged me to dig deeper. Writing this post gave me a chance to think about the experiences behind those accomplishments and why they matter. It’s not bragging if it’s based on facts.
My blog already helps me continue that reflection (Why I started this blog). Writing each week makes me think about what I’ve learned, the small steps I’ve taken, and how I’ve grown. I also want to keep an achievement notebook where I can capture things as they happen. Learning a new tool or stepping into a leadership role on a group project might become something worth exploring in a future post, interview, or performance review.
Sharing my accomplishments with recruiters is still something I’m practicing as I make the transition from teaching to data science. Continuing to review my statements and say them out loud will help me become more comfortable explaining what I bring to a role. I also love the idea of Remarkable Wednesday and would like to share an accomplishment in our WhatsApp group.
I want to keep advocating for others, too. That means listening to their stories, recognizing their contributions, and helping them feel comfortable speaking up. Being seen starts with having someone willing to hear you and acknowledge the work you’ve done.
I hope this post encourages you to recognize something you’ve accomplished, including the small things you might usually overlook. Give yourself credit, practice sharing your story, and bring attention to someone else’s good work along the way.
It was great having you in my class. You are an outstanding student who is enthusiastic, hardworking, responsible, self-motivated, and engaging.
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