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From analyst to analytics architect: analytics engineering, big data pipelines, causal inference, advanced forecasting, and production Generative AI analytics.
8 modules · 48 lessons
Analytics engineering and data architecture, big data performance and automated pipelines, data quality governance and trust, causal inference and decision science, advanced forecasting and optimization, advanced Generative AI for analytics, LLMOps and cost engineering, and a capstone production analytics platform.
This is the final stage of the Ucanly Data Analytics with Python track, where analysts become analytics architects. Built for practitioners with hands on experience delivering analysis, dashboards, and experiments on real business data, this program goes deep into analytics engineering with dbt and dimensional modelling, big data processing with Polars, DuckDB, and PySpark, and rigorous data quality and governance with Great Expectations. You will move from reporting into decision science with causal inference, quasi experiments, and uplift modelling, and build advanced forecasting systems with hierarchical and gradient boosted time series models. On the Generative AI side, you will build production RAG over enterprise data, governed semantic layers for text to SQL, multi agent analytics with LangGraph, and full LLMOps covering evaluation, guardrails, and cost optimization. You will graduate having architected and launched a scalable, production grade analytics platform with automated pipelines, a governed semantic layer, an executive decision system, and an AI analytics agent, deployed with full CI/CD.
Complete this course to earn a verified Ucanly certificate you can add to your profile, share on LinkedIn, and showcase to employers as proof of the skills you've built.
It is recommended, but hands on experience delivering analysis, dashboards, and experiments on real business data is sufficient to start.
Yes, you will process data at scale with Polars, DuckDB, and PySpark, and orchestrate pipelines with Airflow.
DAGs and confounding, quasi experiments like difference in differences and matching, uplift modelling and treatment effects, and multi armed bandit based experimentation.
A scalable, production grade analytics platform with automated pipelines, a governed semantic layer, an executive decision system, and an AI analytics agent, deployed with full CI/CD and mentored end to end by Ucanly.