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AdvancedData Science8 weeks
Certificate IncludedCertificate EligibleUcanly certificate

Data Science: Advanced

From data scientist to data science architect: data engineering at scale, causal inference, MLOps, cloud data platforms, and production Generative AI.

Duration
8 weeks
Level
Advanced
Content
48 lessons
8 modules · 17 h
Certificate
Included
On completion
Access
Fee by email
Or take the free aptitude test

What you'll learn

Process big data at scale with PySpark and orchestrate reliable pipelines with Airflow
Build clean, tested data layers with dbt and handle streaming, real time data
Apply deep learning to tabular data, time series, and embeddings with PyTorch
Move from prediction to decision using causal inference, uplift modelling, and multi armed bandits
Run full MLOps: feature stores, experiment tracking with MLflow, and model monitoring with drift detection
Architect cloud data platforms on Snowflake and BigQuery with Infrastructure as Code and CI/CD
Build production RAG, text to SQL semantic layers, and multi agent analytics with LangGraph
Run LLMOps in production: evaluation, guardrails, cost optimization, and AI governance
Architect and launch a scalable, production grade data science platform with a feature store and full CI/CD

Who this course is for

  • Intermediate level graduates ready to architect production grade data platforms
  • Working data scientists preparing for senior data scientist or data science architect roles
  • Engineers who want deep, hands on experience with MLOps and causal decision science
  • Anyone targeting senior level data science system design interviews

Skills you'll gain

Advanced Data ScienceData Science ArchitectPySparkAirflowdbtSnowflakeBigQueryFeature StoreMLOpsMLflowModel MonitoringCausal InferenceUplift ModellingDeep LearningProduction RAGText to SQLLangGraphLLMOpsData GovernanceSenior Data Scientist

This course includes

Live mentor led Ucanly sessions plus self paced recorded content
Ucanly mock system design and case study interviews and senior level portfolio review
Resume and profile audit with placement guidance
Production style projects in every module
Lifetime access to course materials and future updates
Advanced Certificate of Completion from Ucanly

Course content

8 modules · 48 lessons · 17 h

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.

Requirements

  • Completion of the Intermediate level, or hands on experience building, tuning, and deploying machine learning models on real business data
  • Comfort with Python, SQL, and basic cloud or deployment concepts
  • A computer or cloud environment capable of running distributed data processing

About this course

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.

Earn a Ucanly certificate

Complete this course to earn a Ucanly certificate with its own page and QR code. Save it as a PDF, share its link, or add it to the certifications on your LinkedIn profile.

Certificate IncludedCertificate EligibleUcanly certificate

Frequently asked questions

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.