About Me

I'm a self-motivated, hardworking and positive Data Scientist with a drive for continuous improvement and an ambition to fulfil a career that enables development of technical and interpersonal skills. Having taken on a number of varied roles within large-scale aerospace programmes, I have experience of data analysis, data service delivery and defining technology strategy.

I love developing and applying my technical skills to tackle interesting problems and draw meaningful insights from data. In particular, I'm passionate about the role of data analytics in football. I've set this page up to give an overview of some of the work I've undertaken in my spare time!

Projects

Listed below are a number of projects that I have undertaken, either for personal interest or to complement learning (see Courses & Learning section). Click on a project image to open its source repository or site, which contains source code and additional information:


Property Market Analysis

Having decided on a move to London, I found myself trawling through endless pages of rental property listings on Rightmove. On a mission to find a value-for-money home, I realised that I had subconsicously formulated biases and hypothesese about the market. I therefore produced a Tableau dashboard to test these hypothesese and analyse the London rental market, clearly presenting market trends whilst providing the information I required to find a suitable home. This work also involves the development of a Rightmove data extraction tool to acquire property listing data.

  Web scraping: BS4  

  HTML basics  

  Data processing: pandas  

  Data engineering - Joins  

  Data vis.: Tableau  



Football Data Analytics

A collection of Python projects focusing on the analysis and visualisation of football data, including scraping data from informative football websites and processing large public-domain match event datasets. This work includes production of appealing visuals and assessments of player performance, as well as the development of expected goals models using supervised machine learning techniques.

  Data processing: pandas  

  Data vis.: Matplotlib  

  Web scraping: BS4 & Selenium  

  Package & Module development  

  Supervised ML - Linear Regression  

  Supervised ML - Logistic Regression  

  Supervised ML - Neural Networks  



Machine Learning from Scratch

Implementation of a variety of Machine Learning algorithms from scratch in Python, drawing inspiration from books and tutorials. This works inolves the application of Object-Oriented Programming to produce supervised and unsupervised ML models, with the neccessary testing completed in order to demonstrate performance. Each algorithm is represented as an object with fit, predict and visualise methods that not only provide functionality, but give the user a visual representation of the statistical methods applied.

  Package & Module development  

  Module documentation  

  Object-oriented Programming  

  Supervised ML - Linear Regression  

  Supervised ML - kNN  

  Supervised ML - SVMs  

  Unsupervised ML - k Means  

  Unsupervised ML - Mean Shift  

Courses & Learning

I have completed a number of courses and learning activities to improve my competence and proficiency as a Data Scientist. These are listed below, sorted by date of completion (most recent top), along with key learning and certificates as appropriate. When viewing on mobile, a landscape orientation is recommended.

Course Summary of Learning Evidence
Stanford Online - Machine Learning Specialisation Supervised Learning, Linear regression, Logistic regression, Regularisation, Gradient Descent, Tensorflow, Neural Networks, Decision trees, Tree ensembles, Anomaly detection, Unsupervised learning, Recommender Systems, Reinforcement Learning Certificate
Udemy - Tableau for Data Science Time series, Aggregation, Filters, Joining, Blending, Relationships, Visualising data, Geographic data, Table calculations, LOD calculations, Clustering, Dashboards & stories. Certificate
Udemy - Python for Data Science & Machine Learning Numpy, Pandas, Matplotlib, Seaborn, Sci-kit learn, Supervised ML, Unsupervised ML, Tensorflow, Keras, Deep learning, Neural networks, PCA, Recommender systems, NLP, Big data. Certificate
Udemy - Complete Python Bootcamp Jupyter notebooks, Data types, Basic operations, Methods, Functions, OOP, Modules & packages, Numpy, Pandas, Matplotlib, Handling errors, Decorators, Generators, Importing files, Web scraping, Image processing. Certificate Coursework

Contact Me

Please use the icons at the bottom of the navigation pane to get in touch via Twitter, GitHub, Linked-in, Tableau public or Email.