EdX

Python and Pandas for Data Engineering (edX)

Offered by AI (Pragmatic AI Labs),
Python and Pandas for Data Engineering (edX)

Master Python essentials and Pandas for data engineering. Learn to set up development environments, manipulate data, and efficiently solve real-world problems.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

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.
This course is part of the Data Engineering Foundations Professional Certificate.

What you'll learn

  • 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)
\- Overview of Key Concepts (video, 5 minutes)
\- Introduction to Setting Up Your Python Environment (video, 0 minutes)
\- Installing Packages with pip in Python (video, 6 minutes)
\- Saving Requirements File in Python (video, 3 minutes)
\- Creating and Using a Python Virtual Environment (video, 5 minutes)
\- Expression Statements in Python (video, 3 minutes)
\- Assignment Statements in Python (video, 5 minutes)
\- Import Statements in Python (video, 4 minutes)
\- Other Simple Statements in Python (video, 5 minutes)
\- Compound Statements in Python (video, 5 minutes)
\- If Statements in Python (video, 6 minutes)
\- While Loops in Python (video, 4 minutes)
\- Functions in Python (video, 7 minutes)
\- Key Terms (reading, 10 minutes)
\- Key Terms (reading, 10 minutes)
\- Meet your Supporting Instructors: Alfredo Deza and Noah Gift (reading, 10 minutes)
\- Course Structure and Discussion Etiquette (reading, 10 minutes)
\- Getting Started and Best Practices (reading, 10 minutes)
\- Key Terms (reading, 10 minutes)
\- Lesson Reflection (reading, 10 minutes)
\- Key Terms (reading, 10 minutes)
\- Lesson Reflection (reading, 10 minutes)
\- Key Terms (reading, 10 minutes)
\- Evaluating to True or False (reading, 10 minutes)
\- Lesson Reflection (reading, 10 minutes)
\- Python Statements (quiz, 30 minutes)
\- Assignment Statements (quiz, 30 minutes)
\- Import Statements (quiz, 30 minutes)
\- If Statements (quiz, 30 minutes)
\- While Loops (quiz, 30 minutes)
\- Quiz-Setting Up Your Python Environment (assignment, 180 minutes)
\- Meet and Greet (optional) (discussion prompt, 10 minutes)
\- Install a Package with the pip Command (ungraded lab, 60 minutes)
\- Export a Requirements File (ungraded lab, 60 minutes)
\- Create a Virtual Environment (ungraded lab, 60 minutes)
\- Practicing with Expression Statements (ungraded lab, 60 minutes)
\- Decorator Functions (ungraded lab, 60 minutes)
\- Setting up a Python Environment (ungraded lab, 60 minutes)

Module 2: Essential Python (11 hours)

  • Introduction to Python Essentials (video, 0 minutes)
  • Sequences in Python (video, 8 minutes)
  • Lists and Tuples in Python (video, 5 minutes)
  • Strings in Python (video, 10 minutes)
  • Creating Range Objects in Python (video, 2 minutes)
  • Creating Dictionaries in Python (video, 4 minutes)
  • Accessing Dictionary Data in Python (video, 3 minutes)
  • Dictionary Views in Python (video, 2 minutes)
  • Sets and Set Operations in Python (video, 6 minutes)
  • List Comprehensions in Python (video, 6 minutes)
  • Generator Expressions in Python (video, 4 minutes)
  • Generator Functions in Python (video, 7 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Essential Python Concepts (quiz, 30 minutes)
  • Sequence Operations (quiz, 30 minutes)
  • Lists and Tuples (quiz, 30 minutes)
  • Range Objects (quiz, 30 minutes)
  • Accessing Data in Dictionaries (quiz, 30 minutes)
  • Sets and Set Operations (quiz, 30 minutes)
  • List Comprehensions (quiz, 30 minutes)
  • Generator Expressions (quiz, 30 minutes)
  • Practicing with Strings in Python (ungraded lab, 60 minutes)
  • Creating Dictionaries in Python (ungraded lab, 60 minutes)
  • Dictionary Views in Python (ungraded lab, 60 minutes)
  • Comprehensions and Generators in Python (ungraded lab, 60 minutes)
  • Practicing Essential Python (ungraded lab, 60 minutes)

Module 3: Data in Python: Pandas and Alternatives (12 hours)

  • Introduction to Data in Python: Pandas and Alternatives (video, 0 minutes)
  • Creating Pandas DataFrames in Python (video, 4 minutes)
  • Investigating Data in a Pandas DataFrame (video, 6 minutes)
  • Selecting Data in a Pandas DataFrame (video, 6 minutes)
  • Manipulating Pandas DataFrames (video, 4 minutes)
  • Updating Pandas DataFrame Data (video, 5 minutes)
  • Applying Functions in a Pandas DataFrame (video, 6 minutes)
  • Creating NumPy Arrays in Python (video, 15 minutes)
  • Spark and PySpark DataFrames in Python (video, 6 minutes)
  • Creating Dask DataFrames in Python (video, 6 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Polars (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Pandas and Alternatives (quiz, 30 minutes)
  • NumPy (quiz, 30 minutes)
  • PySpark (quiz, 30 minutes)
  • Dask (quiz, 30 minutes)
  • Creating DataFrames (ungraded lab, 60 minutes)
  • Looking at Data in DataFrames (ungraded lab, 60 minutes)
  • Selecting Data in a Pandas DataFrame (ungraded lab, 60 minutes)
  • Manipulating DataFrames (ungraded lab, 60 minutes)
  • Updating Data in a DataFrame (ungraded lab, 60 minutes)
  • Applying Functions in a Pandas DataFrame (ungraded lab, 60 minutes)
  • Manipulate DataFrames with Polars to gain insights (ungraded lab, 60 minutes)
  • Pandas and Alternatives (ungraded lab, 60 minutes)

Module 4: Python Development Environments (13 hours)

  • Introduction to Python Development Environments (video, 0 minutes)
  • Introduction to Vim Normal Mode (video, 6 minutes)
  • Switching from Normal to Insert and Visual Modes in Vim (video, 4 minutes)
  • Working with the Vim Command Line (video, 6 minutes)
  • Vim Configuration (video, 3 minutes)
  • Introduction to Visual Studio Code (video, 1 minute)
  • Setting Up Visual Studio Code (video, 2 minutes)
  • Debugging Visual Studio Code (video, 3 minutes)
  • What is Version Control? (video, 3 minutes)
  • Introduction to Git and Git Concepts (video, 7 minutes)
  • Version Control with GitHub (video, 6 minutes)
  • Summary of Python and Pandas for Data Engineering (video, 0 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Key Terms (reading, 10 minutes)
  • Lesson Reflection (reading, 10 minutes)
  • Next Steps (reading, 10 minutes)
  • Cumulative Python and Pandas for Data Engineering Quiz (quiz, 45 minutes)
  • Insert and Visual Modes (quiz, 30 minutes)
  • Vim Command Line Mode (quiz, 30 minutes)
  • Features of Visual Studio Code (quiz, 30 minutes)
  • Version Control (quiz, 30 minutes)
  • Git Commands (quiz, 30 minutes)
  • Hosted Git (quiz, 30 minutes)
  • Basic Vim Commands (ungraded lab, 60 minutes)
  • Explore Visual Studio Code (ungraded lab, 60 minutes)
  • Visual Studio Code Debugger (ungraded lab, 60 minutes)
  • Setup and Provision a Python Project (ungraded lab, 60 minutes)
  • Pandas Final Challenge: Life Expectancy and Happiness (ungraded lab, 60 minutes)
  • Final Jupyter Sandbox (ungraded lab, 60 minutes)
  • Final VS Code Sandbox (ungraded lab, 60 minutes)
  • Final Sandbox Linux Desktop (ungraded lab, 60 minutes)
Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introducción al desarrollo de aplicaciones web (edX) EdX
Universidad Autonoma de Madrid

Introducción al desarrollo de aplicaciones web (edX)

Engineering Computer Science

Aprende a desarrollar una aplicación web desde cero con diferentes tecnologías como HTML, CSS, Python, JSON, JavaScript y Ajax. Hoy en día utilizamos la web para todo tipo de tareas: buscar un vuelo, comprar entradas, ver el pronóstico meteorológico, leer noticias, etc. Todo esto es posible gracias a las aplicaciones web creadas para darnos estos servicios.

Self Paced
Self-Paced
Python HTML Javascript
Details
Programación para todos (empezando con Python) (edX) EdX
University of Michigan,MichiganX

Programación para todos (empezando con Python) (edX)

CS: Programming

Este curso en línea es una introducción "sin prerrequisitos" a la programación en Python. Aprenderás sobre las variables, la ejecución condicional, la ejecución repetida y cómo usamos las funciones. Este curso de Python tiene el objetivo de enseñar a todos lo básico de la programación de computadoras usando Python. Conocerás cómo construir un programa de una serie de instrucciones simples en Python.

Self Paced
Self-Paced
Programming Python Python Programming
Details
Computing in Python IV: Objects & Algorithms (edX) EdX
Georgia Institute of Technology,GTx

Computing in Python IV: Objects & Algorithms (edX)

CS: Programming

Learn about recursion, search and sort algorithms, and object-oriented programming in Python. Complete your introductory knowledge of computer science with this final course on objects and algorithms. Now that you've learned about complex control structures and data structures, learn to develop programs that more intuitively leverage your natural understanding of problems through object-oriented programming. Then, learn to analyze the complexity and efficiency of these programs through algorithms. In addition, certify your broader knowledge of Introduction to Computing with a comprehensive exam.

Self Paced
Self-Paced
Programming Python Computing
Details
PyTorch Basics for Machine Learning (edX) EdX
IBM

PyTorch Basics for Machine Learning (edX)

Statistics & Data Analysis Data Science

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

Self Paced
Self-Paced
ML Machine Learning Linear Regression
Details