Build the data foundation
Prepare data and use statistics to describe patterns and limitations.
Build data science and machine learning foundations, then create agents that use tools and context. Learn to measure the models and workflows you build.
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Develop a foundation in data science and machine learning, then explore how AI agents use tools and context to handle multi-step tasks. Learn to evaluate their output and limitations.
Prepare data and use statistics to describe patterns and limitations.
Work with supervised learning, validation and meaningful evaluation metrics.
Build tool-using workflows and assess their grounding, reliability, cost and latency.
Build both the model and the workflow around it. Learn machine learning foundations, LLM APIs, retrieval and tool calling, then test the quality and limitations of agent outputs.
Learn Python and statistics, build machine learning models and extend your work into agentic applications.
Complete topics, assignments and learning outcomes.
Put it into practice Analyse and transform a real-world dataset.
Prepare data for analysis and modelling.
Put it into practice Write an exploratory report with visual evidence.
Describe patterns and limitations in a dataset.
Put it into practice Train and evaluate baseline and candidate models.
Measure model performance with appropriate evaluation.
Put it into practice Build an agent that answers questions over a dataset using tools.
Design agent workflows that use tools and context.
Put it into practice Ship and evaluate an end-to-end data science agent.
Test agent outputs for quality and reliability.
Explore project ideas that bring the Data Science with Agentic AI curriculum together. Build, test and explain the decisions behind your work.
Ask a question in plain language, let the agent plan and run the analysis with tools, and check every step it took.
Run an agent against a set of test cases and score its answers for accuracy, grounding and reliability.
Rank useful suggestions from user and item data, and compare how different models perform.
Each curriculum module includes practical work to help you apply and review the idea.
Choose NSP or Noida, then find the weekday or weekend schedule that fits you.
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Talk to an advisorSchedules and fees shown are previews. Our team will confirm availability, mentor and final fee before enrolment.
Meet Coding Blocks learners whose practice and persistence led to opportunities at leading companies.
Need help with your starting point or schedule?
Talk to an advisorThe roadmap begins with Python and data foundations. Basic mathematical comfort and a willingness to code will help as you move into statistics, machine learning and agent workflows.
The roadmap covers python & data foundations, statistics & exploratory analysis, machine learning, llms & agent foundations, agent evaluation & capstone. Project ideas include Data analysis agent, Customer churn lab. Ask your advisor to confirm the work and review process for your selected batch.
Build both the model and the workflow around it. Learn machine learning foundations, LLM APIs, retrieval and tool calling, then test the quality and limitations of agent outputs.
Use the NSP and Noida selector to compare any published weekday and weekend schedules. If a centre has no listed batch, our team can help you check upcoming availability. Ask about live online options if you prefer to learn remotely.
These depend on your selected batch. Review the available schedule and fee together, and ask our team to confirm the current duration, mentor allocation and enrolment details.
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Start with a clear roadmap and a batch that fits your routine.
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