Python for Data and AI Engineers

Getting Started with Python

Python is a high-level, interpreted, interactive and object-oriented scripting language. This chapter introduces Python, including how to install it and run your first lines of code. There are quite some editors available for writing Python code but this course focusses on using Visual Studio Code. We'll also cover modern Python tooling including uv, a fast Python package installer and project manager.

• Introduction to Python
• Installing Python
• Executing Python Code from the Command Shell
• Python and Visual Studio Code
• Working with packages in Python
• Working with Virtual Environments in Python
• Modern Python tooling with uv
• Interactive development in Jupyter notebooks
• LAB: Installing Python and executing code

Basic Language Constructs in Python

We explore programming in Python by discussing some basic syntax, variables and conditional statements.

• Introduction to Python Coding Syntax
• Declaring and Using Variables
• Data Types in Python
• Conditional Statements
• LAB: Writing basic Python code

Collections and Loops

Collections allow you to store and organize data efficiently, making it easier to handle. Loops help you repeat actions on these collections.

• Collections: Lists, Tuples, Sets and Dictionaries
• Loops in Python
• List Comprehension
• Immutable and Mutable Types
• LAB: Collections and Loops

Functions and Error Handling

We explore how to structure reusable code, handle unexpected situations and manage resources.

• Functions
• Exception Handling
• Context Manager
• LAB: Functions and Error Handling

Working with Classes and Objects

Python classes provide all the standard features of Object Oriented Programming: they can inherit from (multiple) other base classes, leverage modern Python features like dataclasses and context managers, ...

• Introduction to Object-Oriented Programming
• Defining and instantiating Classes in Python
• Working with Constructors
• Instance and Class Variables
• Class and Static Methods
• Properties
• Inheritance
• Multiple Inheritance
• Abstract Base Classes
• Working with Access Modifiers
• Python dataclasses for simplified class definitions
• Context Managers
• LAB: Working with classes and objects

Using and Creating Modules

Modules in Python are reusable code libraries and Python ships with quite a large amount of built-in Modules. We learn how to import them and create our own Modules.

• Introduction to Modules
• Importing Modules
• Creating Modules
• LAB: Using and creating Modules

The Python Standard Library and External Ecosystem

You do not need to reinvent the wheel when coding in Python. Its Standard Library offers a rich collection of built-in modules that simplify common tasks, while external libraries provide specialized tools for modern development.

• Introduction to the Standard Library
• Date and Time Handling
• Managing Files and Data (os, pathlib, json)
• Internet Protocols and Network Communication
• Popular External Python Frameworks: Overview of Requests, FastAPI, PyTorch, BeautifulSoup and SQLAlchemy
• LAB: Exploring the Python Standard Library

Model Validation with Pydantic

Pydantic is a powerful Python library that uses Python type annotations to validate data and settings management. It provides runtime type checking and automatic data conversion, making it essential for building robust data pipelines and APIs. This chapter covers how to define data models, validate complex data structures, and handle validation errors effectively.

• Introduction to Pydantic and data validation
• Defining BaseModel classes and field types
• Working with built-in validators and custom validation
• Pydantic Configuration
• Validation error handling and custom error messages
• Data Parsing and Serialization
• LAB: Implementing data validation for an e-commerce order processing system

Unit Testing in Python

Testing is a critical aspect of software development that ensures code reliability and maintainability. Python provides excellent testing frameworks, with pytest being the most popular choice for its simplicity and powerful features. This chapter covers writing effective unit tests, mocking dependencies, and implementing test-driven development practices for data engineering and app development.

• Introduction to unit testing concepts
• Getting started with pytest framework
• Writing test functions and organizing test files
• Mocking external dependencies and APIs
• LAB: Implementing unit tests for Python applications

Data Processing and Cleansing using Pandas

Pandas is a Python library which makes loading and transforming data a lot easier. As long as all your data fits in memory, Pandas is your friend.

• What is Pandas
• Introducing Pandas Data Structures
• Reading and Writing Data
• Inspecting and Slicing Data: Selecting, Filtering and Sorting Data
• Altering Data: Creating, Deleting and Renaming Columns
• Copy-On-Write
• Analysing Data: Count Distributions, Missing Values, ...
• Discretizing Data into Bins
• Grouping and Aggregating Data
• LAB: Working with Pandas

Exploratory Data Analysis and Visualization

Data visualization is a critical skill for data scientists and engineers to communicate insights and identify patterns or anomalies in data. This chapter explores the Python visualization ecosystem, starting from basic plotting in Matplotlib and Pandas, moving to the sophisticated statistical aesthetics of Seaborn, and concluding with interactive, web-ready visualizations using Plotly.

• The need for Visualization
• Pandas Visualization: Quick plotting directly from DataFrames
• Matplotlib: The foundation of Python plotting
• Seaborn: Statistical data visualization
• Plotly: Creating interactive and dynamic charts for the web
• LAB: Data Visualization with Python

Exploring Data Lakes and Apache Spark

Data lakes allows storing large data volumes in their original format, but Pandas doesn’t scale well. Apache Spark enables distributed processing, and PySpark brings it to Python (available in Micorsoft Fabric, Azure Synapse Analytics and Databricks).

• Storing your Data in a Data Lake
• Working with Parquet files
• What is Apache Spark?
• From Pandas to PySpark
• Spark SQL
• LAB: Apache Spark in Databricks