Hello World! My name's Jordan Muskitta.
I am a data analyst with a background in financial services and foreign exchange. I love using and manipulating financial data in order to find powerful insights that can make a tangible effect on an organisation. I am currently studying data science part-time at The University of Sydney with goals to speciliase in machine learning and deep neural networks. I also have a passion for trading equities in my spare time and have been developing algorithmic trading strategies with the Interactive Brokers API.
This project allowed me to develop my skills as a python programmer through creating an ASX Annoucement Scraper and Sentiment Analysis bot. I was able to work with new python libraries such as vader and Tika. This project helped ameliorate myself with NLP and machine learning principles.
- Decipher the structure and content of HTML
- Use Beautiful Soup to parse HTML
- Download pdfs locally
- Convert pdf to txt
- Analyze text for sentiment
Click Here To See Twitter Bot Posts
This project was my final capstone as a part of General Assembly's Data Analytics Immersive Course that was a part of an Australia Government initiative. In this Project I was required to analyse a real Australian start-ups data in order to find insights from their customer and revenue data. This was a great project to work on as it expanded my skill set and pushed my analytical thinking. I had to use many tools and languages in this project in order to wrangle, clean and present the data. This project showed me importance of time mangement and project planning, as well as how extensive and time consuming cleaning data can be. In this project I used a mix of Python, MySQL and Tableau.
- Find one subscription insight
- Find one growth/revenue insight
- Find customer churn rate
- Find customer lifetime value
I created this project as a part of my data science studies at university. Like many group projects there were difficulties with participation and contribution, therefore after unsuccessfully trying to garner more of a participatory spirit amongst team members, I opted to do this project alone. I was able to achieve a high distinction in this project, which gave me the confidence in my decision to perform this project solo.
- Find if there's any correlation between Tesla's share price, the NASDAQ price and the components used in Tesla's battery
- Scrape and clean the data
- Comment on the provenance and meta data
- Use machine learning algorithms in order to predict Tesla's price (LSTM)
