Coursera – Applied Python Data Engineering Specialization

What you’ll learn
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

Skills you’ll gain
Data Science
Big Data
Python Programming
Information Engineering
Kubernetes
Data Visualization
Apache Hadoop
Docker Container
Apache Spark
Snowflake (Data Warehouse)

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.

Applied Learning Project

The Specialization features a capstone project focused on using Databricks’ API to replicate an existing project. This provides hands-on experience working with Databricks to build a portfolio-ready data solution. You will apply Python to a variety of data engineering tasks.