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IBM: Mastering Generative AI for Data Science

Intermediate 12 hours English Course CertificateFREE
SkillsGenerative AIData scienceData preparationDataset augmentationAI toolsProductivity

Is this course right for you?

Our take
An IBM edX course, again from Rav Ahuja, on putting generative AI to work across the data-science lifecycle.

Good for: Using generative AI across the data-science workflow.

Skip if: You are new to data science or want general GenAI concepts.

Here the use cases are data-science ones: querying and preparing data, generating and augmenting datasets when real data is scarce, and applying generative AI to the analysis itself. The dataset-augmentation angle is genuinely useful and underappreciated — synthesising plausible data to fill gaps is one of the more practical things genAI offers a data scientist. It's aimed at speeding up an existing workflow, not teaching data science.

So it assumes you already do data science, and it's the wrong pick if you're new to it or want general genAI concepts. Audit it free on edX (verified certificate a paid option, financial assistance available); note some third-party tools it shows carry their own subscriptions or usage costs. AI tools change fast, so some may have moved on since filming (as of 2026).

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About this course

Rav Ahuja teaches how to use generative AI tools (GPT-3.5, ChatCSV, tomat.ai, and others) throughout the data science lifecycle: querying and preparing data, generating and augmenting datasets, and applying generative AI techniques to develop and refine machine learning models.

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Instructor

RA
Rav Ahuja
edX instructor

Rav Ahuja is IBM's Global Program Director for the IBM Skills Network, leading curriculum strategy for AI, data science, and cloud courses on edX.

Frequently asked questions

Data scientists and analysts who want to apply generative AI to their work — using large language models and AI tools to accelerate data-science tasks like exploration, coding, and communication. It assumes a data-science context, so it suits practitioners rather than complete beginners. If you already work with data and want to fold generative AI into your workflow, it is aimed at you.

How to use generative AI within data science: applying large language models and AI assistants to tasks such as writing and debugging code, exploring and explaining data, generating insights, and speeding up the data-science workflow, along with the considerations of using these tools responsibly. It is about augmenting data-science practice with generative AI rather than building models from scratch.

Possibly. If the course has you use commercial AI tools or large-language-model APIs, those can carry usage costs beyond any course subscription, though many tasks can be done on free tiers. Budget a little for API or tool access if you want to follow every hands-on part fully, and check what the course specifically relies on before assuming everything is free.

Check the recent-update date, since generative AI moves very fast. The workflow concepts — how to use AI to assist data-science tasks — stay useful, but specific tools and their capabilities change within months. Treat it as a current snapshot of applying generative AI to data science, and keep up with developments separately, as no course can fully track this rapidly evolving area.

You can audit it on Coursera to watch the material at no cost, with graded elements and the certificate behind a subscription. Since much of the value is in trying the generative-AI techniques yourself, the paid track (and possibly tool access) is worth considering if you want hands-on practice rather than only the lectures.
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