Certificates and Sessions

Gain essential technical skills for transitioning into data science and machine learning roles through our engaging online instructor-led live webinars. All participants complete four core modules together, followed by two elective modules in either data science or machine learning, based on their chosen certificate track.  

This module provides a foundational understanding of Unix shell, Git version control, and Python programming, with an emphasis on reproducibility principles. Participants will gain proficiency in shell commands, file navigation, Git repositories, collaborative workflows, and Python fundamentals, especially functions, object-oriented programming, and NumPy for data analysis. 

Full module description and learning outcomes on GitHub (Shell) (Git) (Python).

This module focuses on essential SQL skills for data scientists, dataset ingestions, query design, relational database management, data modeling, and data privacy adherence. Participants learn querying techniques, problem-solving through live coding, and legal considerations around data sharing. 

Full module description and learning outcomes on GitHub (SQL).

Participants gain proficiency in designing, implementing, and testing logistic regression and classification models, validated with resampling techniques. The module explores the differences between prediction and inference, model interpretability, bias-variance trade-offs, and ethical considerations in decision-making based on model results. 

Full module description and learning outcomes on GitHub (Applying Statistical Concepts).

This module covers the fundamental components of designing and maintaining Machine Learning (ML) models in production. Participants learn about data engineering, feature engineering, hyperparameter tuning, model deployment, model explainability, and logging, experiment tracking, and monitoring techniques. 

Full module description and learning outcomes on GitHub (Production).

  • Proficiency in Unix shell, Git, Python, and SQL.
  • Statistical modeling for regression and classification.
  • Understanding reproducibility and collaboration principles.
  • Designing and maintaining machine learning systems in production.
  • Experiment tracking, logging, and monitoring techniques

This module introduces the essentials of sampling, probability, and survey methodology, including simple probability samples, stratified sampling, cluster sampling, dealing with non-response, estimating, and survey quality. Theoretical foundations and practical applications are explored, with analysis conducted using the Python programming language. 

Full module description and learning outcomes on GitHub (Sampling).

This module focuses on creating effective data visualizations in Python, covering general design principles, accessibility, and equity considerations. Participants learn to create and customize data visualizations from start to finish, using real-world case studies and examples. 

Full module description and learning outcomes on GitHub (Visualization) 

  • Creating and customizing data visualizations in Python. 
  • Applying general design principles to create accessible and equitable data visualizations. 
  • Using data visualization to effectively communicate insights and tell a story. 
  • Implementing and evaluating sampling procedures. 
  • Assessing survey quality and identifying sources of error. 
This module provides essential knowledge of algorithms and data structures, crucial for implementing data science and machine learning methods. It covers Big-O notation, time and space complexity, array-based data structures, searching, sorting, recursion, and optimization problem-solving. Participants will learn to justify their choices and improve code performance. Full course description and learning outcomes on GitHub (Algorithms and Data Structures).

This module offers a comprehensive understanding of deep learning, focusing on neural networks, backpropagation, and advanced architectures for image processing, NLP, and more. Participants will implement, test, and validate deep learning models using Keras and TensorFlow, and explore ethical implications of these models.

Full module description and learning outcomes on GitHub (Deep Learning) 

  • Assessing and selecting fundamental algorithms and data structures using Big-O notation.
  • Implementing recursive functions and solving optimization problems.
  • Developing, implementing, and validating deep learning models for various applications.
  • Utilizing advanced deep learning techniques like CNNs, RNNs, sequence-to-sequence models, and attention mechanisms.
  • Using Keras and TensorFlow for reproducible research.
  • Communicating the ethical implications of deep learning models.
Throughout the learning journey, both certificates integrate job readiness sessions, personalized one-on-one career support, access to a curated list of employment opportunities, and engaging networking events—both virtual and in-person. These vital components empower participants in their pursuit of sought-after roles in the fields of data science and machine learning. By fostering a holistic learning experience, we not only equip individuals with technical expertise but also the professional skills and connections essential for a successful transition into new, or advanced, roles in the evolving landscape of data science and machine learning. Participants will:
  • Elevate their communication and presentation skills
  • Craft a sector-specific resume and prepare for technical assessments
  • Enhance their networking skills and presence on social media platforms
  • Develop skills and strategies for behavioral and technical interviews

The DSI unifies data sciences across the University of Toronto and external partners. We are committed to supporting a future where people and industry thrive. We are working with Palette Skills to make Canada’s skills ecosystem more responsive to industry needs. We know the demand for a skilled, diverse, and engaged workforce is there and we are looking for experienced professionals willing to upskill to help meet that demand. 

We play a vital role within the Upskill Canada ecosystem and are dedicated to creating new career paths for untapped talent to unleash their full potential. We are committed to diversifying the data sciences sector. We encourage those traditionally underrepresented in this sector – women, newcomers, individuals with disabilities – to engage in our upskilling so that we may change the face of the data science and machine learning fields. Additionally, we support fast-growing companies looking to access the talent they need to grow. Through the Certificates, we provide mid-career talent with technical and job readiness skills to make an impact quickly in their next role.  

The certificates are offered in a cohort model. You can take either 16-week certificate.  A typical week involves programming Monday – Thursday, 6 PM – 8:30 PM, and optional programming Friday afternoons and Saturday mornings.

Applications are now closed and will open for cohort 4 on June 3, 2024. Get notified when applications open!

Cohort 3: April 22, 2024 – August 11, 2024
Cohort 4: August 19, 2024 – December 8th, 2024 
Cohort 5: November 18, 2024– March 16, 2025

Course Instructors and Course Support Staff –  Interested in joining our technical skills course team

Please reach out to courses.dsi@utoronto.ca