Completed an intensive graduate program at George Brown College focusing on developing full-stack AI and data science solutions. Gained expertise in AI, machine learning, deep learning, and data analytics, with cross-disciplinary training in computer science, mathematics, and business. Developed machine learning models, data visualizations, and dashboards to communicate findings effectively to both technical and business stakeholders. Acquired business analysis and design-thinking skills to optimize AI-driven solutions for diverse industries, supporting roles such as data scientist, data engineer, and machine learning engineer.

Academic Award:

• Dean’s Honour List (access available upon request).
  
  

Major Courses:

• Machine Learning II: Applied machine learning techniques using scikit-learn, OpenCV for computer vision, NLP, and audio-processing libraries.
• Deep Learning II: Learned advanced deep learning topics, including Convolutional Neural Networks (CNNs), object detection, feature maps, segmentation, GANs, transfer learning, autoencoders, and stability using diffusion pipelines.
• Data Visualization Techniques: Developed advanced reporting and data storytelling skills using Tableau for visualizing complex data insights.
• Big Data Tools & Techniques: Gained hands-on experience in big data frameworks, including Azure Fundamentals, Hadoop (HDFS, Pig, Hive, Beeline), and Spark, along with ETL processes using SQL Server Integration Services.
• Foundations of Data Management: Applied principles from the Data Management Body of Knowledge (DMBOK) framework to support data governance and best practices in data stewardship.
• Mathematical Concepts for Machine Learning: Strengthened mathematical foundations, covering topics like linear algebra, matrix factorization, TF-IDF, gradient descent, convolution, Fourier transforms, and signal processing.
• Mathematical Concepts for Deep Learning: Explored advanced mathematics relevant to neural networks and model architecture design, including complex gradient computations and optimization techniques.
• Design Thinking for AI: Emphasized user-centered design to develop AI solutions, fostering innovative problem-solving techniques aligned with user needs.
• Ethics & Law For Data Science: Focused on ethical considerations and legal responsibilities in AI, covering topics like data privacy, informed consent, accountability, and bias mitigation, preparing students to handle sensitive data responsibly and adhere to legal frameworks.
• Full Stack Data Science System: Developed end-to-end AI projects integrating model building, optimization, and deployment with data management showcasing the culmination of skills learned across the program.
  
  

Relevant Documents (access available upon request):

• Unofficial Transcript.
  
  

An interdisciplinary degree combining advanced mathematical and statistical methods with economic theory to model and analyze financial systems. This program emphasized problem-solving and data-driven insights, covering topics such as market fluctuations, futures pricing, and data mining. Graduates are well-prepared to apply quantitative analysis across finance, business, and sciences.

Major Courses:

• Financial Mathematics: Studied advanced financial mathematics, covering martingales, stopping times, the Snell envelope, interest rate calculations, discrete time option pricing, and the Black-Scholes formula. Applied multivariate normal distribution concepts to Markowitz portfolio theory.
• Bayesian Statistics: Gained an understanding of Bayesian inference, utilizing Markov Chain Monte Carlo (MCMC) methods in R for statistical computation, balancing both theoretical and practical applications.
• Categorical Data Analysis: Analyzed multi-way contingency tables and categorical data using logistic and log-linear models. Focused on study design, risk and association measures, and statistical software applications in categorical data analysis.
• Financial Econometrics: Applied econometric models to analyze financial data, focusing on methods for understanding market trends, risk, and volatility, and enhancing quantitative finance skills for real-world applications.
• Equity Valuation: Developed skills in valuing companies and assessing stock prices, learning to analyze financial statements, understand valuation models, and apply discounted cash flow and relative valuation techniques.
• Options and Futures: Explored derivative markets with a focus on pricing and strategies for options and futures, including hedging techniques and understanding risk management tools in financial markets.
• Industrial Organization II: Examined market structures and firm behavior, focusing on competition, monopoly, and strategic interaction in markets, and applying game theory to assess real-world business strategies.
• Time Series Analysis: Developed expertise in time series forecasting and analysis, learning to model temporal data, detect trends, and apply ARIMA, GARCH, and other models in economic and financial contexts.
• Machine Learning Methods: Introduced to machine learning algorithms and techniques, with an emphasis on supervised and unsupervised learning for analyzing large datasets and deriving actionable insights in finance and economics.
  
  

Relevant Documents (access available upon request):

• Certificate.
• Transcript.