Educational Path

Courses – Data Strategy / Business Data Science Core

Computer Science / Data Science / Machine Learning / Data Processing

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: M. Delabre

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

Computer Security (Builds the foundation for safeguarding data ecosystems, a critical capability for long-term strategy in business data science and data-driven decision-making)

Confidentiality and integrity of data using private and public keys. Protection of TCP/IP protocol layers; protection against computer viruses. User authentication methods. Risk assessment and management.

Prof: A. Tapp

Foundations of Machine Learning (Core foundation for designing intelligent, data-driven systems and models used in business forecasting and automation)

Core concepts in statistical and symbolic learning algorithms. Application examples in data mining, pattern recognition, nonlinear regression, and temporal data.

Prof: I. Mitliagkas

Software Quality and Metrics (Crucial for ensuring the reliability, maintainability, and performance of data products deployed in business environments)

Definition and promotion of quality. Quality assurance. Quality plans. Quality improvement and control (testing, reviews, inspections). Quality standards and frameworks. Measurement theory. Product and process metrics. Quality metrics.

Prof: B. Baudry

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.

Prof: M. Bilodeau

Complementary Courses

Spanish (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

Portuguese (Strategic for international markets)

Communicate in simple situations. Acquisition of vocabulary, principles of pronunciation and basic grammatical structures. Writing exercises, short compositions. Awareness of the culture of the Lusophone world.

Prof:  I. De Oliveira Messias

Principles of Economics (Builds core understanding of economic systems, market dynamics, and decision-making frameworks relevant to business strategy)

Presentation of the basic tools of economic analysis: opportunity cost, supply, demand and price; consumer choice; firms’ production choices; competitive markets; monopoly; efficiency; international trade.

Prof: G. Foba Kengne

Introduction to Macroeconomics (Provides a systemic view of economic environments, monetary systems, and macro-level factors impacting business and data-driven strategy)

Interdependence of markets and national and financial accounting. Monetary markets and theories of inflation. Labor market and types of unemployment. Long- and short-term models. IS-LM model; economic fluctuations.

Prof: H. Park

Volunteering

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

Volunteer – Immigration - April 2024
Organization: Québec Réunifié
Field: Human Rights

Organizations and Events

TechAide Climate Tech & AI for Social Good @ Espace CDPQ Montréal - May 2025

Lime Connect Recruitment Reception @ Omni Hotel Montréal – November 2023

McGill Artificial Intelligence Society @ McGill University - March 2023

MILA TechAide AI Conference 2022 @ MILA Quebec - April 2022

Scholarships and Programs

• RGA Scholarship

  
  

• Léo-Paul Roy Scholarship

  
  

• FAVE Institutional Scholarship (Université de Montréal)

  
  

• Institutional Scholarship (Université de Montréal)

  
  

• Perspective Québec Scholarships (6/6)

Academic Background

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

  Graduation: August 2026

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

  Université de Montréal ranked #9 globally in Artificial Intelligence (Shanghai Ranking 2025)

  Interests: Running, kitesurfing, snowboarding, tennis, economic & international news, languages

  
  

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

  
  

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

  QS Best Student Cities 2025: Montreal – 10th

Hakathons

Participant - Perplexity Hackathon (2025)