I am a third year Computer Science Student at NUIG with a passion for Web development, problem solving and all things Tech.
I really enjoy my course and will graduate in 2020, hopefully with a first class honors degree.
My main experience is with the Mongo / Express / Node.js stack from personal projects and working with API's during my internship at BIMlauncher.
I've learned Java, Javascript and C in University and i'm also learning Python in my free time. Im open to learning new technologies.
I wanted to make an app that I personally would use on a regular basis. I love music so I decided to use the spotify API to make a playlist Generator.
Spotify have a public REST API which uses OAuth 2.0 to make requests on a users behalf. I used Node.js with Axios to easily make async requests to spotify. The first step was to get an authentication token which you can include in subsequent request headers.
Once we have authentication out of the way we can make requests to spotify. Here a user can enter the name of an artist, we then search spotify for the artist info and profile picture. Using EJS we can display the results on the web page. Once the user is happy with the inputs we can get some recommendations.
We display the resulting playlist on a table and give the user the option to add it to their spotify library.
I learned a lot about interacting with API's during this project, in particular using Async await and Promises in Node.js. I also used Deployed the app to a heroku site which was a good learning experience.
Github Link
This was a group project for university where we needed to make a web application. I lead the group to develop this app which uses Node.js, Express and a Mongo database.
The app allows you to create or login to your account, chat with other users and search the database for internships.
Users' hashed login details are stored in the database, when they login they are given a login cookie so that they can access the chat feature. We also added a google login feature.
While they are logged in users can chat. The messages are also stored in the database.
Here the user can search the database of internships by course, year and location, or just display all results. This search is run on the database and the resulting data displayed on a table.
Github Link
The price of rent is a hot topic at the moment especially among students. I decided to take a dataset provided by the Central Statistics Office here and visualise the data using Python and the Pandas library.
I converted the file to csv format and read the dataset into Python using pandas. From here I reduced the 750,000 row dataset down to ~10,000 rows by removing null data and filtering out data from non-cities. I plot the remaining values using matplotlib and the results show a similar increase in rent prices accross all cities
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