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From data scientist to data science architect: data engineering at scale, causal inference, MLOps, cloud data platforms, and production Generative AI.
8 modules · 48 lessons
Data engineering for data scientists, advanced modelling and deep learning, causal inference and decision science, MLOps and production machine learning, cloud and data platform architecture, advanced Generative AI for data teams, LLMOps and governance, and a capstone production data science platform.
This is the final stage of the Ucanly Data Science track, where data scientists become architects. Built for practitioners with hands on experience building, tuning, and deploying machine learning models on real business data, this program goes deep into big data engineering with PySpark and Airflow, deep learning for tabular and time series data, causal inference and decision science, and full MLOps discipline with feature stores, model monitoring, and drift detection. You will architect cloud data platforms on Snowflake and BigQuery, and move into advanced Generative AI for data teams: production RAG over enterprise data, text to SQL semantic layers, 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 data science platform with automated pipelines, a feature store, a monitored model, an AI insight layer, and a full CI/CD pipeline.
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 building, tuning, and deploying machine learning models on real business data is sufficient to start.
Yes, you will process data at scale with PySpark, orchestrate pipelines with Airflow, and build a tested analytics layer with dbt.
DAGs and confounding, uplift modelling and treatment effects, sequential experimentation and multi armed bandits, and optimization based decision modelling.
A scalable, production grade data science platform with automated pipelines, a feature store, a monitored model, an AI insight layer, and a full CI/CD pipeline, defended in a live architecture review with Ucanly mentors.