Applied Python Data Engineering

Matt Harrison, Noah Gift, and Kennedy Behrman

Learn how to use data engineering to leverage big data for business strategy, data analysis, or machine learning and AI. By completing this course series, you'll empower yourself with the knowledge and proficiency required to build efficient data pipelines, manage cutting-edge platforms like Hadoop, Spark, Snowflake, Databricks, and Kubernetes, and tell stories with data through visualization. You will delve into foundational big data concepts, distributed computing with Spark, Snowflake’s architecture, Databricks’ machine learning capabilities, Python techniques for data visualization, and critical methodologies like DataOps.

Learn how to use data engineering to leverage big data for business strategy, data analysis, or machine learning and AI. By completing this course series, you'll empower yourself with the knowledge and proficiency required to build efficient data pipelines, manage cutting-edge platforms like Hadoop, Spark, Snowflake, Databricks, and Kubernetes, and tell stories with data through visualization. You will delve into foundational big data concepts, distributed computing with Spark, Snowflake’s architecture, Databricks’ machine learning capabilities, Python techniques for data visualization, and critical methodologies like DataOps.

This course series is designed for software engineers, developers, researchers, and data scientists who want to strengthen their specialization in data science or machine learning, as well as for professionals who are interested in pursuing a career as a data-focused software engineer, data scientist, or a data engineer working in cloud, machine learning, business intelligence, or other field.

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What's inside

Two courses

Spark, Hadoop, and Snowflake for Data Engineering

(0 hours)

Gain skills for building efficient and scalable data pipelines. Explore Hadoop, Spark, and Snowflake platforms, learning how to optimize and manage them. Delve into Databricks for data analytics and machine learning, and hone Python data science skills with PySpark. Discover MLflow for managing the end-to-end machine learning lifecycle, and learn to integrate it with Databricks.

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Virtualization, Docker, and Kubernetes for Data Engineering

(0 hours)

Throughout this course, you'll explore virtualization, containerization, and Kubernetes, mastering the tools that power data engineering. Each week presents new tools and platforms indispensable in data engineering. From Docker and Kubernetes to advanced topics like AI-driven coding, efficient container image management, and SRE practices, you'll acquire the expertise needed to thrive in the dynamic and data-driven landscape of advanced data engineering.

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Learning objectives

Create scalable big data pipelines (hadoop, spark, snowflake, databricks) for efficient data handling.
Build machine learning workflows (pyspark, mlflow) on databricks for seamless model development and deployment.

Implement dataops/devops to streamline data engineering processes.
Formulate and communicate data-driven insights and narratives through impactful visualizations with python and data storytelling

Create scalable big data pipelines (hadoop, spark, snowflake, databricks) for efficient data handling.
Build machine learning workflows (pyspark, mlflow) on databricks for seamless model development and deployment.
Implement dataops/devops to streamline data engineering processes.
Formulate and communicate data-driven insights and narratives through impactful visualizations with python and data storytelling

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