Welcome!!

Empowering decision making is the key to unlocking insights for resounding success. I create robust data architectures that enlighten business decisions and drive growth.

Data Engineering | Cloud | Data Analysis | All Outdoor Activity

About Me

I am a passionate and dedicated learner, constantly seeking out new challenges and embracing the opportunity to acquire new knowledge. My skill set includes SQL, Python, Spark, AWS, Azure, Databricks, Power BI and GIT and various other tools vital to data analysis and visualization. Building upon my foundation in statistics and my previous studies in software engineering, cloud computing, and data architectures and design patterns. I am currently finishing my Masters Degree Dissertation on Multi-Agentic Framework for Keypoint Analysis in Olympic Lifting at Auckland University of Technology. I am interested in opportunities that will allow me to leverage my skills and experience to drive business growth and innovation. I am particularly passionate about data engineering, data analysis, and cloud computing. I am excited about the potential to transform businesses and solve complex problems.

  • Name: Ryan Lafferty
  • Interested in Data Engineering, BI Engineering or Analytics Engineering Role.
  • Hiking
  • Diving
  • Attempting to Surf
  • Never Ending Education

Education and Skills

Degrees

  • Masters of Computer and Information Systems: Auckland University of Technology
  • Bachelors of Business Administration: The Pennsylvania State University
  • Postgraduate Certificate in Software Engineering: Montgomery County Community College

    • Certifications

  • DataExpert.io Data Engineering Bootcamp
  • Institute of Data: Data Science and AI
  • AWS Solutions Architect
  • AWS Cloud Foundations
    • Postgres
    SQL Server
    Python
    AWS
    Azure
    GIT
    Microsoft Excel
    Power BI
    Databricks

    Projects

    PantryPi: An automated pantry and refrigerator inventory system using a Raspberry Pi and computer vision

    The PantryPi enables users to monitor their pantry and refrigerator items by utilising advanced technologies like IoT, computer vision, and generative AI. It offers intelligent recipe suggestions that optimize the use of available ingredients, tailored to individual preferences. Additionally, Pantry Pi maintains an updated inventory accessible via mobile devices, helping users avoid unnecessary grocery purchases and minimize food waste.

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    Github

    PantryPi: An automated pantry and refrigerator inventory system using a Raspberry Pi and computer vision

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    Github

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    Landslide Classification: Geospatial Analysis

    Landslides can cause significant damage to property and loss of life, making their prediction an important area of research. This dataset provides information on various geological and environmental factors that can contribute to the occurrence of landslides. By training a machine learning model on this dataset, we can develop a tool that can predict the likelihood of a landslide occurring in a given location, based on the features provided. Such a tool could be used by governments, emergency responders, and other stakeholders to better prepare for and mitigate the impact of landslides, potentially saving lives and reducing the economic impact of these events.

    Github
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    AirBnB Geospatial Feature Engineering and Prediction

    This notebook uses geospatial feature engineering to predict the prices of Airbnb listings in New Zealand, leveraging data from Yelp!, Open Street Maps, and geographically weighted spatial lags. The notebook creates predictive models, including linear regression and geographically weighted regression, and evaluates them using various metrics. The analysis attempts to provide insights into the factors influencing Airbnb pricing in New Zealand and demonstrates the potential of geospatial analysis for predicting real estate prices.

    Github

    Semantic Segmentation Using Landsat Satellite Imagery

    For my two week capstone project, I demonstrated my new skills by collecting satellite imagery and creating a deep learning model to classify satellite imagery into specific land use categories. Semantic segmentation is a technique used in remote sensing and computer vision to classify each pixel in an image into different classes or categories. The application of semantic segmentation using Landsat imagery is important due to the widespread use of Landsat data for environmental and resource management.

    Contact Me

    https://www.embed-map.com