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
Become a Data Engineer
Learn about the fundamentals of Python programming in the context of data engineering.
Learn important tools for your Python data toolbox.
Programming Concepts with Python
Enhance your understanding of how Python works.
Learn how to assess and implement efficient algorithms with Python.
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.