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.
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.
Talk to an Advisor
Talk to an Advisor
Talk to an Advisor
Program Overview
Program Overview
Program Overview
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.
Mentor-Guided Learning
Master industry skills on your own schedule.
Combine the flexibility of online learning with the accountability of weekly 1-on-1 sessions with an expert mentor.
AT A GLANCE
Flexible Core Learning
Don't wait for a class to start. Access our industry-aligned video modules and cloud labs 24/7. Progress through the curriculum at the speed that fits your life.
Weekly 1-on-1 Mentorship
You are never alone. Meet weekly with your dedicated mentor to review code, unblock challenges, and get personalized feedback on your progress.
Job-Ready Portfolio
Focus on output. Apply what you learn immediately by building realistic projects. Graduate with a GitHub portfolio that proves you can do the job
Credential
Professional Certificate
Financial Aid
Eligible for Loans & Flexible Payments
Format
Online 1-on-1
Live Mentorship
Enrollment
On Demand
Program Duration
6 Months
Mentorship
Flexible Weekly Check-in
masters
Mentor-Guided Learning
Master industry skills on your own schedule.
Combine the flexibility of online learning with the accountability of weekly 1-on-1 sessions with an expert mentor.
AT A GLANCE
Flexible Core Learning
Don't wait for a class to start. Access our industry-aligned video modules and cloud labs 24/7. Progress through the curriculum at the speed that fits your life.
Weekly 1-on-1 Mentorship
You are never alone. Meet weekly with your dedicated mentor to review code, unblock challenges, and get personalized feedback on your progress.
Job-Ready Portfolio
Focus on output. Apply what you learn immediately by building realistic projects. Graduate with a GitHub portfolio that proves you can do the job
Credential
Professional Certificate
Financial Aid
Eligible for Loans & Flexible Payments
Format
Online 1-on-1
Live Mentorship
Enrollment
On Demand
Program Duration
6 Months
Mentorship
Flexible Weekly Check-in
masters
Mentor-Guided Learning
Master industry skills on your own schedule.
Combine the flexibility of online learning with the accountability of weekly 1-on-1 sessions with an expert mentor.
AT A GLANCE
Flexible Core Learning
Don't wait for a class to start. Access our industry-aligned video modules and cloud labs 24/7. Progress through the curriculum at the speed that fits your life.
Weekly 1-on-1 Mentorship
You are never alone. Meet weekly with your dedicated mentor to review code, unblock challenges, and get personalized feedback on your progress.
Job-Ready Portfolio
Focus on output. Apply what you learn immediately by building realistic projects. Graduate with a GitHub portfolio that proves you can do the job
Credential
Professional Certificate
Financial Aid
Eligible for Loans & Flexible Payments
Format
Online 1-on-1
Live Mentorship
Enrollment
On Demand
Program Duration
6 Months
Mentorship
Flexible Weekly Check-in
masters
Course Outline
Course Outline
Course Outline
Download Outline
Introduction to Data Science
Data Science is the foundation for turning raw data into meaningful insights. In this module, you’ll explore how data science supports decisions, drives strategy, and creates real outcomes across organizations. You’ll walk through how data moves from initial collection to analysis and insight generation, and how data science teams work together to solve business problems. By the end, you won’t just understand what data science is—you’ll feel confident stepping into the data science process.
What You’ll Learn
How data science creates value and supports decisions
The workflow from collecting data to generating insights
Ways to identify meaningful trends, patterns, and signals
Communicating insights through visuals and storytelling
Ethical and responsible practices in data science
Data Engineering
Data engineering is where raw data becomes usable and reliable. In this module, you’ll learn how data flows through ETL pipelines and how to clean, transform, and prepare datasets for real downstream applications. You’ll work hands-on with Python libraries like NumPy and Pandas to handle datasets at scale, normalize them, and shape them for analysis. By the end, you’ll be comfortable building practical data workflows that support analytics, dashboards, and data science projects.
What You’ll Learn
ETL concepts and data pipeline workflows
Cleaning and preprocessing large structured and unstructured datasets
Using NumPy and Pandas to manipulate and transform data
Making data analysis-ready through normalization and reshaping
Creating visualizations that highlight important insights
Data Engineering
Data engineering is where raw data becomes usable and reliable. In this module, you’ll learn how data flows through ETL pipelines and how to clean, transform, and prepare datasets for real downstream applications. You’ll work hands-on with Python libraries like NumPy and Pandas to handle datasets at scale, normalize them, and shape them for analysis. By the end, you’ll be comfortable building practical data workflows that support analytics, dashboards, and data science projects.
What You’ll Learn
ETL concepts and data pipeline workflows
Cleaning and preprocessing large structured and unstructured datasets
Using NumPy and Pandas to manipulate and transform data
Making data analysis-ready through normalization and reshaping
Creating visualizations that highlight important insights
Data Engineering
Data engineering is where raw data becomes usable and reliable. In this module, you’ll learn how data flows through ETL pipelines and how to clean, transform, and prepare datasets for real downstream applications. You’ll work hands-on with Python libraries like NumPy and Pandas to handle datasets at scale, normalize them, and shape them for analysis. By the end, you’ll be comfortable building practical data workflows that support analytics, dashboards, and data science projects.
What You’ll Learn
ETL concepts and data pipeline workflows
Cleaning and preprocessing large structured and unstructured datasets
Using NumPy and Pandas to manipulate and transform data
Making data analysis-ready through normalization and reshaping
Creating visualizations that highlight important insights
Introduction to Machine Learning
Machine Learning turns data into predictions, classifications, and decisions. In this module, you’ll explore how ML models are built, trained, and evaluated in real projects. You’ll work hands-on with Scikit-learn to prepare data, engineer features, and compare model performance. By the end, you’ll be able to build and assess machine learning models that support real-world applications and decisions.
What You’ll Learn
How to prepare data and engineer useful features
Training classical machine learning models with Scikit-learn
Comparing and evaluating model performance
Applying validation and best practices before deployment
How ML supports data-driven decision-making
Introduction to Machine Learning
Machine Learning turns data into predictions, classifications, and decisions. In this module, you’ll explore how ML models are built, trained, and evaluated in real projects. You’ll work hands-on with Scikit-learn to prepare data, engineer features, and compare model performance. By the end, you’ll be able to build and assess machine learning models that support real-world applications and decisions.
What You’ll Learn
How to prepare data and engineer useful features
Training classical machine learning models with Scikit-learn
Comparing and evaluating model performance
Applying validation and best practices before deployment
How ML supports data-driven decision-making
Introduction to Machine Learning
Machine Learning turns data into predictions, classifications, and decisions. In this module, you’ll explore how ML models are built, trained, and evaluated in real projects. You’ll work hands-on with Scikit-learn to prepare data, engineer features, and compare model performance. By the end, you’ll be able to build and assess machine learning models that support real-world applications and decisions.
What You’ll Learn
How to prepare data and engineer useful features
Training classical machine learning models with Scikit-learn
Comparing and evaluating model performance
Applying validation and best practices before deployment
How ML supports data-driven decision-making
Applied Data Science and Deep Learning
Deep learning powers modern AI applications, from image recognition to natural language processing. In this module, you’ll work hands-on with PyTorch and TensorFlow to design, train, and deploy neural networks. You’ll explore real examples like image classification, predictive analytics, and NLP using transfer learning. As you dive into CNNs, RNNs, transformer models, and large language models, you’ll see how deep learning supports real-world solutions across multiple industries.
What You’ll Learn
Building neural networks with PyTorch and TensorFlow
Applying deep learning to images, text, and sequence data
CNNs, RNNs, transformers, and modern LLM techniques
Using HuggingFace for NLP projects
MLOps workflows for deploying and maintaining models
Real-world applications of deep learning across industries
Applied Data Science and Deep Learning
Deep learning powers modern AI applications, from image recognition to natural language processing. In this module, you’ll work hands-on with PyTorch and TensorFlow to design, train, and deploy neural networks. You’ll explore real examples like image classification, predictive analytics, and NLP using transfer learning. As you dive into CNNs, RNNs, transformer models, and large language models, you’ll see how deep learning supports real-world solutions across multiple industries.
What You’ll Learn
Building neural networks with PyTorch and TensorFlow
Applying deep learning to images, text, and sequence data
CNNs, RNNs, transformers, and modern LLM techniques
Using HuggingFace for NLP projects
MLOps workflows for deploying and maintaining models
Real-world applications of deep learning across industries
Applied Data Science and Deep Learning
Deep learning powers modern AI applications, from image recognition to natural language processing. In this module, you’ll work hands-on with PyTorch and TensorFlow to design, train, and deploy neural networks. You’ll explore real examples like image classification, predictive analytics, and NLP using transfer learning. As you dive into CNNs, RNNs, transformer models, and large language models, you’ll see how deep learning supports real-world solutions across multiple industries.
What You’ll Learn
Building neural networks with PyTorch and TensorFlow
Applying deep learning to images, text, and sequence data
CNNs, RNNs, transformers, and modern LLM techniques
Using HuggingFace for NLP projects
MLOps workflows for deploying and maintaining models
Real-world applications of deep learning across industries
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Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
SKILLS GAINED
……………………..
Introduction to Data Science
Data Science is the foundation for turning raw data into meaningful insights. In this module, you’ll explore how data science supports decisions, drives strategy, and creates real outcomes across organizations. You’ll walk through how data moves from initial collection to analysis and insight generation, and how data science teams work together to solve business problems. By the end, you won’t just understand what data science is—you’ll feel confident stepping into the data science process.
What You’ll Learn
How data science creates value and supports decisions
The workflow from collecting data to generating insights
Ways to identify meaningful trends, patterns, and signals
Communicating insights through visuals and storytelling
Ethical and responsible practices in data science
SKILLS GAINED
……………………..
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Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
SKILLS GAINED
……………………..
Python Programming from Zero to Hero
Python is one of the most versatile programming languages today, powering everything from data analysis to automation and backend services. In this module, you’ll start with Python fundamentals—syntax, variables, loops, and logic—and gradually move into building structured, reusable programs using functions, modules, and object-oriented techniques. By the end, you’ll be able to write clean, maintainable code and apply Python confidently in practical projects.
What You’ll Learn
Python fundamentals: variables, logic, loops, decision flow
Structuring programs with functions and modules
Basics of object-oriented programming
Debugging, testing, and writing maintainable scripts
Using Python to prepare and process datasets
Intro to building simple REST APIs
SKILLS GAINED
……………………..
Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
Framer is a web builder for creative pros. Be sure to check out framer.com to learn more.
SKILLS GAINED
……………………..
Data Engineering
Data engineering is where raw data becomes usable and reliable. In this module, you’ll learn how data flows through ETL pipelines and how to clean, transform, and prepare datasets for real downstream applications. You’ll work hands-on with Python libraries like NumPy and Pandas to handle datasets at scale, normalize them, and shape them for analysis. By the end, you’ll be comfortable building practical data workflows that support analytics, dashboards, and data science projects.
What You’ll Learn
ETL concepts and data pipeline workflows
Cleaning and preprocessing large structured and unstructured datasets
Using NumPy and Pandas to manipulate and transform data
Making data analysis-ready through normalization and reshaping
Creating visualizations that highlight important insights
SKILLS GAINED
……………………..
Data Engineering
Data engineering is where raw data becomes usable and reliable. In this module, you’ll learn how data flows through ETL pipelines and how to clean, transform, and prepare datasets for real downstream applications. You’ll work hands-on with Python libraries like NumPy and Pandas to handle datasets at scale, normalize them, and shape them for analysis. By the end, you’ll be comfortable building practical data workflows that support analytics, dashboards, and data science projects.
What You’ll Learn
ETL concepts and data pipeline workflows
Cleaning and preprocessing large structured and unstructured datasets
Using NumPy and Pandas to manipulate and transform data
Making data analysis-ready through normalization and reshaping
Creating visualizations that highlight important insights
SKILLS GAINED
……………………..
Introduction to Machine Learning
Machine Learning turns data into predictions, classifications, and decisions. In this module, you’ll explore how ML models are built, trained, and evaluated in real projects. You’ll work hands-on with Scikit-learn to prepare data, engineer features, and compare model performance. By the end, you’ll be able to build and assess machine learning models that support real-world applications and decisions.
What You’ll Learn
How to prepare data and engineer useful features
Training classical machine learning models with Scikit-learn
Comparing and evaluating model performance
Applying validation and best practices before deployment
How ML supports data-driven decision-making
SKILLS GAINED
……………………..
Introduction to Machine Learning
Machine Learning turns data into predictions, classifications, and decisions. In this module, you’ll explore how ML models are built, trained, and evaluated in real projects. You’ll work hands-on with Scikit-learn to prepare data, engineer features, and compare model performance. By the end, you’ll be able to build and assess machine learning models that support real-world applications and decisions.
What You’ll Learn
How to prepare data and engineer useful features
Training classical machine learning models with Scikit-learn
Comparing and evaluating model performance
Applying validation and best practices before deployment
How ML supports data-driven decision-making
SKILLS GAINED
……………………..
Classical Machine Learning Approaches
Classical machine learning models remain at the core of many real-world data solutions. In this module, you’ll take a deeper dive into traditional approaches, focusing on how to engineer strong features from numeric data and train models that perform reliably. Along the way, you’ll learn deployment considerations and optimization practices so your models are scalable, maintainable, and ready for real use.
What You’ll Learn
How to engineer features that improve model performance
Training and tuning models using Scikit-learn
Classical ML algorithms and workflows in depth
Evaluating and optimizing models for production
Practical considerations for running ML at scale
SKILLS GAINED
……………………..
Classical Machine Learning Approaches
Classical machine learning models remain at the core of many real-world data solutions. In this module, you’ll take a deeper dive into traditional approaches, focusing on how to engineer strong features from numeric data and train models that perform reliably. Along the way, you’ll learn deployment considerations and optimization practices so your models are scalable, maintainable, and ready for real use.
What You’ll Learn
How to engineer features that improve model performance
Training and tuning models using Scikit-learn
Classical ML algorithms and workflows in depth
Evaluating and optimizing models for production
Practical considerations for running ML at scale
SKILLS GAINED
……………………..
Applied Data Science and Deep Learning
Deep learning powers modern AI applications, from image recognition to natural language processing. In this module, you’ll work hands-on with PyTorch and TensorFlow to design, train, and deploy neural networks. You’ll explore real examples like image classification, predictive analytics, and NLP using transfer learning. As you dive into CNNs, RNNs, transformer models, and large language models, you’ll see how deep learning supports real-world solutions across multiple industries.
What You’ll Learn
Building neural networks with PyTorch and TensorFlow
Applying deep learning to images, text, and sequence data
CNNs, RNNs, transformers, and modern LLM techniques
Using HuggingFace for NLP projects
MLOps workflows for deploying and maintaining models
Real-world applications of deep learning across industries
SKILLS GAINED
……………………..
Applied Data Science and Deep Learning
Deep learning powers modern AI applications, from image recognition to natural language processing. In this module, you’ll work hands-on with PyTorch and TensorFlow to design, train, and deploy neural networks. You’ll explore real examples like image classification, predictive analytics, and NLP using transfer learning. As you dive into CNNs, RNNs, transformer models, and large language models, you’ll see how deep learning supports real-world solutions across multiple industries.
What You’ll Learn
Building neural networks with PyTorch and TensorFlow
Applying deep learning to images, text, and sequence data
CNNs, RNNs, transformers, and modern LLM techniques
Using HuggingFace for NLP projects
MLOps workflows for deploying and maintaining models
Real-world applications of deep learning across industries
SKILLS GAINED
……………………..
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple 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.
SKILLS GAINED
……………………..
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple 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.
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
SKILLS GAINED
……………………..
Introduction to Data Science
Introduction to Data Science
Python Programming from Zero to Hero
Python is one of the most versatile programming languages today, powering everything from data analysis to automation and backend services. In this module, you’ll start with Python fundamentals—syntax, variables, loops, and logic—and gradually move into building structured, reusable programs using functions, modules, and object-oriented techniques. By the end, you’ll be able to write clean, maintainable code and apply Python confidently in practical projects.
What You’ll Learn
Python fundamentals: variables, logic, loops, decision flow
Structuring programs with functions and modules
Basics of object-oriented programming
Debugging, testing, and writing maintainable scripts
Using Python to prepare and process datasets
Intro to building simple REST APIs
Python Programming from Zero to Hero
Python is one of the most versatile programming languages today, powering everything from data analysis to automation and backend services. In this module, you’ll start with Python fundamentals—syntax, variables, loops, and logic—and gradually move into building structured, reusable programs using functions, modules, and object-oriented techniques. By the end, you’ll be able to write clean, maintainable code and apply Python confidently in practical projects.
What You’ll Learn
Python fundamentals: variables, logic, loops, decision flow
Structuring programs with functions and modules
Basics of object-oriented programming
Debugging, testing, and writing maintainable scripts
Using Python to prepare and process datasets
Intro to building simple REST APIs
Python Programming from Zero to Hero
Python is one of the most versatile programming languages today, powering everything from data analysis to automation and backend services. In this module, you’ll start with Python fundamentals—syntax, variables, loops, and logic—and gradually move into building structured, reusable programs using functions, modules, and object-oriented techniques. By the end, you’ll be able to write clean, maintainable code and apply Python confidently in practical projects.
What You’ll Learn
Python fundamentals: variables, logic, loops, decision flow
Structuring programs with functions and modules
Basics of object-oriented programming
Debugging, testing, and writing maintainable scripts
Using Python to prepare and process datasets
Intro to building simple REST APIs
Classical Machine Learning Approaches
Classical machine learning models remain at the core of many real-world data solutions. In this module, you’ll take a deeper dive into traditional approaches, focusing on how to engineer strong features from numeric data and train models that perform reliably. Along the way, you’ll learn deployment considerations and optimization practices so your models are scalable, maintainable, and ready for real use.
What You’ll Learn
How to engineer features that improve model performance
Training and tuning models using Scikit-learn
Classical ML algorithms and workflows in depth
Evaluating and optimizing models for production
Practical considerations for running ML at scale
Classical Machine Learning Approaches
Classical machine learning models remain at the core of many real-world data solutions. In this module, you’ll take a deeper dive into traditional approaches, focusing on how to engineer strong features from numeric data and train models that perform reliably. Along the way, you’ll learn deployment considerations and optimization practices so your models are scalable, maintainable, and ready for real use.
What You’ll Learn
How to engineer features that improve model performance
Training and tuning models using Scikit-learn
Classical ML algorithms and workflows in depth
Evaluating and optimizing models for production
Practical considerations for running ML at scale
Classical Machine Learning Approaches
Classical machine learning models remain at the core of many real-world data solutions. In this module, you’ll take a deeper dive into traditional approaches, focusing on how to engineer strong features from numeric data and train models that perform reliably. Along the way, you’ll learn deployment considerations and optimization practices so your models are scalable, maintainable, and ready for real use.
What You’ll Learn
How to engineer features that improve model performance
Training and tuning models using Scikit-learn
Classical ML algorithms and workflows in depth
Evaluating and optimizing models for production
Practical considerations for running ML at scale
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple 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.
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple 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.
Building a Data Science Portfolio
Capstone projects are the best way to turn your learning into tangible results. Throughout this program, you’ll complete multiple 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.
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
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
1:1 with Advisor
Meet us
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
1:1 with Advisor
Meet us
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
1:1 with Advisor
Meet us
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
i
Reach us
1:1 Advising
Schedule a meeting with our Advisors and discuss all the opportunities at MCIT.
Talk to an Advisor
Info Sessions
Join our info sessions that are held periodically toknow more about our programs and offerings.
Attend an Info-Session
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How to Apply
Scholarships
Financial Aid
Financial Aid
Admission
process
Eligibility
Admission Process
Step 1: Book an Appointment with an Advisor
Step 2: Prepare Your Documents
Diploma and transcripts (High School, CEGEP, College, or University)
Birth Certificate (in English or French)
Proof of Canadian status
French language proficiency proof
Current resume
Two government-issued photo IDs
Step 3: Pay Application Fees
$50 application + $150 registration.
Step 4: Submit Your Application Form
Talk to an Advisor
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
Become job ready
Networking Events
Networking Events
Financial Aid
Resume Preperation
Mentorship & Guidance:
Portfolio Preperation
Networking Events
Events That Make You Job-Ready
At MCIT, our programs go beyond the classroom. We create opportunities to connect, grow, and get hired through a range of career-focused events:
Intelligent Networking Events
Curated sessions designed to connect you with industry professionals and hiring companies.Instructor-Led Introductions
Our instructors share their own professional networks, opening doors to real-world opportunities.Peer-to-Peer Networking
Engage with classmates and alumni to build meaningful connections within your industry.Meet the Recruiter
Participate in exclusive events where recruiters come to meet, mentor, and hire MCIT students.
Attend an infosession
Resume preperation
Networking Events
Resume Preperation
interview Preperation
Portfolio
Preparation
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.
Attend an infosession
Resume preperation
Networking Events
Resume Preperation
interview Preperation
Portfolio
Preparation
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.
Attend an infosession
Resume preperation
Networking Events
Resume Preperation
interview Preperation
Portfolio
Preparation
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.
Attend an infosession
Instructor Spotlight
Mojtaba Faramarzi
Applied Research Scientist
Mojtaba is a Ph.D. student in Machine Learning at the University of Montréal’s Mila institute and holds two master’s degrees—one in Machine Learning from Mila and another in Software Engineering from Concordia University. He has worked with leading companies such as Amazon, Microsoft, SAP, and Ericsson. With experience in both teaching (McGill) and industry, he helps students build critical thinking and real-world skills.
Michel Chamoun
Data Science & Business Analyst
Michel is an expert AI developer and consultant who leverages skills in AI, data analysis, and optimization. He has built GenAI concepts using ChatGPT and implemented AI solutions on Microsoft Azure
Mojtaba Ghasemi
Senior Data Scientist
As a Senior Data Scientist with a Ph.D. in Biomedical Engineering, Ghasemi specializes in advanced analytics and machine learning. He has 5 years of experience translating complex insights and leading teams to deliver impactful business solutions.
Iraj Hedayati
Data Engineering Lead
Iraj Hedayati is a seasoned Data Engineer with over a decade of experience scaling data infrastructure for high-growth tech companies. Currently a consultant at Apple, he specializes in distributed systems, Spark, and backend development. As an instructor, Iraj focuses on real-world applications in big data and cloud infrastructure, drawing from his extensive background in leading large-scale migrations and optimizing modern data pipelines.
Upon successful completion the college grants the student an AEC (Attestation d’études collégiale)
Business Intelligence and Visualization Analyst (LEA.CV)
-900 hrs-
Intructor spotlight
Mojtaba Faramarzi
Applied Research Scientist
Mojtaba is a Ph.D. student in Machine Learning at the University of Montréal’s Mila institute and holds two master’s degrees—one in Machine Learning from Mila and another in Software Engineering from Concordia University. He has worked with leading companies such as Amazon, Microsoft, SAP, and Ericsson. With experience in both teaching (McGill) and industry, he helps students build critical thinking and real-world skills.
Michel Chamoun
Data Science & Business Analyst
Michel is an expert AI developer and consultant who leverages skills in AI, data analysis, and optimization. He has built GenAI concepts using ChatGPT and implemented AI solutions on Microsoft Azure
Mojtaba Ghasemi
Senior Data Scientist
As a Senior Data Scientist with a Ph.D. in Biomedical Engineering, Ghasemi specializes in advanced analytics and machine learning. He has 5 years of experience translating complex insights and leading teams to deliver impactful business solutions.
Iraj Hedayati
Data Engineering Lead
Iraj Hedayati is a seasoned Data Engineer with over a decade of experience scaling data infrastructure for high-growth tech companies. Currently a consultant at Apple, he specializes in distributed systems, Spark, and backend development. As an instructor, Iraj focuses on real-world applications in big data and cloud infrastructure, drawing from his extensive background in leading large-scale migrations and optimizing modern data pipelines.
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Montreal College of Information Technology
200-1255 Robert-Bourassa Blvd.
Montreal, Quebec H3B 3B2
+1 514 312 2383
info@montrealcollege.ca
Montreal College of Information Technology
Collège des technologies de l’information de Montréal
200-1255 Robert-Bourassa Blvd.
Montreal, Quebec H3B 3B2
+1 514 312 2383
info@montrealcollege.ca
Montreal College of Information Technology
Collège des technologies de l’information de Montréal
200-1255 Robert-Bourassa Blvd.
Montreal, Quebec H3B 3B2
+1 514 312 2383
info@montrealcollege.ca
Montreal College of Information Technology
Collège des technologies de l’information de Montréal
200-1255 Robert-Bourassa Blvd.
Montreal, Quebec H3B 3B2
+1 514 312 2383
info@montrealcollege.ca