Applied AI in
Data Analytics

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Starts on

October 2026

Duration

10 Weeks, Online

4–6 hours/week

Program Fee

CA$2,400

Eligibility

Technical Background

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Starts on

October 2026

Program Fee

CA$2,400

Duration

10 Weeks, Online

4–6 hours/week

Eligibility

2+ years of Experience in a technical role

Data Analytics Beyond Traditional Tools

Organizations are overwhelmed by massive volumes of structured and unstructured data—text, images, audio, and more—that traditional analytics tools are struggling to keep up with. The challenge is knowing when, where, and how to leverage AI to quickly turn raw data into actionable insights.

For analysts, researchers, and data professionals, this gap isn't about technical expertise alone—it's about adapting your analytical thinking and reasoning to seamlessly integrate AI into your workflows, without getting lost in complexity or coding-intensive tasks.

Through practical, hands-on learning with accessible no-code and low-code AI tools, the Applied AI in Data Analytics program equips you to transform your data analysis workflows, automate tedious data processing tasks, and position yourself as a leader in data-driven decision-making.

of the world's data has been created in just the past two years, underscoring AN explosive Rate of growth .

SOURCE: G2

is the projected Market value of Data analytics by 2032, exhibiting a CAGR of 25.5% from 2024.

SOURCE: FORTUNE

Your transformative journey in this program

Identify Practical AI Applications for Data Analytics

Learn when, where, and how to apply AI tools to solve real challenges across data analysis, preparation, and modeling workflows.

Simplify AI Adoption with No-Code Solutions

Build confidence using accessible AI tools to streamline data preparation, modeling, and insight communication—without heavy coding or technical barriers.

Enhance Data-Driven Reasoning

Strengthen your ability to translate AI-generated insights into clear, actionable recommendations for stakeholders to support better business outcomes.

Program highlights

Action-Oriented Curriculum

Focus on applying AI to your work without delving into complex technical theories, ensuring immediate impact.

Integrated AI Application Framework

Utilize a user-centered approach to AI-driven problem-solving that can be applied to any role or industry.

Unparalleled Faculty Expertise

Learn from renowned faculty, bringing extensive experience in AI applications and Design Thinking methodologies.

Flexible Format With Live Sessions

Gain deep insights into emerging AI topics through weekly live sessions and participate in activities in a flexible format.

Immediate Professional Impact

Implement new strategies and tools in your work as you learn, leading to enhanced performance and recognition.

Collaborative Peer Learning

Participate in interactive discussions and group activities that foster knowledge sharing in AI-driven decision making.

Meet the Faculty

Dr. Fred
Popowich

Professor, Computing Science,
Artificial IntelligenceSFU School of Computing Science

Dr. Steven
Bergner

Term Lecturer, Data Acquisition
and Machine LearningSFU School of Computing Science

Program Curriculum

Module 1: Foundations of AI for Data Analytics

• Navigate the Course Structure and Expectations: Understand the program’s flow, learning objectives, and lab components.
• Compare Traditional vs. AI-Driven Analytics: Examine key differences, benefits, and challenges of AI-powered data analysis.
• Learn a Modern Data Science Framework: Structure AI-driven projects using an industry-standard data science workflow framework.
• Set Up Essential Tools for AI Labs: Install and configure the software and platforms required for hands-on exercises.
• Master Core Programming Concepts for AI Workflows: Develop foundational coding skills crucial for AI-driven analytics.

Module 2: Exploring Data with AI

• Develop Intuition for Data Exploration: Formulate meaningful questions to guide analysis and extract actionable insights.
• Compare Traditional vs. AI-Driven Exploratory Data Analysis (EDA): Examine conventional EDA techniques and explore how AI improves pattern detection, anomaly identification, and data structuring.
• Leverage AI for Insightful EDA: Identify missing values, uncover hidden patterns, and detect anomalies using AI-powered tools.
• Conduct Hands-On AI-Assisted Data Exploration: Use Low-Code/No-Code and Natural Language Query (NLQ) tools to interact with real-world datasets efficiently.

Module 3: Preparing & Transforming Data with AI

• Use AI to Automate Data Preparation: Examine why data preparation is essential and how AI can assist with cleaning, integration, and transformation.
• Clean Data with AI-Powered Tools: Handle missing values, inconsistencies, and duplicates using AI-assisted methods.
• Integrate and Engineer Features with AI: Match schemas, link records, and generate new features with AI support.
• Transform and Encode Data for Modeling: Apply AI-assisted scaling, normalization, and feature encoding. Use dimensionality reduction techniques like PCA to manage complexity.
• Practice AI-Assisted Data Preparation: Use Low-Code/No-Code tools to clean and transform real-world datasets, and explore trade-offs in an AI-powered simulation.

Module 4: Analyzing Data with AI

• Use AI to Support Data Analysis: Move beyond exploratory analysis to uncover deeper insights with AI tools and consider how findings translate to real-world applications.
• Identify Trends and Relationships in Data: Apply AI to detect patterns in structured and unstructured datasets, perform correlation analysis, and assess feature importance.
• Detect and Interpret Outliers: Use AI to flag unusual data points and evaluate their impact on analysis, modeling, and decision-making, with examples like fraud detection.
• Apply AI Tools for Data Analysis: Use Low-Code/No-Code platforms to extract meaningful insights and support data-driven decisions.

Module 5: Building AI Models Using No-Code/Low-Code Tools

• Move from Data Analysis to Machine Learning: Explore how AI models identify patterns in data and adapt beyond fixed rules.
• Select the Right Model for the Task: Compare different model types and architectures using an AI-powered simulation to practice decision-making.
• Make Critical Choices in Model Development: Determine training-validation splits, choose appropriate evaluation metrics, and decide when to stop training.
• Build and Train Models with AI Tools: Use Low-Code/No-Code platforms to develop and evaluate machine learning models on real-world datasets.

Module 6: Deploying AI Models Using No-Code/Low-Code Tools

• Understand AI Model Deployment: Explore what it means to deploy a model and compare deployment patterns like batch processing and real-time inference.
• Prepare a Model for Deployment: Transition a trained model to production, considering performance, security, and deployment constraints.
• Deploy a Model Using No-Code/Low-Code Tools: Gain hands-on experience deploying a model as an API and integrating it with scripts, Google Sheets, or web apps.
• Manage AI Model Updates and Iterative Deployment: Develop a strategy for monitoring, improving, and updating models as data and business needs evolve.

Module 7: Tuning Models with AI

• Automate Model Tuning with AI: Use AI tools to optimize hyperparameters and track tuning experiments systematically.
• Select and Refine Features for Better Models: Evaluate feature importance and weigh the trade-offs of removing variables to improve performance.
• Measure Model Performance Effectively: Move beyond basic metrics using advanced evaluation methods like Precision-Recall Curves and leverage AI tools to analyze errors and biases.
• Monitor and Refresh Deployed Models: Detect performance degradation and determine when a model requires retraining or updates.

Module 8: Advanced AI Techniques

• Identify When to Use Advanced Model Architectures: Recognize the limitations of traditional models and explore architectures like CNNs, Transformers, GANs, LSTMs, and Diffusion models, matching them to real-world use cases.
• Leverage Pretrained Models and Transfer Learning: Improve efficiency by adopting and fine-tuning existing models; learn how to find and select the right pretrained models for specific tasks.
• Enhance Model Performance with Ensemble Methods: Combine multiple models to improve accuracy and robustness, using AI-driven techniques to automate model selection and integration.
• Balance Model Complexity and Practical Constraints: Weigh trade-offs in size, cost, compute power, environmental impact, and explainability to optimize models for real-world deployment.

Module 9: Communicating Insights with AI

• Translate AI Outputs into Actionable Insights: Convert model predictions and metrics into a clear narrative that explains key findings or supports decision-making.
• Choose the Right Communication Approach: Select effective methods—visualizations, reports, or narratives—based on the needs of technical and non-technical audiences.
• Practice AI-Driven Storytelling: Transform AI results into a compelling story, tailoring messaging and presentation for different stakeholders.

Module 10: AI in Practice & Next Steps

• Develop a Practical AI Project: Apply AI concepts through a flexible project, such as automating a task, drafting a project plan, evaluating deployment feasibility, analyzing data, or creating a training resource.
• Present AI Insights: Share project findings through concise, engaging presentations and participate in peer discussions.
• Review Key Takeaways and Next Steps: Reflect on course learnings, explore resources for continued AI adoption, and plan for future skill development.

Download the program brochure to access the full schedule, including the dates and times of the weekly online sessions.

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Inspiring Journeys

“The Applied AI in Data Analytics program gave me practical tools to apply AI to real-world data analysis, validate my work with confidence, and prepare for the evolving future of work.”

Leanne BeetAstroparticle Physics Facility Manager | TRIUMF

“The program is much more than you expect. The skills you gain let you move faster, do more sophisticated work, and become not one full-time engineer, but more like ten."

AI in HR Analytics From Insight to Action: Ashley's Journey

Ashley BenningtonManager, HR Analytics | Colliers International

Certificate of completion

Upon successfully completing the program, you'll receive a LinkedIn badge and a digital certificate from Simon Fraser University. As a program alum, you'll also gain exclusive access to various workshops, events, and networking opportunities. You may also qualify for tuition assistance if you choose to enroll in more online programs through DIAL. The certificate images shown are for illustrative purposes and may change at the discretion of Simon Fraser University.