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Dec 01 2025

DSI Launches Unique Microcredential in Genetic Data Analysis

The human genome contains billions of molecules, and small variations within them can influence biological processes, traits, and disease. As the volume and complexity of genomic data grow, so does the need for researchers with specialized skills in genomic data analysis.

To meet this need, the Data Sciences Institute is launching the new Genetic Data Analysis Toolbox. This 6-week microcredential provides focused, hands-on training in tools and methods to analyze and interpret data generated by modern genomics technology.

Demand is growing for professionals with applied genomic data skills across biomedical research, clinical settings, and the biotechnology, pharmaceutical, agricultural, and forestry sectors. The Genetic Data Analysis Toolbox bridges the gap between traditional academic training and practical data skills required for today’s data-intensive projects. It is well suited for researchers, graduate students, and professionals who are working with, or preparing to work with, genomic datasets in academic, clinical, and applied settings.

In Genetic Data Analysis Toolbox, learners gain experience with contemporary software such as PLINK, MAGMA, and GRAF-pop, and receive an introduction to genome-wide association studies (GWAS) and population genetics methods. While participants are required to have a basic understanding of R, the microcredential is designed for learners with limited computational or statistical backgrounds.

“We are incredibly excited to be launching the first microcredential of this kind in Canada,” says Lisa Strug, Director, Data Sciences Institute and Tier 1 Canada Research Chair in Genome Data Science. “Through interdisciplinary training in genomics, data and computational science, we are able to fill a critical gap for research in the life and health sciences and prepare learners with applied and reproducible data science skills that are essential for clinical, academic, and industry settings.”

Unlike broader bioinformatics or genomics programs, this training is focused on genetic association studies, population genetics, and genome-wide analysis workflows. Learners develop hands-on experience with industry-standard tools in a short time period, making this an ideal option for those seeking immediately applicable targeted skills.

A subsidized registration fee is available to trainees and mentors in Strategic Training for Advanced Genetic Epidemiology (STAGE), a Canada-wide training program in molecular epidemiology and statistical -omics. Learners who have completed the DSI professional Certificates can register at a subsidized price with the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada.

Registration is open for the Genetic Data Analysis Toolbox microcredential. Don’t miss your chance to build your skills in genetic data analysis.

Nov 25 2025

AstraZeneca and the Data Sciences Institute Launch New Industry Partnership

As artificial intelligence transforms every sector, excellence in data science is essential for leaders across industries.

AstraZeneca Canada, one of the country’s leading research-based pharmaceutical companies, is joining the Data Sciences Institute (DSI) as an Industry Partner. This new partnership exemplifies the DSI Industry Membership model and the DSI’s role in connecting industry leaders with top-tier data science expertise drawn from across the University of Toronto’s unmatched breadth of excellence in various disciplines.

Through this new collaboration, AstraZeneca will play a founding role in both the upcoming DSI Talent Showcase and the new DSI Industry Speaker Series. The immersive format of the Talent Showcase will enable AstraZeneca to engage directly with DSI-affiliated trainees and their data science research in a live setting. As part of the Industry Speaker Series, leaders from AstraZeneca will open a window into how data science is transforming the pharmaceutical sector and driving real-world impact.

There is strong industry demand for data science and AI talent, and especially for collaboration opportunities with the University of Toronto community. As a gateway between industry and academia, the DSI is meeting this demand and advancing data science impact in Canada and beyond.

For industry partners, the DSI offers a unique combination of access to world-class students and trainees who are ready to make an impact, alongside platforms — such as the DSI Talent Showcase and Industry Speaker Series v where industry and academia come together.

“At AstraZeneca, we believe the future of pharmaceutical innovation is being shaped by data, AI, and advanced analytics. Partnering with the University of Toronto’s Data Sciences Institute enables us to create greater patient impact by collaborating with — and investing in — emerging talent who share our vision of using data to transform science and deliver life-changing medicines,” says Martin Booth, Head of Analytics & Data Excellence at AstraZeneca Canada.

As a DSI Industry Partner, AstraZeneca joins a group of forward-thinking organizations shaping the future of data science. This partnership is just the beginning: the DSI is welcoming more industry leaders to join this vision, with access to top talent and a collaborative hub of University of Toronto excellence.

The DSI is a hub for a breadth of data science and AI research and training. Access to this top talent — across undergraduates, doctoral students and postdoctoral fellows — was a key draw for AstraZeneca, who will have a unique opportunity to connect and innovate with high-performing students, researchers, and faculty.

Lisa Strug, Director of the Data Sciences Institute, highlights the importance of this partnership in shaping the future workforce and industry-ready research in data science.

“Building connections between academia and industry are a core part of our mission at the Data Sciences Institute. This partnership with AstraZeneca will allow new access to the talent pipeline and the research excellence that the DSI brings together from across the University of Toronto. I am delighted to see how this and future partnerships will continue to grow the impact of data science on industry innovation.”

The Data Sciences Institute welcomes other industry leaders ready to join in this vision!

See our Industry Partnership Model and contact parterships.dsi@utoronto.ca to get started.

About the Data Sciences Institute

The Data Sciences Institute at the University of Toronto is a hub for research, training, and industry collaboration. Bringing together faculty, students, and partners, DSI advances data-driven discovery through interdisciplinary research, hands-on training, and partnerships that translate data science into real-world impact. The DSI enhances and capitalizes on U of T’s established data science leadership in a city renowned for its data science and AI expertise.

About AstraZeneca Canada

AstraZeneca is a global, innovation-driven biopharmaceutical business with a focus on the discovery, development and commercialization of medicines that transform lives – in such therapy areas as Cardiovascular and Metabolic disease, Oncology, Respiratory and Immunology; and Rare Diseases. In Canada, AstraZeneca employs more than 2,600 people across the country and is one of the country’s leading R&D contributors, investing $285M in Canadian R&D in 2024. Through recent investments – including the expansion of our Mississauga-based Research & Development Hub and the creation of a new Alexion Development Hub focused on rare diseases – the company has roughly tripled its employee footprint in Canada since 2022. To learn more visit www.astrazeneca.ca.

Nov 24 2025

How Artificial Intelligence is Reshaping Qualitative Research

AI tools offer new and exciting possibilities for analyzing complex social phenomena by interpreting language, images, and behavior—and they also raise important methodological questions.

On November 20, the Data Sciences Institute hosted a workshop exploring the ways that artificial intelligence is reshaping qualitative research. This was an exciting chance to connect at the intersection of AI, data science and the social sciences, an area that is ripe for further research attention. AI, and the data as well as data science that make it possible, are playing increasingly important roles in policy and decision-making, underscoring the need for robust engagement with how these technologies can be applied to social phenomena.

Co-organizers Professors Ethan Fosse (Associate Director, Data Sciences Institute, Department of Sociology, University of Toronto Scarborough) and Nicholas Spence (Departments of Sociology and Health & Society, University of Toronto Scarborough) wanted to create a space to foster collaboration and interdisciplinary innovation between the computational and social sciences.

Their aim is to build on the momentum of what Fosse dubbed “the Toronto Revolution,” or the era following Geoffrey Hinton’s arrival at the University of Toronto, which has cemented the university’s status as the birthplace of the current AI boom. In addition to the collaborative potential as new tools help qualitative researchers obtain new insights from unstructured data, the workshop also aimed to tackle the ethical and methodological questions that these tools raise.

“The Toronto Revolution did more than improve image recognition,” says Professor Fosse. “By rendering unstructured data computationally accessible, it has enabled the infrastructure to finally bridge (and dissolve) the historic divide between qualitative and quantitative research in the social sciences. The objective is augmentation, not automation — using computational tools to extend, rather than replace, traditional qualitative research methods.”

Professor Susan McCahan (Department of Mechanical and Industrial Engineering. Faculty of Applied Science and Engineering; and Associate Vice President & Vice Provost, Digital Strategies, University of Toronto) explored some of these methodological questions by presenting methodologies used in her research. Still, she said, the question remains what new AI methods need to be developed given the rapidly changing technologies.

“Even if you could find a list of AI capabilities in the literature, it would need to be updated every day, given how fast as things are changing.”

Professor Corey Abramson (Sociology, Rice University) delivered the keynote, “On Patterns, Edge Cases, and Scalable Interpretation: Pragmatic Uses of AI in Qualitative Research.” He argued that, from filing cabinets to qualitative data analysis tools, fraught technologies are nothing new. AI technologies, too, are both generative and dangerous—neither all good nor all bad. While the wide breadth of qualitative research will mean AI has different relevance for different researchers, he argued that it is essential for all to at least understand it. After all, this technology is shaping the social world that researchers seek to understand.

Dr. Qin Liu (Senior Research Associate, Institute for Studies in Transdisciplinary Engineering Education and Practice, Faculty of Applied Science & Engineering, University of Toronto) similarly emphasized the continued role of humans in understanding the social world. She argued for maintaining human engagement with qualitative data and with the results of AI coding. While such coding can augment researchers’ capacity for certain types of analysis, human intelligence is needed to bridge gaps between different ways of thinking about the world.

Dr. Jordan Joseph Wadden (Clinical Ethicist, Centre for Clinical Ethics, Unity Health Toronto; Assistant Professor (status), Department of Family and Community Medicine, Temerty Faculty of Medicine, University of Toronto) highlighted ethical factors that developers, researchers, and policymakers must contend with, emphasizing that such ethical considerations are an opportunity to reflect and improve research and create positive impacts by identifying and addressing problems early.

Professor Spence echoed this sense that careful consideration makes qualitative research incorporating AI better. This integration, he noted, “is being done in great, published research.” Spence continued: “Humans have not been removed from the process” but rather have an “additional set of tools to look at social world in new ways.”

This workshop is a step towards establishing U of T as a leader in AI-assisted qualitative research. The organizers look to continue its momentum with further workshops bringing together an even greater breadth of researchers.

“U of T possess a high density of world-class qualitative scholars in the social sciences and other fields, and we inherit the legacy of the Toronto Revolution in deep learning,” concluded Professor Fosse. “By merging these strengths, we can establish the University of Toronto as a leader in rigorous, transparent, and theoretically grounded AI-assisted qualitative research.”

Nov 18 2025

Deploying AI Microcredential Sees Soaring Interest from Professionals

The Data Sciences Institute’s Deploying AI microcredential is a massive success in its first session. Organizations are seeking employees who can deploy AI responsibly and effectively—and professionals are responding with incredible demand for learning opportunities to build their skills.

Deploying AI is a hands-on opportunity to dive into the power of Large Language Models (LLMs). Learners develop the technical know-how and practical strategies needed to take AI from prototype to production.

Responding to input from learners and employers, DSI launched Deploying AI this fall and the enthusiasm from learners was palpable. The offering quickly sold out, reflecting soaring demand for the tools and confidence to deploy generative AI at scale.

The microcredential builds on the success of the DSI Data Science and Machine Learning Software Foundations Certificates offered with the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada. For learners that have completed a Certificate, this microcredential is a next step in deepening their AI capabilities.

Deploying AI offers a unique value as it is short, targeted learning experience dedicated to the frameworks, tools, and applied skills that professionals need to navigate the ethical, operational and organizational challenges of AI integration.

Deploying AI incorporates case studies from industry. Recently, learners heard from Sepehr Sisakht, an industry leader who is applying AI in practice as CEO of Shyftbase Inc. Learners gained a firsthand look at how AI deployment challenges are approached in a real-world setting. Applied perspectives like this bridge the gap between technical training and practical implementation, as learners gain insight into the workflows, trade-offs, and decisions faced by professionals deploying AI at scale.

“We developed Deploying AI in response to industry demand. Employers need people who can deploy and scale AI solutions in the real-world, now,” says Prof. Rohan Alexander, Director, Technical Skills and Curriculum (Faculty of Information and Department of Statistics, Faculty of Arts & Science).

“We’ve heard from our learners that our microcredential is making a real difference, whether they are preparing for that next great job or are in a role where they can put their learnings into practice right way. The response has been phenomenal.”

That response is echoed in the feedback on Deploying AI from learners. Learners highlighted the immediate usefulness of the microcredential, noting that the microcredential “provided a great primer to the tools and concepts for building AI solutions” and that the theory was useful and the practical knowledge immediately applicable to my work.”

Learners also emphasized the value of hands-on projects, noting that the assignments allowed them to showcase their ability and build credibility as AI professionals.

Registration is open for the February session of the Deploying AI microcredential. Don’t miss your chance to build the skills employers need to deploy AI effectively.

Sep 15 2025

Helping prepare Canada’s financial services workforce with AI literacy

How U of T’s Data Sciences Institute is helping professionals and employers stay competitive

As digital literacy becomes crucial to navigating the financial sector, the rapid pace of change can feel overwhelming for many mid-career experts and their employers, alike. This is particularly true in areas such as data sciences, artificial intelligence (AI) and machine learning.

Hiring professionals who are proactively adapting to the rise of data science and generative AI is one of the smartest ways businesses can stay competitive.

The University of Toronto’s Data Sciences Institute (DSI), a hub for data science research, training and partnerships, is helping businesses do just that by growing a pool of job-ready professionals. The DSI has upskilling certificates and microcredentials targeting the growing demand for professionals in finance and other fields to become knowledgeable in AI and understand how available tools can enhance their work.

Since launch, over 500 professionals have completed certificates, forming a job-ready talent pool aimed at bridging the gap between skilled candidates and employers. The DSI plays a vital role and is dedicated to creating new career paths for untapped talent to unleash their full potential.

As a manager of anti-money laundering and compliance data at Scotiabank, Matias Velastegui is one such professional. In search of a way to boost his technical skills, he completed Machine Learning Software Foundations, a 16-week intensive certificate at the DSI.

With the financial support of Upskill Canada, powered by Palette Skills and the Government of Canada, DSI certificates and microcredentials train mid-level professionals on digital literacy skills, including AI and machine learning, and are designed to meet the talent needs of high-growth sectors.

“It provided me with valuable tools that I’m confident I’ll apply in future professional and academic projects,” Velastegui says.

Certificates and microcredentials at the DSI are delivered live online, with weekly support through virtual office hours. Included are opportunities to learn from industry experts during case studies that provide participants with important insights into the professional world of data science and AI analytics.  In parallel to the technical training, learners have access to career transition support and exclusive employer networking events, which strengthens talent pipelines and helps keep Canadian businesses globally competitive.

While the certificate offers a comprehensive data science and machine learning overview, the DSI also offers a shorter, three-week microcredential on Deploying AI that focuses on building AI applications to augment work tasks. Deploying AI provides professionals with technical and strategic skills to turn AI prototypes into practical, workplace-ready solutions.

Velastegui says he decided to pursue the DSI certificate because it combined academic rigour with a practical focus. In his current role, he works extensively with SQL and Python languages. Through the DSI certificate, he learned new strategies for approaching complex queries, more efficient ways of handling data and best practices for data governance.

“From ensuring data integrity to grasping the mathematical principles of neural networks, professionals must be actively engaged in these advancements and prepared to evolve alongside them,” Velastegui says.

Now, he plans to hire other DSI upskilling participants to his own team. Velastegui is confident in the DSI’s focus on improving coding expertise and collaboration skills in virtual environments.

The DSI offers employers looking for new talent the opportunity to share job opportunities with its pool of participants, all of whom have post-secondary degrees and now have enhanced skills in data science and machine learning.

The certificates in Data Science and Machine Learning Software Foundations offer part-time coursework, so participants can continue working regular hours. Sakib Sadat, an intelligent automation program manager at Manulife, says the certificate is intensive but worth the time commitment.

Sadat says there has been a strong industry push for professionals to boost their skills using AI and machine learning tools, both for company standards and for personal development. Participating in the DSI’s certificate was a way for him to improve his productivity and the quality of his work.

Through the certificate, Sadat gained the ability to critically assess and identify which AI solutions presented by clients add real value in his current role.

“This certificate was one way to get the accumulated technical expertise to make those assessments on whether or not AI is going to actually be productive,” Sadat says.

Beyond the technical skills, Sadat and Velastegui agree that adding a University of Toronto AI-related certification to your resume is a good way to stand out in a pool of professionals, either when looking to move up internally or make a career switch.

“From a career progression standpoint, AI is a personal brand and selling feature in the industry, whether you’re in IT or not,” Sadat adds.

For professionals looking to learn more about the DSI’s upskilling offerings and employers accessing the DSI professional talent pool, visit certificates.datasciences.utoronto.ca.

This story was created by Content Works, Postmedia’s commercial content division, on behalf of the Data Sciences Institute.