Professional education for data science, analytics, and AI at Waterloo

Whether you are in finance, health care, marketing, or policy development, technology gives us the opportunity to store and analyze more data than ever before - and it's big data.

Learn to leverage data science and data analytics to accelerate your career, or enable your organization to innovate, compete, and stay agile in our rapidly changing marketplace. Explore WatSPEED's data-based courses and certificate programs.

Canada's most innovative universityLearn from leaders of innovation

In Maclean's University Rankings 2022, the University of Waterloo was ranked as the nation's most innovative university.

Top ranked STEM programsEngineering, math, and computer science

Learn from professors, researchers, and lecturers from one of Canada's top universities for engineering, math, and computer science.

Hands-on learning experience#1 in experiential learning

Waterloo is a pioneer and leader in experiential learning and our programs offer a hands-on learning experience.

Waterloo's data certificate programs

WatSPEED's data programs are designed to help professionals in the public and private sectors gain valuable skills to support career advancement, innovation, and decision-making. They offer you an opportunity to gain a competitive edge by learning modern data tools and techniques.

Data Analytics for Behavioural Insights Certificate Program

As advances in technology and the widespread availability of data rapidly progress, you can leverage data insights like never before. To help inform decisions, you need effective tools and techniques to analyze data, answer important questions about your communities, and inform policy development and decision-making.

This three-course certificate program extends beyond basic methods of coding and analysis, enabling you to interpret data findings from complex statistical analysis and directly apply them to decision-making and policy development. It is specifically designed for individuals who have a limited background in statistics and are interested in acquiring skills that will enable them to analyze data, extract insights, and apply them to decision-making. As part of this program, you will also discover how to understand and interpret data through a comprehensive introduction to statistics, regression analysis, Python, and R, and through unique case studies based on Canadian public data.

Establish the foundational knowledge you need to drive data-based decision-making and better understand how to interpret and leverage complex data for your organization.

What you'll learn

• Use basic methods of coding to conduct increasingly sophisticated statistical analysis with R and Python.
• Analyze data with the complex statistical methods used by social scientists.
• Draw conclusions that have the power to impact your decision-making and policy development.
• Draw behavioural insights to tell effective stories that support decision-making.
• Review unique case studies, based on publicly available Canadian data, and conduct analysis that can be used to draw behavioural insights.

Data Science Certificate Program

This four-course program, developed in partnership with the School of Continuing Studies at the University of Toronto, will give you the opportunity to learn from experts about advanced statistical modelling, machine learning and big data tools. You'll cover content essential for the toolbox of a data analytics professional, including neural networks and deep learning, programming languages and software used in data extraction and analysis, and data security, compliance, and privacy issues.

The course content covers the seven domains of INFORMS' Certified Analytics Professional (CAP) certification. You'll also learn how to represent data visually to help decision-makers understand your findings. Whether you work in operations, business intelligence or marketing communications, are a recent math or science graduate, or want to change or advance your career, this certificate will open up the in-demand field of big data.

What you'll learn

• Explore the evolution of data science and predictive analytics.
• Know statistical concepts and techniques including regression, correlation and clustering.
• Apply data management systems and technologies that reflect concern for security and privacy.
• Adopt techniques and technologies including data mining, neural network mapping and machine learning.
• Represent big data findings visually to aid decision-makers.

Foundations of Large Language Models

Technologies like OpenAI's ChatGPT and Google's Bard are changing the way we work and generative AI will become increasingly popular. As data professionals and developers, it is important to know how these tools work under the hood and how you can leverage large language models (LLMs) for your work.

LLMs have revolutionized the field of natural language processing (NLP) and are increasingly being used to solve a wide range of NLP problems in various industries. Understanding LLMs can help developers and data scientists, like you, to:

• Build better NLP models: LLMs are state-of-the-art models for many NLP tasks, and understanding how they work can help developers and data scientists to build better models and achieve better performance on their NLP tasks.
• Develop custom NLP applications: LLMs can be fine-tuned to specific NLP tasks, making them highly adaptable to different domains and use cases. Developers and data scientists who understand LLMs can leverage this flexibility to develop custom NLP applications for their specific needs.
• Optimize model performance: Understanding LLMs can help developers and data scientists to optimize model performance by selecting the appropriate architecture, prompt engineering, fine-tuning strategies, and downstream tasks for their specific use case.

This course will provide you with a comprehensive understanding of the latest techniques, tools, and applications of LLMs so you can build applications or processes and further improve your effectiveness and efficiency when working with large language models.

What you will learn

• How to apply and integrate various LLMs to an organization’s existing data infrastructure and systems. This includes understanding open-source alternatives to ChatGPT, Sydney, and Bard.
• Understanding the evolution of transformer architectures and the historical context behind ChatGPT, including different types of LLMs and their lifecycles (pretraining, fine-tuning, and inference).
• Familiarity with various machine learning paradigms, including unsupervised, supervised, self-supervised, and in-context learning.
• Knowledge of different downstream tasks that LLMs can be applied to, such as prediction, extraction, sequence labeling, sequence transformation, and generation.
• Ability to perform prompt engineering and effective fine-tuning, including prompt construction, effective completions, and understanding the tradeoffs between zero-shot, k-shot, domain/knowledge transfer, in-context learning, and supervised fine-tuning.
• Understanding LLMs as components in larger architectures, including their use in embeddings for dense retrieval, recommendations, clustering, synthetic data generation, negative mining, and managing model size through knowledge distillation, pruning, and quantization.