“Organizations are swimming in data and increasingly turning to AI to make sense of it faster. A professional who knows how to responsibly direct AI tools can dramatically accelerate a team’s analytics capacity.” – Saber Talazadeh, Computing Flexible Learning Faculty
BCIT Computing Flexible Learning’s Applied Data Analytics Certificate (ADAC) has seen over 250 students graduate since 2018. With a 94% satisfaction rate, most grads are now working full time to meet the growing need for data professionals.
“Since its inception, ADAC has been continually updated to include the latest tools in data analytics, and best practices for the most current business intelligence approaches.” explains Kevin Cudihee, Program Head. This includes new courses, such as COMP 2453 Microsoft Fabric for Data Analytics. “I’ve also developed a laddered path through the Data Visualization with Microsoft Power BI Microcredential and the Applied Computer Information Systems (ACIS) Associate Certificate, so students can build their skills one course and one credential at a time.”
“Most recently I’ve worked with AWS industry expert and Flexible Learning instructor Saber Talazadeh to develop our newest course, COMP 3854 – AWS Generative AI for Data Analytics, which was built specifically for ADAC, and is also an elective in the Computer Systems Technology (CST FLEX) Diploma.”
We checked in with Saber in this Q&A to learn more about AWS Generative AI for Data Analytics, which will be offered online this fall.
Q: What can you tell us about COMP 3854, AWS Generative AI for Data Analytics?
Saber Talazadeh (ST): AWS Generative AI for Data Analytics is a hands-on, applied course that sits at the intersection of two of the most talked-about areas in tech right now: cloud-based data analytics and Generative AI (GenAI).
Students work directly with AWS AI services to do things that would have seemed ambitious even a couple of years ago. These include generating and refining SQL queries with AI assistants, designing data pipelines, building agentic workflows, and completing a full end-to-end analysis project from raw data to a polished final presentation. Everything is grounded in real tools and real-world data scenarios.
Q: Who will want to learn these data analysis skills?
ST: This course will resonate with students who are curious, hands-on, and excited about where data and AI are heading together.
It’s the third course in a progression – following Introduction to Python for Data Analysis (COMP 2853) and Data Analytics Fundamentals (COMP 2854) – so students come in with a solid foundation and are ready to level up.
The class size is small enough that there’s genuine back-and-forth, students aren’t just watching AI tools work: they’re experimenting, comparing platforms, and developing their own instincts for what makes a good AI-assisted workflow.
Q: How will organizations benefit from professionals who can use generative AI for data analytics?
ST: Organizations are swimming in data and increasingly turning to AI to make sense of it faster. A professional who knows how to responsibly direct AI tools, prompt effectively, validate outputs, build automated pipelines, and communicate insights clearly, can dramatically accelerate a team’s analytics capacity.
Crucially, this course also covers responsible AI and data privacy principles, so graduates understand not just what’s possible but what’s appropriate. That judgment is essential.
Learn more about COMP 3854: AWS Generative AI for Data Analytics
Q: Why did you want to work in data?
ST: I’ve always been drawn to the practical side of data, and how you can get from a messy dataset to a decision someone can act on. When Generative AI started maturing to the point where it could meaningfully assist in that process – through writing queries, suggesting schema designs, and flagging anomalies – it felt like the field had shifted in a fundamental way. I wanted to help students get ahead of that shift rather than catch up to it.
Q: What are some of the most exciting applications you see students moving toward?
ST: I’m particularly excited about agentic workflows – where AI doesn’t just answer a question but orchestrates a series of steps autonomously. In data analytics, that could mean an agent that monitors incoming data, flags irregularities, and generates a draft report, all without manual intervention.
Students who can design and govern those systems are going to be genuinely in demand across industries, from healthcare to finance to logistics.
Q: How do you keep up with a changing industry?
ST: Using GenAI platforms regularly means you notice how they’re evolving. Beyond that, I follow AWS releases closely, engage with practitioner communities, and try to bring real case studies into the course so that what we’re teaching reflects what’s actually happening in industry.
The course includes cross-industry case studies for exactly that reason.
Q: Any final insights on how to thrive in a data career?
ST: Data analytics is no longer just a technical skill, it’s a communication skill. The students who will stand out are the ones who can take an AI-generated insight, interrogate it critically, and explain it clearly to a non-technical stakeholder.
That combination of technical fluency and clear thinking is what this course tries to cultivate.
Feature photo: Saber Talazadeh