OpenCourser brand icon
Sign in
Sorry, this page is no longer available
We may earn an affiliate commission when you visit our partners.
Course image
Course image
edX logo

Python and Pandas for Data Engineering

4.5 Filled star Filled star Filled star Filled star Half star
Based on 30 ratings
see reviews
Kennedy Behrman and Noah Gift

In this course, you'll gain the Python and Pandas skills essential for data engineering:

  • Set up version-controlled Python environments with necessary libraries
  • Write Python programs using key language features and data structures
  • Manipulate and analyze data using the powerful Pandas library
  • Explore alternative data structures like NumPy arrays and PySpark DataFrames
  • Utilize Vim, Visual Studio Code, and Git for productive development
Read more

In this course, you'll gain the Python and Pandas skills essential for data engineering:

  • Set up version-controlled Python environments with necessary libraries
  • Write Python programs using key language features and data structures
  • Manipulate and analyze data using the powerful Pandas library
  • Explore alternative data structures like NumPy arrays and PySpark DataFrames
  • Utilize Vim, Visual Studio Code, and Git for productive development

Whether you're a beginner or have some programming experience, you'll learn to harness Python and Pandas to tackle data engineering challenges. Hands-on exercises reinforce your learning each step of the way.

Enroll now

What's inside

Learning objectives

  • Python environment setup and package management
  • Core python syntax and data structures
  • Pandas dataframes for data manipulation
  • Alternatives to pandas for big data
  • Development with vim, vs code, and git

Syllabus

Module 1: Getting Started with Python (14 hours)
\- Overview of Python, Bash and SQL Essentials for Data Engineering (video, 7 minutes)
\- Meet your Course Instructor: Kennedy Behrman (video, 0 minutes)
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Explores Python and Pandas for Data Engineering, which are standard tools for data engineers
Beginners may need to take other courses first, as this has few explicit prerequisites
Students build a strong foundation in Python syntax, which is helpful in other areas of tech
Learners get hands-on experience with real-world data engineering tools like Pandas
This course is a great choice for learners who want to enter the field of data engineering

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
Save

Reviews summary

Practical python & pandas for data engineering

According to learners, this course provides an exceptional foundation in Python and Pandas for data engineering, praised for its clear explanations and practical, hands-on labs. Students particularly value the often-overlooked inclusion of development environment setup with tools like VS Code and Git, which prepares them for real-world scenarios. While the course delivers a strong core in data manipulation, some learners note that the coverage of big data alternatives like PySpark and Dask is more of a brief introduction than an in-depth exploration. There's a mixed opinion on its suitability for absolute beginners; some find the pacing too fast, suggesting it might be better for those with some prior programming experience.
Valuable sections on setting up Vim, VS Code, and Git.
"The section on Git and VS Code was a bonus, truly helping me streamline my workflow."
"The inclusion of Vim, VS Code, and Git setup was brilliant – often overlooked in other courses."
"I also liked the focus on setting up proper development environments."
"The coverage of development tools like VS Code and Git is a huge advantage for real-world scenarios."
Features practical exercises that reinforce learning effectively.
"I particularly appreciated the hands-on labs for NumPy and PySpark, which provided practical experience."
"The practical examples and ungraded labs truly reinforced the learning for me."
"The hands-on exercises were key to understanding the material."
"The labs were well-designed and genuinely helped me grasp the concepts. I've already applied many techniques."
Provides a clear, practical foundation in Python and Pandas.
"The instructor's explanations of Pandas are incredibly clear, breaking down complex topics into digestible parts."
"I came in with some Python experience but needed to solidify my Pandas skills for my data science role, and this course delivered."
"The systematic approach to Python fundamentals, followed by deep dives into Pandas for data manipulation, was exactly what I needed."
"The clarity of explanations for Python and Pandas is unmatched. I truly benefited from the practical application of concepts."
Inconsistent clarity or polish in some explanations and labs.
"I found some of the video quality inconsistent, and a few explanations felt rushed, particularly on advanced concepts."
"The instructor sometimes skips over crucial details, assuming prior knowledge."
"Decent course, but inconsistent. Some modules are very clear and helpful, while others feel less polished or assume too much."
Offers a brief introduction to PySpark and Dask, not a deep dive.
"I felt the coverage of PySpark and Dask was a bit too brief. While it introduced the concepts, it didn't delve deep enough."
"My main feedback would be that the PySpark/Dask sections could be expanded. It feels like an overview rather than deep dive."
"The 'alternatives' part is really just an introduction, not enough to be truly useful for large-scale data engineering."
"I agree with some others that the big data tools are more of a quick introduction than a thorough exploration."
May be too fast for absolute beginners, assuming prior knowledge.
"For an absolute beginner, it might be too fast-paced. I had to look up external resources to fully grasp certain topics."
"I found this course quite difficult to follow as a beginner. The pace was too fast, and explanations were often high-level."
"Not recommended for true beginners despite the description."
"While it claims to be for beginners, I think it moves quite fast and might be challenging for someone without any programming background."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Python and Pandas for Data Engineering with these activities:
Organize and Review Course Materials
Ensure a strong foundation for success in the course by organizing and reviewing all available materials.
Show steps
  • Gather all course materials, including syllabus, readings, assignments, and quizzes
  • Create a system for organizing these materials, such as folders or digital notebooks
  • Regularly review the materials to identify areas for further study or clarification
Review Core Python Syntax and Data Structures
Strengthen the foundation by reviewing the basics of Python syntax and data structures before starting the course.
Browse courses on Python
Show steps
  • Go through online resources or textbooks to refresh your understanding of variables, data types, operators, and control flow
  • Practice writing simple Python programs to reinforce your knowledge
Solve LeetCode Problems on Python Lists and Dictionaries
Enhance problem-solving skills and reinforce concepts of Python lists and dictionaries by practicing on LeetCode, a platform for coding challenges.
Browse courses on Python
Show steps
  • Choose a set of LeetCode problems that focus on Python lists and dictionaries
  • Solve the problems and review the solutions to understand different approaches and techniques
  • Identify patterns and common operations related to lists and dictionaries
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review 'Hands-On Data Analysis with Pandas' by Wes McKinney
Build a strong understanding of the fundamentals of data analysis with Pandas, the essential library for data manipulation and analysis in Python.
View Python for Data Analysis: Data Wrangling with... on Amazon
Show steps
  • Read the first three chapters of the book to gain an overview of Pandas and its capabilities
  • Complete the exercises at the end of each chapter to reinforce your understanding
  • Create a small data analysis project using Pandas to apply your skills
Follow a Tutorial on PySpark DataFrames
Gain familiarity with PySpark DataFrames, a powerful tool for handling large-scale data, by following an online tutorial.
Browse courses on Pyspark
Show steps
  • Find a comprehensive tutorial on PySpark DataFrames
  • Follow the steps in the tutorial to create, manipulate, and analyze a DataFrame
  • Experiment with the DataFrame API to explore different operations and functions
Participate in a Study Group for Python Development Environments
Engage with peers, exchange knowledge, and solidify understanding of Python development environments by joining a study group.
Browse courses on Python
Show steps
  • Find or create a study group with other participants taking the course
  • Meet regularly to discuss course topics, share resources, and practice using Python development environments
  • Collaborate on projects or assignments to gain hands-on experience
Build a Data Preprocessing Pipeline
Develop hands-on experience with data preprocessing, a crucial step in data engineering to prepare data for analysis and modeling.
Browse courses on Data Preprocessing
Show steps
  • Identify a dataset to work with, such as a CSV file or a database table
  • Load the dataset into a Pandas DataFrame
  • Apply data cleaning techniques to handle missing values and outliers
  • Transform the data into the desired format for analysis or modeling
  • Save the preprocessed data to a file or database
Create a Tutorial on Using NumPy Arrays
Demonstrate a deep understanding of NumPy arrays, a fundamental data structure for numerical operations in Python, by creating a tutorial for others.
Browse courses on NumPy
Show steps
  • Explain the basics of NumPy arrays, including their creation, indexing, and slicing
  • Show how to perform common operations on arrays, such as addition, subtraction, and multiplication
  • Discuss the advantages and disadvantages of using NumPy arrays compared to other data structures

Career center

Learners who complete Python and Pandas for Data Engineering will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Python and Pandas for Data Engineering.
Object Detection Using Facebook's Detectron2
Most relevant
Functions, Methods, and Interfaces in Go
Most relevant
Supply Chain Analytics Essentials
Most relevant
Python Basics: Interacting with the Internet
Most relevant
Plant Bioinformatics
Most relevant
Fundamentals of Machine Learning in Finance
Most relevant
Python Classes and Inheritance
Most relevant
Business Analytics for Decision Making
Most relevant
Quality Improvement in Healthcare Organizations
Most relevant

edX Buyer Protection & Guarantee

Verified partner policies & purchase protections

World-Class University Partner
Free Trial & Audit

Free Audit Track Available

Free audit track available for most courses, giving open access to video lectures, course readings, and assignments at no charge.

Return & Refund Policy

14-Day Full Refund Window

14-day full refund available up to 14 days after course start or 14 days after payment (whichever comes second, up to 6 months max), provided verified certificate has not yet been earned.

Buyer Protections

Verified Academic & Industry Partner

University-backed credentials authored by elite institutions like Harvard, MIT, and Oxford, with tamper-proof certificate verification.

Key Purchase Protections
Free audit track for open learning access
14-day money-back guarantee on verified certificates
Prestigious university credentials (Harvard, MIT)
Automatic unenrollment refund process via dashboard
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Rating
4.5 Filled star Filled star Filled star Filled star Half star
Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Kennedy Behrman
Noah Gift
Language
English
Transcript
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Explores Python and Pandas for Data Engineering, which are standard tools for data engineers
Beginners may need to take other courses first, as this has few explicit prerequisites
Students build a strong foundation in Python syntax, which is helpful in other areas of tech
Learners get hands-on experience with real-world data engineering tools like Pandas
This course is a great choice for learners who want to enter the field of data engineering
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Python and Pandas for Data Engineering.
Enroll now
Save for later
Add this course to your list. Find it anytime.
Save
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser