Python for Data Engineering Fundamentals Part II

Dive into the world of Python data engineering with this continuing fundamentals course. This course will build on your existing knowledge of data engineering. Once you’ve completed this course, you’ll have mastered the basics. From there, you can implement what you learned in the real world or continue on to additional intermediate or advanced courses.

This course focuses on the following:

  • Refining fundamental Python knowledge for data engineering
  • Introducing basic Python concepts like dictionaries, functions, and default arguments
  • Building proficiency in the use of Jupyter Notebook
  • Practical application of your newly acquired data skills in a data project for your portfolio

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What You’ll Learn

In this second course on Python for Data Engineering Fundamentals, we’ll build upon the knowledge and skills you developed during part I.

First, we’ll introduce you to the concept of dictionaries; you’ll learn how to create them, update them, and use them to build frequency tables.

Next, we’ll cover the indispensable role of functions, and you’ll learn how to use them to optimize your coding abilities. We’ll even teach you how to create your own functions and implement debugging techniques to fix them if they break.

Another crucial part of this course is the introduction to Jupyter Notebook, a powerful Python tool that makes it easy to share your coding ideas and present your work.

At the end of the course, you will combine all the skills you learned and put them into practice with a project that mirrors real-world business scenarios. The project will have you perform data analysis for Android and iOS app developers.

Enroll in this skill path if you want to learn how to do the following:

  • Create dictionaries and update dictionary values
  • Count with dictionaries and build frequency tables
  • Learn what functions do
  • Create your own functions and combine functions
  • Discover how to debug functions
  • Learn how to use default arguments
  • Use multiple return statements and variables
  • Explore how mutable and immutable data types behave
  • Install Jupyter Notebook and leverage its powerful data visualization features
  • Combine your skills to complete a data project from start to finish
money in envelope

Data engineers command an average salary of $127k, according to Indeed.

bar graph going up

Python won the TIOBE’s programming language of the year award four years in a row.

LinkedIn estimates 33% year-over-year job growth for data engineer positions.

How Our Python for Data Engineering Fundamentals Course Works

Even though data science is similar to data engineering, the differences are significant enough to require an entirely new course. From basic data processing to PostgreSQL database management to building data pipelines — we’ve specifically designed this course to help you develop the skills you’ll need as a data engineer.

This course contains the final lessons of our Python fundamentals for data engineering series. Before jumping into this course, be sure to complete part one. Or, if you’re confident in your knowledge of variables, working with strings, lists, for loops, and conditional statements, go ahead and give this course a shot! By completing these lessons, you’ll prepare yourself for intermediate and advanced Python courses.

At Dataquest, we have a distinct teaching method focused on providing you with the knowledge and skills that will make you job-ready as quickly as possible. That’s why we don’t use training videos and fill-in-the-blank coding problems. We believe that the best way to develop valuable and in-demand data skills is through hands-on learning. With us, you’ll use real code to solve real problems from day one.

Another unique aspect of Dataquest is the opportunity it creates to add relevant, compelling projects to your portfolio. Our guided project lets you apply and reinforce your data engineering skills by using real code to solve real-world problems. A strong portfolio is one of the things hiring managers look for in qualified candidates!

Additionally, with Dataquest, you’ll become a part of a vibrant and supportive community of students and professionals. You’ll never be alone, and if you ever need a hand from our powerful support tools, we’re only a click away.

Here’s a glance at part two of our Python for Data Engineering Fundamentals course:

Enroll in this course to learn Python for Data Engineering Fundamentals!

Python for Data Engineering Fundamentals Part II Lessons List


Learn to build frequency tables using dictionaries.

Functions: Fundamentals

Learn how to speed up your workflow using functions.

Functions: Intermediate

Learn more about using functions to write better code while avoiding common pitfalls.

Learn and Install Jupyter Notebook

Learn the basics of using Jupyter Notebook.

Profitable App Profiles for the App Store and Google Play Markets

Learn to combine the skills you learned in this course to perform data analysis.

Who is the Python for Data Engineering Fundamentals Course for?

Whether you’re brand new to the data engineering field or an experienced data engineer brushing up on the fundamentals, we designed this Python course with you in mind.

There are many types of people who could benefit from this course:

Students Who Enrolled in This Course Also Enrolled in The Following:

If you’re pursuing a career in data science as a data engineer, we suggest you enroll in the following paths. They’ll take you from beginner to job-ready in less than a year!

On the other hand, if you simply want to reinforce your existing data engineering skills or enhance work performance at your current job, we suggest you consider the following skill development courses:

Qualify for In-demand Jobs in Data Engineering With Python

Proficiency with Python is a valuable, rewarding, and in-demand skill across a variety of industries. The skills you’ll learn in this path can help you start your journey to any of the following careers: