Educational Path

Courses – Data Strategy / Business Data Science Core

Computer Science / Data Science / Machine Learning / Data Processing

Introduction to Data Science (Foundational for understanding data processing pipelines and analytical modeling in Business Data Science)

Context and applications of probability, statistics, optimization, and computing tools for data science; data cleaning and visualization; statistical challenges in machine learning with structured data.

Prof: G. Gidel

Operations Research Models (Key for optimizing business processes and data-driven decision-making strategies)

Linear programming. Simplex method. Duality. Integer programming. Network problems. PERT/CPM methods. Shortest path algorithms. Deterministic and probabilistic dynamic programming. Stochastic models.

Prof: K. Amghar

Databases (Essential for designing and maintaining efficient data infrastructures to support business intelligence)

Architecture. Data organization models. Definition, creation, updating, and querying. Database exploitation and management.

Prof: M. Boyer

Operating Systems (Critical for understanding the underlying infrastructure enabling scalable data systems)

Core functions. Parallelism management. Synchronization. Deadlocks. Scheduling. Memory and I/O management. File systems. Protection and distributed systems.

Prof: L. Paull

Introduction to Numerical Algorithms (Foundational for accurate computational modeling and machine learning algorithm reliability)

Floating-point arithmetic, error analysis. Solving linear and nonlinear equations. Interpolation, least squares. Numerical differentiation and integration. Ordinary differential equations.

Prof: M. Mignotte

Data Structures (Core to data optimization, algorithm design, and efficient data handling)

Abstract data types, trees, dictionaries, priority queues, graphs, external methods.

Prof: F. Major

Computer Architecture (Provides a technical foundation crucial for anticipating technological shifts impacting data infrastructure.)

Instruction sets: RISC vs CISC. Addressing modes. Exceptions. I/O devices, buses, interrupts. Hardwired and microprogrammed control. Pipelining and parallelism. Technology evolution.

Prof: A. Tsikhanovich

Human-Computer Interfaces (User-centered strategy, adoption of data-driven systems)

Interface concepts and languages. Event-driven programming. User modeling. GUI design and programming. Impacts on multimedia, collaboration, and communication.

Prof: M. Bessmeltsev

Software Engineering (Project organization, crucial for building "data products")

Introduction to software engineering. Development cycles. Analysis, modeling, and specification. Object-oriented design. Debugging. Development tools and environments.

Prof: L. Lafontant

Discrete Structures in Computer Science (Essential for building strong analytical thinking and modeling complex data relationships in strategic systems)

Discrete mathematics applied to computer science: logic, sets, functions, combinatorics, graphs, trees, and formal languages.

Prof: M. Csűrös

Mathematics / Statistics

Calculus I (Analytical rigor)

Sequences and series. Functions of several variables, continuity, partial derivatives, differentials, tangent planes, chain rule. Gradient, level surfaces, extrema. Multiple integrals, change of variables, Jacobians.

Prof: I. Ndiaye

Linear Algebra (Essential in Machine Learning and Data Strategy)

Linear systems, Gaussian elimination, matrix inverses. Vector spaces, linear independence, linear transformations, basis changes. Inner product spaces. Determinants. Diagonalization. Applications.

Prof: K. Amoura

Probability (Risk analysis, fundamental for modeling)

Probability spaces. Combinatorial analysis. Conditional probability. Independence. Random variables. Distributions and generating functions. Expectation. Weak law of large numbers. Central limit theorem.

Prof: X. Phung

Introduction to Statistics (Solid base for data-driven strategies)

Data description and production. Probability concepts. Inference. Confidence intervals. Hypothesis testing. Count data. Contingency tables. Simple linear regression. Use of statistical software.

Prof: J. Coulombe

Concepts and Methods in Statistics (Advanced, crucial for predictive analytics)

Point and interval estimation. Hypothesis testing. Graphical methods. Chi-squared tests. Decision theory. Bayesian inference. Two-sample comparisons. Related to CAS and ICA accreditation exams.

Prof: M. Bilodeau

Complementary Courses

Spanish Level A2 (Strategic for international markets)

Communication in familiar settings. Reading and listening comprehension of simple texts and conversations. Basic writing. Awareness of Hispanic cultures. Communication-oriented pedagogy.

Prof: L. Malo

Current Term (Spring/Summer 2025)

Introduction to Algorithms (Crucial for optimizing data processes, designing efficient solutions, and scaling data-driven strategies)

Algorithm design and analysis. Asymptotic notation. Solving recurrences. Greedy algorithms. Divide-and-conquer. Dynamic programming. Graph traversal. Backtracking. Probabilistic algorithms.

Prof: S. Ducharme

Programming Languages Concepts (Provides deep understanding of how different programming paradigms impact system architecture and data application design)

History. Concepts and implementation of basic programming entities. Execution mechanisms: stack, heap, parameter passing. Low-level programming (C language). Structured, functional, and logic programming. Specialized languages.

Prof: S. Monnier

Internet Technology (Fundamental for designing accessible, data-driven platforms and understanding the structure of information flows in digital ecosystems)

Introduction to web applications and website organization. XML, XML schemas, and XSLT transformations. Client-side programming (JavaScript) and server-side programming (CGI, PHP, Ajax). Search engines. Web design. Introduction to the Semantic Web.

Prof: A. Seguin

Future Courses (In Progress – This List Will Update)

Quantum Computing (Prepares for anticipating the next frontier of computational innovation, essential for long-term strategic positioning in data and AI fields)

Reversible computing; quantum information; non-locality; quantum cryptography; quantum circuits, parallelism, and interference; Simon’s, Shor’s, and Grover’s algorithms; quantum teleportation; error correction; implementation.

Prof: G. Brassard

Advanced Machine Learning Project (Develops the ability to translate advanced machine learning models into actionable solutions for business and strategic insights)

Practical preparation for machine learning applications through real-world projects on real datasets. Use of specialized machine learning software for AI. Recommended: IFT 6135 Representation Learning previously or concurrently.

Prof: (to be confirmed)

Volunteering

Volunteer – Canadian Federal Elections - April 2025
Organization: New Democratic Party of Canada
Field: Politics

Navigated civic engagement processes during the Canadian Federal Elections through event participation and operational support.

Volunteer – Family Support and Immigration Awareness - April 2024
Organization: Québec Réunifié
Field: Human Rights

Navigated immigration complexities in Quebec/Canada through event participation.

Organizations and Events

Lime Connect Recruitment Reception – November 2023

Engaged with corporate representatives from leading companies (IBM, Deloitte, TD, Microsoft, etc.) during Lime Connect’s selective recruitment and networking event for high-potential students and professionals.

McGill Artificial Intelligence Society - March 2023

Engaged in the closing discussions of MILA’s TechAide AI Conference.

MILA TechAide AI Conference 2022 - April 2022

Engaged with AI innovation talks hosted by McGill Artificial Intelligence Society.

Scholarships and Programs

• RGA Scholarship

  
  

• Léo-Paul Roy Scholarship

  
  

• Perspective Québec Scholarships (4/6)

Academic Background

• Université de Montréal — Bachelor's Degree in Computer Science
  (2025)

  Graduation: April 2026

  AI coursework taught by faculty affiliated with Mila – Quebec AI Institute, the world’s largest academic research center in deep learning.

  [Shanghai Global Ranking 2025: Computer Science – 24th]

  [THE World University Rankings 2023: Computer Science – 34th]

  
  

• Université de Montréal — Mathematics and Computer Science (Data Science specialization)
  (2021–2025)

  [QS World University Rankings 2023: Statistics & Operational Research – 32nd]

  
  

• Université de Montréal — Preparatory Year, Natural Sciences
  (2018–2020)

  Grade: 3.0 / 4.3 | QS Best Student Cities 2025: Montreal – 10th

Hakathons

Planning to participate in Hack the 6ix this year.

Excited to learn, build, and explore how tech can drive meaningful impact.

#HackThe6ix #AI #Hackathon #LearningByBuilding

Participant - Perplexity Hackathon (2025)

Currently developing Insight & Metrics, a real-time, AI-powered dashboard (from my portfolio project) focused on climate reasoning, bias detection, and open knowledge synthesis - leveraging Perplexity's API to explore civic use cases of search-powered intelligence.