Sonam Rinjin Sherpa
Master of Science in Data Science
Regis University
Expected Graduation: Spring 2026
Denver,CO
Master of Science in Data Science
Regis University
Expected Graduation: Spring 2026
Denver,CO
I am a 24-year-old graduate student currently pursuing a Master’s degree in Data Science at Regis University. I enjoy learning new things and pushing myself to grow both academically and personally. I am naturally curious and like understanding how systems work, solving problems, and building skills that will help me in my future career.
Outside of academics, I enjoy content creation, music, art, and going to the gym. I like creating and sharing ideas, whether it is through music or other creative projects. Staying active and maintaining a disciplined routine is also important to me, and the gym helps me stay focused and balanced. My goal is to continue developing my skills, build a meaningful career, and grow into someone who can make a positive impact in both professional and creative spaces.
Programming Languages
Python, SQL, JavaScript, R, C, C++, PHP
Tools & Frameworks
RapidMiner, Github, RStudio, DBMongo Compass, Powerpoint, Excel, Word, NumPy, Pandas, Matplotlib, Scikit-Learn, NLTK
Machine Learning
TensorFlow, PyTorch, Scikit-learn, Keras
Web Development
Django, Node.js
This practicum project looks in the development of a mood=aware music recommendation system using publicly available spotify datasets.With the exponential increase in the growth of music streaming platforms, listeners can choose from hundreds and millions of songs, which makes discovering new music relevant to taste, context, and mood. In this project, a content-based recommendation approach is developed using numerical audio features extracted from Spotify datasets. Features such as danceability, energy, tempo, loudness, and valence, which describe the musical properties and emotion of songs. Each track is represented as a feature vector, and similarity between songs is calculated using cosine similarity and nearest neighbor techniques. The system retrieves songs with similar acoustic characteristics and generates recommendations accordingly.
Feel free to reach out for collaboration opportunities, questions about my projects, or just to connect!
I'm always open to discussing new projects, creative ideas, or opportunities to be part of your vision.