Master Certificate in Machine Learning & Data Science
Master Certificate in Machine Learning & Data Science
Master Certificate in Machine Learning & Data Science
Become a job-ready data and AI professional with our Master Certificate in Data Science, Cloud, and AI. Gain hands-on experience with real-world projects, learn to analyze data, build intelligent systems, and work with modern cloud and AI tools. Build skills, knowledge, and confidence to launch your career in high-demand roles like data scientist, data engineer, or machine learning practitioner.
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Durée:
6 Months
Durée:
6 Months
Durée:
6 Months
Durée:
6 Months
Durée:
6 Months
Durée:
6 Months
Durée:
6 Months
Durée:
Voie rapide : 8 mois
Début : 15 oct 2025
Horaire : Lu-Mer-Ven
Début : 15 oct 2025
Horaire : Lu-Mer-Ven
Début : 15 oct 2025
Horaire : Lu-Mer-Ven
Début : 15 oct 2025
Horaire : Lu-Mer-Ven
Événements :
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Aperçu du programme
Aperçu du programme
Aperçu du programme
The Master Certificate in Data Science, Cloud, and AI equips you with the essential skills and practical experience to become a job-ready data and AI professional. Through a mix of foundational concepts, hands-on projects, and applied learning, you’ll gain the knowledge to analyze data, build intelligent systems, and work with modern AI and cloud technologies. The program emphasizes both technical expertise and problem-solving skills, preparing you for high-demand roles such as data scientist, data engineer, or machine learning practitioner, and giving you the confidence to contribute effectively to data-driven and AI-powered initiatives.
Plan de cours
Plan de cours
Plan de cours
Become job-ready in cloud computing, data science, and artificial intelligence with this comprehensive program. Learn foundational concepts in data analysis, Python programming, and data engineering, and advance to classical machine learning, deep learning, NLP, and MLOps. Through hands-on projects and real-world applications, you’ll gain practical skills to build intelligent systems, work with large datasets, and deploy scalable AI solutions. This program is designed to prepare you for high-demand roles in cloud architecture, data science, AI development, and data-driven decision-making.
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Introduction to Data Science
The Data Literacy & Foundations path equips learners with the essential knowledge and practical skills needed to work confidently with data in real-world business and technical environments. You’ll begin by understanding the big picture of data science and the role data plays in organizations, exploring how executives and leaders leverage insights to make informed decisions. The path then guides you through hands-on experiences with data—collecting, cleaning, processing, and governing it effectively. You’ll also develop skills in visualizing data, communicating insights through compelling storytelling, and applying ethical principles and privacy considerations for responsible data use. By the end of the course, you’ll have a strong foundation in both technical and business aspects of data, preparing you to contribute effectively to data-driven teams and make informed decisions with confidence.
Concepts covered:
Overview of data science and its organizational impact
Executive and leadership perspectives on data-driven decision-making
Data collection, cleaning, transformation, and preparation
Data governance, quality, and compliance frameworks
Fundamentals of data analysis and insights generation
Data visualization principles and storytelling techniques
Ethics, privacy, and responsible data practices
Key tools and frameworks used in data management and analysis
APTITUDES
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Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
APTITUDES
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Introduction to Data Science
The Data Literacy & Foundations path equips learners with the essential knowledge and practical skills needed to work confidently with data in real-world business and technical environments. You’ll begin by understanding the big picture of data science and the role data plays in organizations, exploring how executives and leaders leverage insights to make informed decisions. The path then guides you through hands-on experiences with data—collecting, cleaning, processing, and governing it effectively. You’ll also develop skills in visualizing data, communicating insights through compelling storytelling, and applying ethical principles and privacy considerations for responsible data use. By the end of the course, you’ll have a strong foundation in both technical and business aspects of data, preparing you to contribute effectively to data-driven teams and make informed decisions with confidence.
Concepts covered:
Overview of data science and its organizational impact
Executive and leadership perspectives on data-driven decision-making
Data collection, cleaning, transformation, and preparation
Data governance, quality, and compliance frameworks
Fundamentals of data analysis and insights generation
Data visualization principles and storytelling techniques
Ethics, privacy, and responsible data practices
Key tools and frameworks used in data management and analysis
APTITUDES
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Introduction to Data Science
The Data Literacy & Foundations path equips learners with the essential knowledge and practical skills needed to work confidently with data in real-world business and technical environments. You’ll begin by understanding the big picture of data science and the role data plays in organizations, exploring how executives and leaders leverage insights to make informed decisions. The path then guides you through hands-on experiences with data—collecting, cleaning, processing, and governing it effectively. You’ll also develop skills in visualizing data, communicating insights through compelling storytelling, and applying ethical principles and privacy considerations for responsible data use. By the end of the course, you’ll have a strong foundation in both technical and business aspects of data, preparing you to contribute effectively to data-driven teams and make informed decisions with confidence.
Concepts covered:
Overview of data science and its organizational impact
Executive and leadership perspectives on data-driven decision-making
Data collection, cleaning, transformation, and preparation
Data governance, quality, and compliance frameworks
Fundamentals of data analysis and insights generation
Data visualization principles and storytelling techniques
Ethics, privacy, and responsible data practices
Key tools and frameworks used in data management and analysis
APTITUDES
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Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
APTITUDES
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Python Programming from Zero to Hero
This Python Programming path takes you from beginner to advanced, providing the knowledge and hands-on experience needed to use Python confidently for data analysis, software development, and automation. You’ll start with the fundamentals of Python programming, including syntax, data types, and core programming constructs, then progress to functions, modules, and object-oriented programming. Advanced topics such as collections, decorators, performance optimization, testing, debugging, and building REST APIs will give you practical, job-ready skills. The course also covers working with data in Python, including importing, cleaning, and preparing data for analysis. By the end of the course, you’ll be able to write clean, efficient, and maintainable Python code, and apply it effectively in real-world projects.
Concepts covered:
Python fundamentals: variables, data types, control flow, loops
Functions, modules, and reusable code structures
Object-oriented programming (OOP) concepts and classes
Advanced Python features: collections, decorators, performance optimization
Debugging, testing, and best practices for maintainable code
Building REST APIs using Python
Working with data: importing, cleaning, and processing datasets
APTITUDES
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Python Programming from Zero to Hero
This Python Programming path takes you from beginner to advanced, providing the knowledge and hands-on experience needed to use Python confidently for data analysis, software development, and automation. You’ll start with the fundamentals of Python programming, including syntax, data types, and core programming constructs, then progress to functions, modules, and object-oriented programming. Advanced topics such as collections, decorators, performance optimization, testing, debugging, and building REST APIs will give you practical, job-ready skills. The course also covers working with data in Python, including importing, cleaning, and preparing data for analysis. By the end of the course, you’ll be able to write clean, efficient, and maintainable Python code, and apply it effectively in real-world projects.
Concepts covered:
Python fundamentals: variables, data types, control flow, loops
Functions, modules, and reusable code structures
Object-oriented programming (OOP) concepts and classes
Advanced Python features: collections, decorators, performance optimization
Debugging, testing, and best practices for maintainable code
Building REST APIs using Python
Working with data: importing, cleaning, and processing datasets
APTITUDES
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Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
APTITUDES
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Data Engineering
The Data Engineering path provides practical, hands-on skills to extract, transform, and load data efficiently, and to prepare it for analysis and downstream applications. You’ll start with core ETL and data engineering principles, including cleaning and pre-processing techniques for structured and unstructured data. The course then dives into Python libraries like NumPy and Pandas, teaching you how to manipulate, normalize, and process data at scale. Finally, you’ll gain experience in data visualization using Matplotlib and Seaborn, enabling you to communicate insights effectively. By the end of this path, you’ll be ready to build robust ETL pipelines, work with large datasets, and create visualizations that support data-driven decisions.
Concepts covered:
ETL and ELT fundamentals for data pipelines
Core data engineering skills and best practices
Text and tabular data cleaning and preprocessing techniques
Python libraries for numerical computation (NumPy)
Data manipulation and transformation with Pandas
Data normalization and preparation for analysis
Data visualization principles using Matplotlib and Seaborn
APTITUDES
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Data Engineering
The Data Engineering path provides practical, hands-on skills to extract, transform, and load data efficiently, and to prepare it for analysis and downstream applications. You’ll start with core ETL and data engineering principles, including cleaning and pre-processing techniques for structured and unstructured data. The course then dives into Python libraries like NumPy and Pandas, teaching you how to manipulate, normalize, and process data at scale. Finally, you’ll gain experience in data visualization using Matplotlib and Seaborn, enabling you to communicate insights effectively. By the end of this path, you’ll be ready to build robust ETL pipelines, work with large datasets, and create visualizations that support data-driven decisions.
Concepts covered:
ETL and ELT fundamentals for data pipelines
Core data engineering skills and best practices
Text and tabular data cleaning and preprocessing techniques
Python libraries for numerical computation (NumPy)
Data manipulation and transformation with Pandas
Data normalization and preparation for analysis
Data visualization principles using Matplotlib and Seaborn
APTITUDES
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Data Engineering
The Data Engineering path provides practical, hands-on skills to extract, transform, and load data efficiently, and to prepare it for analysis and downstream applications. You’ll start with core ETL and data engineering principles, including cleaning and pre-processing techniques for structured and unstructured data. The course then dives into Python libraries like NumPy and Pandas, teaching you how to manipulate, normalize, and process data at scale. Finally, you’ll gain experience in data visualization using Matplotlib and Seaborn, enabling you to communicate insights effectively. By the end of this path, you’ll be ready to build robust ETL pipelines, work with large datasets, and create visualizations that support data-driven decisions.
Concepts covered:
ETL and ELT fundamentals for data pipelines
Core data engineering skills and best practices
Text and tabular data cleaning and preprocessing techniques
Python libraries for numerical computation (NumPy)
Data manipulation and transformation with Pandas
Data normalization and preparation for analysis
Data visualization principles using Matplotlib and Seaborn
APTITUDES
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Introduction to Machine Learning
The Introduction to Machine Learning path provides a practical foundation in building, evaluating, and deploying machine learning models. You’ll start by understanding the key concepts and workflows of ML, including feature engineering, model selection, and the lifecycle of production ML systems. The course then dives into classical machine learning models using Scikit-learn, teaching you how to prepare data, engineer features, and train models effectively. You’ll also learn best practices for model evaluation, validation, and deployment, with hands-on projects that demonstrate real-world applications. By the end of this course, you’ll have the skills to develop, assess, and implement machine learning solutions that support data-driven decision-making.
Concepts covered:
Building features from numeric and categorical data
Creating, training, and deploying machine learning models
Classical machine learning algorithms and workflows using Scikit-learn
Data preparation and feature engineering for ML
Foundations of machine learning engineering
Model evaluation, validation, and best practices for production systems
Practical applications of machine learning in real-world scenarios
APTITUDES
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Introduction to Machine Learning
The Introduction to Machine Learning path provides a practical foundation in building, evaluating, and deploying machine learning models. You’ll start by understanding the key concepts and workflows of ML, including feature engineering, model selection, and the lifecycle of production ML systems. The course then dives into classical machine learning models using Scikit-learn, teaching you how to prepare data, engineer features, and train models effectively. You’ll also learn best practices for model evaluation, validation, and deployment, with hands-on projects that demonstrate real-world applications. By the end of this course, you’ll have the skills to develop, assess, and implement machine learning solutions that support data-driven decision-making.
Concepts covered:
Building features from numeric and categorical data
Creating, training, and deploying machine learning models
Classical machine learning algorithms and workflows using Scikit-learn
Data preparation and feature engineering for ML
Foundations of machine learning engineering
Model evaluation, validation, and best practices for production systems
Practical applications of machine learning in real-world scenarios
APTITUDES
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Introduction to Machine Learning
The Introduction to Machine Learning path provides a practical foundation in building, evaluating, and deploying machine learning models. You’ll start by understanding the key concepts and workflows of ML, including feature engineering, model selection, and the lifecycle of production ML systems. The course then dives into classical machine learning models using Scikit-learn, teaching you how to prepare data, engineer features, and train models effectively. You’ll also learn best practices for model evaluation, validation, and deployment, with hands-on projects that demonstrate real-world applications. By the end of this course, you’ll have the skills to develop, assess, and implement machine learning solutions that support data-driven decision-making.
Concepts covered:
Building features from numeric and categorical data
Creating, training, and deploying machine learning models
Classical machine learning algorithms and workflows using Scikit-learn
Data preparation and feature engineering for ML
Foundations of machine learning engineering
Model evaluation, validation, and best practices for production systems
Practical applications of machine learning in real-world scenarios
APTITUDES
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Classical Machine Learning Approaches
The Classical Machine Learning Approaches path provides a deep dive into traditional machine learning techniques and their practical applications. You’ll start by learning how to build features from numeric data and create robust machine learning models. The course also covers the complete lifecycle of production ML systems, ensuring your models are scalable, maintainable, and ready for real-world deployment. Using Scikit-learn, you’ll explore classical algorithms, training strategies, and best practices for building high-performing solutions. By the end of this module, you’ll have the skills to develop, evaluate, and implement classical ML models effectively in professional projects.
Concepts covered:
Feature engineering from numeric data
Building and training machine learning models
Classical ML algorithms and workflows with Scikit-learn
Production machine learning systems and deployment practices
Best practices for model evaluation and performance optimization
APTITUDES
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Classical Machine Learning Approaches
The Classical Machine Learning Approaches path provides a deep dive into traditional machine learning techniques and their practical applications. You’ll start by learning how to build features from numeric data and create robust machine learning models. The course also covers the complete lifecycle of production ML systems, ensuring your models are scalable, maintainable, and ready for real-world deployment. Using Scikit-learn, you’ll explore classical algorithms, training strategies, and best practices for building high-performing solutions. By the end of this module, you’ll have the skills to develop, evaluate, and implement classical ML models effectively in professional projects.
Concepts covered:
Feature engineering from numeric data
Building and training machine learning models
Classical ML algorithms and workflows with Scikit-learn
Production machine learning systems and deployment practices
Best practices for model evaluation and performance optimization
APTITUDES
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Classical Machine Learning Approaches
The Classical Machine Learning Approaches path provides a deep dive into traditional machine learning techniques and their practical applications. You’ll start by learning how to build features from numeric data and create robust machine learning models. The course also covers the complete lifecycle of production ML systems, ensuring your models are scalable, maintainable, and ready for real-world deployment. Using Scikit-learn, you’ll explore classical algorithms, training strategies, and best practices for building high-performing solutions. By the end of this module, you’ll have the skills to develop, evaluate, and implement classical ML models effectively in professional projects.
Concepts covered:
Feature engineering from numeric data
Building and training machine learning models
Classical ML algorithms and workflows with Scikit-learn
Production machine learning systems and deployment practices
Best practices for model evaluation and performance optimization
APTITUDES
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Applied Data Science and Deep Learning
The Applied Data Science and Deep Learning path provides hands-on expertise in modern machine learning, neural networks, and AI workflows. You’ll start with PyTorch, learning to build solutions for image classification, natural language processing, predictive analytics, and transfer learning. Next, you’ll work with TensorFlow and Keras to design, train, and deploy neural networks and ML workflows. The path also introduces MLOps principles for deploying and managing models in production, continuous training with evolving data streams, and practical applications of deep learning across industries such as healthcare, retail, and marketing. You’ll explore advanced topics like convolutional and recurrent neural networks, transformer models, BERT, large language models, and HuggingFace frameworks. By the end of this module, you’ll have the skills to design, implement, and deploy deep learning solutions for real-world problems.
Concepts covered:
PyTorch fundamentals and advanced applications (image classification, NLP, predictive analytics)
TensorFlow and Keras workflows for building and deploying neural networks
MLOps principles and production-ready ML pipelines
Deep learning foundations and practical applications across industries
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Transformer models, BERT, and Large Language Models (LLMs)
Model explainability and continuous training with evolving datasets
Practical tools and frameworks including HuggingFace
APTITUDES
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Applied Data Science and Deep Learning
The Applied Data Science and Deep Learning path provides hands-on expertise in modern machine learning, neural networks, and AI workflows. You’ll start with PyTorch, learning to build solutions for image classification, natural language processing, predictive analytics, and transfer learning. Next, you’ll work with TensorFlow and Keras to design, train, and deploy neural networks and ML workflows. The path also introduces MLOps principles for deploying and managing models in production, continuous training with evolving data streams, and practical applications of deep learning across industries such as healthcare, retail, and marketing. You’ll explore advanced topics like convolutional and recurrent neural networks, transformer models, BERT, large language models, and HuggingFace frameworks. By the end of this module, you’ll have the skills to design, implement, and deploy deep learning solutions for real-world problems.
Concepts covered:
PyTorch fundamentals and advanced applications (image classification, NLP, predictive analytics)
TensorFlow and Keras workflows for building and deploying neural networks
MLOps principles and production-ready ML pipelines
Deep learning foundations and practical applications across industries
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Transformer models, BERT, and Large Language Models (LLMs)
Model explainability and continuous training with evolving datasets
Practical tools and frameworks including HuggingFace
APTITUDES
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Applied Data Science and Deep Learning
The Applied Data Science and Deep Learning path provides hands-on expertise in modern machine learning, neural networks, and AI workflows. You’ll start with PyTorch, learning to build solutions for image classification, natural language processing, predictive analytics, and transfer learning. Next, you’ll work with TensorFlow and Keras to design, train, and deploy neural networks and ML workflows. The path also introduces MLOps principles for deploying and managing models in production, continuous training with evolving data streams, and practical applications of deep learning across industries such as healthcare, retail, and marketing. You’ll explore advanced topics like convolutional and recurrent neural networks, transformer models, BERT, large language models, and HuggingFace frameworks. By the end of this module, you’ll have the skills to design, implement, and deploy deep learning solutions for real-world problems.
Concepts covered:
PyTorch fundamentals and advanced applications (image classification, NLP, predictive analytics)
TensorFlow and Keras workflows for building and deploying neural networks
MLOps principles and production-ready ML pipelines
Deep learning foundations and practical applications across industries
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Transformer models, BERT, and Large Language Models (LLMs)
Model explainability and continuous training with evolving datasets
Practical tools and frameworks including HuggingFace
APTITUDES
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Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete three hands-on projects that allow you to practice the full data science workflow—from collecting and cleaning data to building models, generating insights, and presenting your findings.
These projects are designed to help you:
Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently
By the end of this module, you’ll have a portfolio of capstone projects that not only reflects your technical expertise but also shows your problem-solving skills and readiness for a professional data science role.
APTITUDES
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Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete three hands-on projects that allow you to practice the full data science workflow—from collecting and cleaning data to building models, generating insights, and presenting your findings.
These projects are designed to help you:
Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently
By the end of this module, you’ll have a portfolio of capstone projects that not only reflects your technical expertise but also shows your problem-solving skills and readiness for a professional data science role.
APTITUDES
……………………..
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete three hands-on projects that allow you to practice the full data science workflow—from collecting and cleaning data to building models, generating insights, and presenting your findings.
These projects are designed to help you:
Apply real-world skills in Python, data analysis, machine learning, and visualization
Solve practical business problems using data-driven approaches
Demonstrate your abilities to potential employers through a portfolio of completed work
Gain confidence in handling data projects independently
By the end of this module, you’ll have a portfolio of capstone projects that not only reflects your technical expertise but also shows your problem-solving skills and readiness for a professional data science role.
APTITUDES
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Python Programming from Zero to Hero
Python Programming from Zero to Hero
Python Programming from Zero to Hero
Data Engineering
Data Engineering
Data Engineering
Introduction to Machine Learning
Introduction to Machine Learning
Introduction to Machine Learning
Classical Machine Learning Approaches
Classical Machine Learning Approaches
Classical Machine Learning Approaches
Applied Data Science and Deep Learning
Applied Data Science and Deep Learning
Applied Data Science and Deep Learning
Building a Data Science Portfolio
Building a Data Science Portfolio
Building a Data Science Portfolio
Comment postuler
Comment postuler
Comment postuler
Bourses
Aide financière
Aide financière
Processus
d'admission
Admissibilité
Admission
Étape 1 : Prenez rendez-vous avec un conseiller
Étape 2 : Préparez vos documents
Diplôme et relevés de notes (Secondaire, CEGEP, Collège ou Université)
Certificat de naissance (en français ou en anglais)
Preuve de statut au Canada
Preuve de compétence en français
CV à jour
Deux pièces d’identité gouvernementales avec photo
Étape 3 : Payez les frais d’admission
50 $ demande + 150 $ inscription
Étape 4 : Soumettez votre formulaire d’admission
Parlez à un conseiller
Financial Aid
Scholarships
Scholarships
Admission process
Eligibility
Our financial partners offer loans and personalized support to local entrepreneurs and internationally trained professionals.
You can also apply with the government to get financial aid through the AFE loan program (Aide financière aux études/Student financial assistance).
➔ Attend an Info Session
➔ Meet an Advisor
➔ Submit Documents
➔ Get Scholarship
➔ Begin your Career
Attend an infosession
Scholarships
Financial Aid
Scholarships
Scholarships
Admission process
Eligibility
Our financial partners offer loans and personalized support to local entrepreneurs and internationally trained professionals.
You can also apply with the government to get financial aid through the AFE loan program (Aide financière aux études/Student financial assistance).
➔ Attend an Info Session
➔ Meet an Advisor
➔ Submit Documents
➔ Get Scholarship
➔ Begin your Career
Attend an infosession
Scholarships
Financial Aid
Scholarships
Scholarships
Admission process
Eligibility
Our financial partners offer loans and personalized support to local entrepreneurs and internationally trained professionals.
You can also apply with the government to get financial aid through the AFE loan program (Aide financière aux études/Student financial assistance).
➔ Attend an Info Session
➔ Meet an Advisor
➔ Submit Documents
➔ Get Scholarship
➔ Begin your Career
Attend an infosession
Scholarships
Contactez-nous
Contactez-nous
Contactez-nous
Meet & Greet
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
Meet & Greet
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
Meet & Greet
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
Planifiez une rencontre avec nos conseillers et explorez toutes les options au CTIM.
1:1 with Advisor
Rencontrez-nous
Planifiez une rencontre avec nos conseillers et explorez toutes les options au CTIM.
1:1 with Advisor
Rencontrez-nous
Planifiez une rencontre avec nos conseillers et explorez toutes les options au CTIM.
1:1 with Advisor
Rencontrez-nous
Info Sessions
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
i
Info Sessions
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
i
Info Sessions
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Meet us
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Prêt pour l'emploi
Prêt pour l'emploi
Prêt pour l'emploi
Événements de réseautage
Événements de réseautage
Aide financière
Préparation de CV
Mentorat et conseils
Préparation de portfolio
Événements de réseautage
Événements qui vous préparent à l'emploi
Au CTIM, nos programmes vont au-delà de la salle de classe. Nous créons des occasions de réseautage, de développement et d’embauche à travers divers événements axés sur la carrière :
Événements de réseautage intelligent
Des sessions ciblées pour vous connecter avec des pros du secteur et des entreprises qui recrutent.
Présentations menées par nos enseignants
Nos enseignants partagent leur propre réseau pour vous ouvrir à des opportunités concrètes.
Réseautage entre pairs
Échangez avec vos collègues et notre réseau d'anciens étudiantsd pour tisser des liens solides dans votre domaine.
Rencontrez les recruteurs
Participez à des événements exclusifs où les recruteurs viennent rencontrer, guider et embaucher les étudiants du CTIM.
Attend an infosession
Préparation de CV
Événements de réseautage
Resume Preperation
interview Preperation
Préparation de protfolio
Craft a Winning Resume & Land Your Dream Job Faster!
Your resume is your first impression on potential employers
Join our Resume Preparation Workshop to learn how to create a standout resume that highlights your skills, experience, and strengths in a way that grabs employers’ attention.
Assistez à une Info-session
Préparation de CV
Événements de réseautage
Resume Preperation
interview Preperation
Préparation de protfolio
Craft a Winning Resume & Land Your Dream Job Faster!
Your resume is your first impression on potential employers
Join our Resume Preparation Workshop to learn how to create a standout resume that highlights your skills, experience, and strengths in a way that grabs employers’ attention.
Assistez à une Info-session
Préparation de CV
Événements de réseautage
Resume Preperation
interview Preperation
Préparation de protfolio
Craft a Winning Resume & Land Your Dream Job Faster!
Your resume is your first impression on potential employers
Join our Resume Preparation Workshop to learn how to create a standout resume that highlights your skills, experience, and strengths in a way that grabs employers’ attention.
Assistez à une Info-session
Plein feu sur nos enseignants
Mojtaba Faramarzi
Scientifique de recherche appliquée
Mojtaba est doctorant en apprentissage automatique à l’institut Mila et détient deux maîtrises—l’une en apprentissage automatique de Mila et l’autre en génie logiciel de l’Université Concordia. Il a collaboré avec des entreprises de premier plan telles qu’Amazon, Microsoft, SAP et Ericsson. Fort d’une expérience en enseignement (McGill) et en industrie, il aide les étudiants à développer esprit critique et compétences pratiques.
Michel Chamoun
Analyste d'affaires et science des données
Michel est un développeur et consultant hautement qualifié, expert en IA, analyse de données et optimisation des processus. Au sein de l’équipe GenAI, il a conçu des preuves de concept exploitant les capacités de compréhension du langage naturel de chatGPT et a intégré des modules d’IA sur Microsoft Azure. Comme consultant en stratégie et opérations, il a conçu des algorithmes pour le profilage des accès utilisateurs.
Mojtaba Ghasemi
Sénior scientifique de données
Scientifique des données axé sur les résultats, titulaire d’un doctorat en génie biomédical, spécialisé en analytique avancée, modélisation prédictive et apprentissage automatique. Avec 5 ans d’expérience et travaillant actuellement comme scientifique des données principal, Mojtaba excelle à traduire des analyses complexes pour un public non technique et à diriger des équipes interfonctionnelles livrant des solutions d’affaires percutantes.
Iraj Hedayati
Chef de l'ingénierie data
Iraj Hedayati est un ingénieur en données, chevronné avec plus de dix ans d’expérience dans la conception et la mise à l’échelle d’infrastructures de données au sein d’entreprises technologiques en forte croissance. Il travaille actuellement comme consultant chez Apple, où il se spécialise dans les systèmes distribués, Apache Spark et le développement backend. Iraj enseigne des cours en ingénierie des données axés sur des applications concrètes en traitement de Big Data, infrastructures infonuagiques et pipelines de données modernes.
À la réussite du programme, le CTIM délivre à l’étudiant une AEC (Attestation d’études collégiales)
Analyste en intelligence d’affaires et visualisation (LEA.CV) – 900 h –
Mise en lumière de l'instructeur
Mojtaba Faramarzi
Scientifique de recherche appliquée
10 + années
Mojtaba est doctorant en apprentissage automatique à l’institut Mila et détient deux maîtrises—l’une en apprentissage automatique de Mila et l’autre en génie logiciel de l’Université Concordia. Il a collaboré avec des entreprises de premier plan telles qu’Amazon, Microsoft, SAP et Ericsson. Fort d’une expérience en enseignement (McGill) et en industrie, il aide les étudiants à développer esprit critique et compétences pratiques.
Michel Chamoun
Analyste d'affaires et science des données
5 années
Michel est un développeur et consultant hautement qualifié, expert en IA, analyse de données et optimisation des processus. Au sein de l’équipe GenAI, il a conçu des preuves de concept exploitant les capacités de compréhension du langage naturel de chatGPT et a intégré des modules d’IA sur Microsoft Azure. Comme consultant en stratégie et opérations, il a conçu des algorithmes pour le profilage des accès utilisateurs.
Mojtaba Ghasemi
Sénior scientifique de données
10 + années
Scientifique des données axé sur les résultats, titulaire d’un doctorat en génie biomédical, spécialisé en analytique avancée, modélisation prédictive et apprentissage automatique. Avec 5 ans d’expérience et travaillant actuellement comme scientifique des données principal, Mojtaba excelle à traduire des analyses complexes pour un public non technique et à diriger des équipes interfonctionnelles livrant des solutions d’affaires percutantes.
Iraj Hedayati
Chef de l'ingénierie data
15 + années
Iraj Hedayati est un ingénieur en données, chevronné avec plus de dix ans d’expérience dans la conception et la mise à l’échelle d’infrastructures de données au sein d’entreprises technologiques en forte croissance. Il travaille actuellement comme consultant chez Apple, où il se spécialise dans les systèmes distribués, Apache Spark et le développement backend. Iraj enseigne des cours en ingénierie des données axés sur des applications concrètes en traitement de Big Data, infrastructures infonuagiques et pipelines de données modernes.
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Collège des technologies de l’information de Montréal
200 - 1255 Boulevard Robert-Bourassa
Montréal, Québec H3B 3B2
+1 514 312 2383
Collège des technologies de l’information de Montréal
200 - 1255 Boulevard Robert-Bourassa
Montréal, Québec H3B 3B2
+1 514 312 2383
Collège des technologies de l’information de Montréal
200 - 1255 Boulevard Robert-Bourassa
Montréal, Québec H3B 3B2
+1 514 312 2383
Collège des technologies de l’information de Montréal
200 - 1255 Boulevard Robert-Bourassa
Montréal, Québec H3B 3B2
+1 514 312 2383