Data Engineer

Discover what you will learn on your journey to becoming a Data Engineer!

Learn the fundamentals of Python, and build your data engineering foundation.

Learn how to create, manage, and protect relational databases with SQL.

Pandas provides tools for working with tabular data, i.e. data that is organized into tables that have rows and columns. Tabular data has a lot of the same functionality as SQL or Excel, but Pandas adds the power of Python.After learning Pandas, you’ll be able to ingest, clean, and aggregate large quantities of data, and then use that data with other Python modules like Scipy (for statistical analysis) or Matplotlib (for visualization).

This course will cover how to create Pandas DataFrames, calculate aggregates, and merge multiple tables.

Keep building your SQL skills with advanced techniques and hands-on practice.

Clean, well-structured data is essential to data science but cleaning data requires both a keen eye and technical skills. Develop both here!

This course will introduce you to all of the tools you will need to get started off-platform as well as some helpful tricks for each technology. These are the most common technologies used by Data Professionals across sectors.

This course will teach you how to do data science projects on your own computer locally. In this course, you will learn about: * Using the Command Line * Installing and using Jupyter Notebooks * Setting up and using PostgreSQL

Put your data management skills to work in this portfolio project

This course is an introduction to the underlying concepts behind big data with a practical and hands-on approach with PySpark. Big data is everywhere, and touches data science, data engineering, and machine learning. It is becoming central to marketing, strategy, and research. This course covers the applications and implications of big data on finance, social media, health, and medicine. PySpark makes it easy to start analyzing big data, making the potential of big data accessible to anyone who knows Python.

In this course, you will learn how to handle big data with PySpark. In addition to learning how to manage the data, you will also be exposed to the conceptual underpinnings that make working with big data possible.

Dive deeper into Python 3, an essential skill for Data Engineers!

We use our mouse and fingers to click images of icons and access files, programs, and folders on our devices. However, this is just one way for us to communicate with computers.

The command line is a quick, powerful, text-based interface developers use to more effectively and efficiently communicate with computers to accomplish a wider set of tasks. Learning how to use it will allow you to discover all that your computer is capable of!

By the end of the course, you will be able to navigate, access, and modify files and folders on your computer—all without a mouse!

Build a data pipeline portfolio project.

Version control (what git gets you) is essential to working on teams, and managing the status of code, edits, and workflows. If you've ever named a file ""-final"", ""final-final"", etc. You need version control.

By the end of this unit, you will know everything you need manage files and contribute to collectively built code.

Take your git and Github skills to the next level by utilizing one of the most powerful tools: branching. By creating branches, code can evolve independently and later merge with other pieces of code. In addition, getting familiar with markdown will help you both communicate within the GitHub ecosystem and become more flexible with your writing.

You'll learn how to read and write markdown, and essential text formatting tool, leverage branching to work concurrently on code with your team, and some of the essential practices to facilitate teamwork.

Build your Data Engineering portfolio on GitHub

Learn more about NoSQL databases, MongoDB, its basic operations, and some of its more advanced features.