We may earn an affiliate commission when you visit our partners.

Python and Pandas for Data Engineering

Kennedy Behrman and Noah Gift

4.5

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

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

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)

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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

Show steps

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've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Python and Pandas for Data Engineering.

Fluent Python

Save

Serves as the ultimate masterclass in idiomatic Python, guiding developers through data models, generators, metaprogramming, and concurrent concurrency patterns.

Python and Pandas for Data Engineering

What's inside

Learning objectives

Syllabus

Traffic lights

Save this course

Reviews summary

Practical python & pandas for data engineering

Activities

Career center

Reading list

Share

Similar courses

edX Buyer Protection & Guarantee

Free Audit Track Available

14-Day Full Refund Window

Verified Academic & Industry Partner