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Certificates
Take your career to the next level by learning to work through a complete data science pipeline and to extract value from data. This program is available online.
Certificates
Shape your business or organization by turning insights obtained from data analysis into actionable business outcomes and strategies. This program is available in person and online.
Certificates
This advanced and practical non-credit professional development certificate program is designed to equip professionals with actionable industry-relevant knowledge and skills required to be fully functional data scientists and Al developers.
Certificates
This program is currently closed for admissions. To explore alternative programs available to you at this time, please contact info.conted@mcgill.ca or call 514-398-6200.
This non-credit professional development certificate program is designed to equip professionals with the industry-relevant knowledge and skills required to manage and secure the software lifecycle in cloud-based environments. Admission to this program is not required to take the course YCIT 017 Cloud Computing Fundamentals. Registration in this course is open to all.
Want to start your career path in Data Analytics but don’t have a background in programming? Then McGill’s Micro-Credential in Data Analytics with Python is for you!
Courses & Workshops
YCBS 255 Statistical Machine Learning
(8 CEUs)
Fundamental statistical machine learning concepts and tools using Python. Emphasis on descriptive statistics, statistical distributions, random number generation, basic data visualization; linear regression; basic classification; error estimation: cross-validation, bias-variance trade-off; shrinkage methods; dimension reduction; beyond linearity: smoothing splines, local regression, additive models; tree and ensemble methods; powerful classifiers; unsupervised learning.
(30 INFORMS PDUs)
The PDUs earned through this non-credit-bearing course can be used by CAP and aCAP certificants to satisfy the 30 PDUs needed to renew their certification every three (3) years. More info.
(30 INFORMS PDUs)
The PDUs earned through this non-credit-bearing course can be used by CAP and aCAP certificants to satisfy the 30 PDUs needed to renew their certification every three (3) years. More info.
YCBS 256 Data Science for Business Decisions
(8 CEUs)
Overview of how data science can help drive business decisions and create new business models. Emphasis on data strategy, the data science lifecycle and process, business and analytics problem framing, overcoming challenges of implementing a data-driven business, including ethics, data governance, and privacy. Application of data science across various industries and business areas. Data science tools, including Alteryx and Tableau for data preparation, analysis, and visualization.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
(30 INFORMS PDUs)
The PDUs earned through this non-credit-bearing course can be used by CAP and aCAP certificants to satisfy the 30 PDUs needed to renew their certification every three (3) years. More info.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
(30 INFORMS PDUs)
The PDUs earned through this non-credit-bearing course can be used by CAP and aCAP certificants to satisfy the 30 PDUs needed to renew their certification every three (3) years. More info.
YCBS 257 Data at Scale
(9 CEUs)
Overview of various aspects of large data sets and how they are managed both on site and in the Cloud. Emphasis on hands-on experience from data ingestion to analysis of large data sets, both data-at-rest and data-in-motion (streaming data), including defining Big Data and its 5 V's: Volume, Velocity, Variety, Veracity, and Value.
YCBS 258 Practical Machine Learning
(9 CEUs)
This course aims to introduce participants to essential machine learning methods and techniques through an end-to-end machine learning project. Emphasis is placed on practical experience with machine learning using Python programming language, scikit-learn and TensorFlow, as well as on understanding classification and training models. The course will provide an introduction to artificial Neural Networks, deep learning, convolutional and recurrent neural nets and reinforcement learning.
YCBS 260 Statistics for Business Decision Making
(8 CEUs)
This course provides an overview of fundamental statistical and mathematical concepts needed to perform statistical data analysis to support business decisionmaking and projections such as probability, random variables, descriptive statistics, regression modelling, common probability distributions, experimental design.
YCBS 261 Data Analytics Fundamentals
(8 CEUs)
Introduction to fundamental analytical methods, tools and techniques used to collect, analyze, interpret and predict business outcomes based on data. Overview of NoSQL databases, RDBMS databases and data structures. Complete data processing cycle and predictive analytics using machine learning with Alteryx, Excel, SQL and Tableau to analyze data, create forecasts and models, design visualizations, and communicate insights.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
YCBS 262 Leading Data Science Projects & Teams
(8 CEUs)
Overview of organizational capabilities and structures required to successfully create a data-driven business culture, including analytics maturity models, an integrated approach to defining and staffing data science projects, roles and responsibilities within a data project, development of data products and services, AI Canvas, collaboration and innovation tools and techniques including Design Thinking. Challenges and best practices in data governance and compliance. Data Science tools and techniques including Alteryx, Tableau, GitHub, and Google Cloud Platform.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
This course is aligned with the IIBA’s Certification in Business Data Analytics (IIBA®-CBDA) competencies. More information.
YCBS 299 Data Science Capstone Project
(9 CEUs)
Integration and application of knowledge and skills gained during the program through hands-on projects supported by our industry partners to build a full data science pipeline from preparing, analyzing and visualizing data to building and testing models. Communication and presentation of insights and recommendations derived from data analysis using visualization and storytelling techniques.
YCIT 017 Cloud Computing Fundamentals
(7.5 CEUs)
Value proposition of cloud computing versus traditional computing models; the economics and history of computing in business; terminology associated with modern computing; nuanced differences between IaaS, PaaS and SaaS; containerization versus virtualization; identity, networking, and security best practices.
YCIT 018 Cloud Networking and Security
(7.5 CEUs)
Networking concepts and their application to cloud environments; configuration of hybrid and multi-cloud network connectivity; application of fundamental security principles in networking decisions.
YCIT 019 Cloud Architecture
(9 CEUs)
The power of containerization and its distinction from virtualization; features of the Linux kernel underpinning containerization; how to set up a Docker environment, build containers, and use an orchestration tool, namely, Kubernetes; additional tools for monitoring, sharing, and deploying applications.
YCIT 020 Advanced Cloud Architecture
(9 CEUs)
Advanced cloud-native architecture topics and tools to architect enterprise-scale application deployments. Service meshes, secrets management, routine management, monitoring and troubleshooting of Kubernetes clusters, and tools to manage enterprise-scale production clusters.
YCIT 021 DevOps Practices and Tools
(9 CEUs)
Introduction to DevOps culture, its benefits; and a variety of popular DevOps tools. Emphasis on the application of principles that allow teams to develop resilient software while reducing time to market. Establishing guardrails to mitigate risks; continuous development workflows.
YCIT 022 Site Reliability Engineering (SRE)
(9 CEUs)
Introduction to the theory of Service Level Objectives and Agreements, a principled way of describing and measuring the desired reliability of a service. Set up and application of these principles in any organization that builds software systems. Site Reliability Engineering (SRE) tools, techniques, and best practices.
YCNG 228 Predictive & Classification Modelling
(6 CEUs)
Predictive modelling is a process that uses data and statistics to predict outcomes using data models. These models are used to detect fraud, optimize marketing campaigns, reduce risk, manage resources and improve operations. This course will familiarize students with how to design experiments and optimize ML models and interpret their output. Students will test hypotheses by using different techniques, design experiments, use machine learning algorithms to evaluate the output of different machine learning models, and explore methods to increase precision and/or accuracy.
YCNG 229 Neural Networks & Deep Learning
(6 CEUs)
Artificial neural networks are a set of algorithms, inspired by the way human brain processes information, which are designed to recognize patterns. Deep learning is one of machine learning methods based on artificial neural networks. Neural networks and deep learning offer the most powerful techniques to deal with different aspects of data science such as natural language processing, computer vision and time series analysis. Building on the knowledge and skills acquired during YCBS 258 Practical Machine Learning course, this course will focus on the practical application of neural network models with the objective to develop students’ ability to implement them using Python and Keras.
YCNG 230 Intelligent Agents & Reinforcement Learning
(6 CEUs)
Intelligent agents are programs that can be applied to autonomously solve real-world optimization and planning problems, as well as help deal with incomplete information or uncertain environments. Reinforcement Learning is an approach to build goal-oriented intelligent agents, which help find strategies to optimize a desired outcome. In this course, students will explore the development of intelligent agents using different techniques, algorithms and approaches; the design and implementation of systems that exhibit intelligent behaviour through an end-to-end project; practical application of the most current programming tools, search methods, knowledge representation using logic and probability, planning and decision making under uncertainty and constraint satisfaction problems; reinforcement learning fundamentals including design of intelligent agents and integration with deep learning.
YCNG 231 Deep Learning for Computer Vision
(6 CEUs)
Computer vision is one of the fields of study, which has benefited tremendously from the latest advancements in artificial intelligence. Computer vision is concerned with helping machines learn to “see” and understand the context of their environment. It involves acquiring, processing, transforming, modifying, analyzing and understanding digital images or videos. The result is the extraction of data needed to understand the content of digital images, infer something from the environment, thus allowing complex decisions to be made. In this course, participants will learn more about computer vision from acquisition of image data to complex decision-making using deep learning methods and techniques. Image acquisition through sensors; feature detection; image classification, detection, segmentation. Convolutional Neural Networks (CNN) architectures and their applications. Most current computer vision applications, libraries and image databases will be explored.
YCNG 232 Natural Language Processing Fundamentals
(6 CEUs)
Natural Language Processing (NLP) is a subfield of artificial intelligence concerned with the ability of computers to process, understand and interpret human languages. Deep learning has revolutionized NLP by improving many aspects of the field since its transition from classic linguistic approaches. This course will provide an overview of Natural Language Processing fundamentals such as language models, word embeddings, Recurrent Neural Networks (RNNs), wide variety of applications of neural networks in NLP, intrinsic and extrinsic evaluation, most current NLP tools.
YCNG 233 Time Series Analysis Fundamentals
(6 CEUs)
Time series is an area of machine learning concerned with the analysis of series of data points ordered in time. Time series analysis is used in many fields to predict trends: from industrial machinery data, smart home, precision agriculture, cyber security, customer usage/engagement, marketing, asset management, finance, etc. Due to the temporal aspect of the data, time series data require specific preprocessing, feature engineering, algorithms, and validation strategy. This course will dive into the fundamentals of applying artificial intelligence techniques on time series data, statistical, machine and deep learning models for time series. Emphasis is placed on the main applications of time series analysis: forecasting, clustering and anomaly detection. Data science techniques applicable to time series. Deep learning methods, rolling predictions, online learning, backtesting.
YCNG 234 Internet of Things
(5 CEUs)
The Internet of Things (IoT) is an important source of relevant data for different industries. It permits to collect and process information about the physical world in real time. This course will examine applications of IoT, the fundamental technologies that support IoT devices and the transmission, processing, the analysis of the data they generate, and the fundamental software and hardware technologies that support IoT devices; data transmission protocols; tools to process the data generated by IoT devices; data analysis techniques that enable common applications related to IoT devices.
YCNG 235 Recommender Systems
(6 CEUs)
Have you ever wondered how the message: “Customers like you also purchased the following items…” appears on your screen when you are shopping online? Recommender Systems use machine learning algorithms that help users discover new products and services. There exists a wide spectrum of recommender systems applications that help users choose movies, restaurants, music to listen to, jobs to apply to, products to purchase, social media profiles, among many others. This course will explore fundamental concepts and techniques in recommender systems: similarity models, non-personalized, content-based, and hybrid systems; association rules mining; collaborative filtering: user-, item-, and graph-based models; matrix factorization; graph recommenders, sequential recommenders, evaluation of recommender systems.
Testimonial
As machine learning continues to become more present in our everyday lives, it's important that the next generation of business and technical leaders are equipped with the necessary skills to take advantage of the possibilities that this new technology brings. I look forward to sharing my own insight and knowledge on the topic, and hope that graduates of this course will go on to apply their learnings for positive impact.
Shibl Mourad
Canada engineering lead, DeepMind
Corporate learning
The McGill School of Continuing Studies (SCS) offers professional development and educational opportunities for corporate clients and local and international partners. Whether you are a multinational corporation, international organization, small or medium-sized enterprise, government body or educational institution seeking specialized courses or workshops or a comprehensive program for your employees, SCS has the solution for you.
