Data Engineer

Learn how to build data pipelines to work with large data sets.

This path teach you how to utilize Python along with pandas to work with large data sets and loading them into a Postgres database. In this path, you'll learn how to work with big data, building data pipelines, and more.

Throughout this path, you will learn the following

  • How to work with production databases
  • Key computer science concepts like data structures, algorithms, and recursion.
  • How to handle larger data sets.

Learn by coding!

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Become a Data Engineer

Python Fundamentals

Learn about the fundamentals of Python programming in the context of data engineering.

Python Intermediate

Learn important tools for your Python data toolbox.

Programming Concepts with Python

Enhance your understanding of how Python works.

Algorithm Complexity

Learn how to assess and implement efficient algorithms with Python.

SQL Fundamentals

Learn the basics of working with SQL databases.

Intermediate SQL for Data Analysis

Learn to work with multi-table databases.

Postgres for Data Engineers

Learn about the SQL database Postgres.

Optimizing Postgres Databases

Learn how to optimize your Postgres databases.

NumPy for Data Engineers

Learn how NumPy can be used to optimize your data processing.

Processing Large Datasets in Pandas

Learn how to work with datasets by optimizing your pandas workflow, processing data in batches, and augmenting pandas with SQLite.

Optimizing Code Performance on Large Datasets

Learn how to process data more quickly by optimizing CPU and I/O performance. Learn to parallelize your code for better performance.

Algorithms & Data Structures

Learn about different data structures, and how they can help speed up your data analysis.

Recursion & Trees

Learn about recursion and how it applies to tree data structures, and how tree data structures are used to speed up processing of data analysis tasks.

Building a Data Pipeline

Learn how to build a Python data pipeline from scratch.