Maria Vaghani

Software Engineer with passion for design, automation and DRY code

About me

I started coding when I was taking a Structural Dynamics class, where we needed to write a software that would calculate stress and displacements at each node. This project fascinated me very much by its utility and beauty of execution, that I decided to continue learning programming post-graduation. I started learning Data Science and Machine Learning, when I quickly realized that I find solving complex problems via code more fulfilling than data analysis. That’s what led me to the app academy where I picked up competitive technologies and Computer Science foundations.

Skills

I am passionate about learning new technologies And here is what I know and could bring to the table

  • Ruby

  • Rails

  • React

  • AWS

  • Node js

  • Mongo DB

  • Postgre sql

  • Express js

  • JavaScript

  • jQuery

  • Sass

  • HTML5

  • CSS3

  • Git

  • Python

  • Numpy

Projects

Software Engineering

Project created to demonstrate my skills in programming and ability to create full stack application from scratch in a short period of time.

Docuflix

Github | Live

A pixel-perfect clone of a media streaming platform Netflix. Docuflix presents a hand-picked collection of documentaries, allows you to create your watch profile, save documentaries to your watch list, and search your favorite titles

Cipher

Github | Live

Collaborative IDE environment to solve DS&A problems by yourself or in a real time with a group of fellow engineers

Who Stole the Apple

Github | Live

An interactive and playful IDE environment where you could train your algorithmic thinking

Data Science and Machine Learning

In addition to having experience in software engineering, I also have a good command on writing Machine Learning algorithms and performing Data Analysis

Optimizing App Offers With Starbucks

Github | Article

Data Analysis of the Starbucks app offers, data cleaning to remove falsely classified offers, and Machine Learning model algorithm that predicts which offer should be sent to a specific customer to reach the highest return of investment.

Disaster Response Messages Classification NLP

Github | Live

Data Analysis of the Starbucks app offers, data cleaning to remove falsely classified offers, and Machine Learning model algorithm that predicts which offer should be sent to a specific customer to reach the highest return of investment.

Recommendations with IBM

Github

Data Analysis of interactions that users have with articles on the IBM Watson Studio platform, and recommendation algorithm that suggests which articles should be offered to users based on their previous iterations wiith the platform. Recommendations systems in the project explored: rank based recommendations, user-user Collaborative filtering, content based recommendations, and matrix factorization.

NYC AirBnB Price Prediction

Github

Data analysis of AirBnB prices in New York City and algorithm to train Machine Learning model based on location, proximity to the most notable landmarks and listing details

Resume

Download the latest version of my resume

Get in touch

Feel free to contact me to discuss my opportunities with your company!

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