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Python for Data Analysis and Visualization
Go from raw, messy files to clear, defensible insights using Python, pandas, NumPy, and modern visualization libraries, and learn to present findings as a story.
Beginner16 hours
Data engineers, analysts and data scientists · Intermediate
Data Engineering and Data Science
Build the data foundations, pipelines and machine learning skills behind reliable analytics and AI.
What you can build toward
Grow useful skills, one step at a time.
A practical progression from analysis and modeling to pipelines, streaming and machine learning.
- Explore foundational concepts and real-world examples.
- Practice critical thinking, evaluation, and responsible use.
- Choose a next step based on your experience and goals.
Recommended courses
Explore the next building blocks.
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SQL, Data Modeling and Data Warehousing
Write advanced analytical SQL, design dimensional models, and build tested warehouse transformations with dbt on modern cloud and open-source warehouses.
Intermediate14 hours
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Data Pipelines and Orchestration
Build reliable batch ETL and ELT pipelines with Python, Airflow, and dbt, using incremental loads, idempotent design, testing, lineage, and lakehouse storage formats.
Intermediate16 hours
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Applied Machine Learning with Python
Build, validate, tune, and explain supervised and unsupervised models with scikit-learn, with emphasis on sound evaluation and responsible practice.
Intermediate18 hours
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Streaming and Big Data Engineering
Build event-driven systems and large-scale processing jobs with Apache Kafka and Apache Spark, covering delivery semantics, windowing, scaling, cost, and reliability.
Advanced18 hours
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Deep Learning and NLP Foundations
Build neural networks in PyTorch, apply CNNs and transformers, fine-tune models with Hugging Face, and evaluate and deploy them with care.
Advanced18 hours
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