Name: Priya Singh
Profile: Software Developer/ Data Scienist
Email: singhusha25632@gmail.com
Priya.Singh2@utdallas.edu
Current Location: Dallas, TX
Skills
Python 90%About me
I recently graduated from the University of Texas at Dallas with a Master of Science in Computer Science, achieving a GPA of 3.879. With over a year of hands-on experience in software development, I've adeptly utilized Python, Flask, C#, MySQL, Node.js, and JavaScript to design and implement various applications.
I worked as a Software Developer at The University Of Texas at Dallas from May 2023 to May 2024.
I have graduated in 2018 with a Bachelors of Science in Mathematics (Honors Degree)Bachelors of Science in Mathematics (Honors Degree) along with a minor in CS from University of Delhi, India.
I also have a year of experience working as a Product Developer and have done a 6-month Data Science Research Internship under Dr.Punita Saxena.
Other Programming Languages, Libraries & Tools:
C#.NET, PHP, Flask, Numpy, Pandas, Scikit-learn, AWS Lambda, pySpark, Hadoop, Matplotlib, Tensorflow, Keras, Regex, Git/GitHub, Postman, RESTful API
Courses and Certifications
What you focus on grows !
Projects
Design is not what it looks like and feels like, design is how it works - Steve Jobs
Development
NFT Trading Platform
NFT Trading app with features like buying, selling, and negotiation of NFTs including owner-driven sales mechanisms & automated ownership transfers. This was done as project in CS-6360 Database Design Class.
Development
Online Grocery Store
An e-commerce website for grocery featuring category-based shopping & user-friendly navigation along with an intuitive admin dashboard allowing administrators to efficiently add, modify, and monitor products.
Development
Nebula-X
Developed a course search application catering specifically to UT Dallas students. Integrated real-time professor ratings from RateMyProfessor website. This project was developed as a part of challenge during HackUTD 2022.
Development
Expense Tracker
Built an expense-tracker to streamline budgeting, track spending habits, and gain insights for better financial management.
ML
Sepsis Prediction
Constructed ML models- Naive Bayes, LR, Decision Trees, XGBoost, Neural Network, attaining F1 score 0.85. Addressed correlation, missing data, & class imbalance.
CV
Skin Disease Detection
Successfully implemented and CNN, DenseNet, ResNet, and MobileNet, achieving 96.84% training accuracy and 80.11% test accuracy, with MobileNet delivering the highest performance.
Prediction
Site Prediction
Won WeHack'23 by creating a predictive ML model for CBRE, optimizing commercial site selection using AI to evaluate target audience, demographics, and historically profitable locations within 24 hours.
Data Analytics
Netflix Dashboard
Built an interactive dashboard for Netflix to visualize TV shows & movies distribution by country, identify top 10 genres, & analyze viewing trends from year 2000-2020.
