Programming in Python: Foundations for AI and Data Science

Master the art and science of data-driven problem solving with Python. This hands-on course is designed for learners who want to move beyond Python basics and into the fast-paced world of data science, artificial intelligence, and real-world application development.

Across 10 immersive, AI-guided lessons, you'll gain practical skills in data analysis, visualization, statistics, machine learning, and Python development workflows. Each lesson features real-world scenarios, challenge exercises, and smart agent coaching to help you understand not just how to do something—but why it works.

The course concludes with a powerful Capstone Project, where you'll build a real data-driven Python application that brings together everything you've learned.

Whether you're looking to launch a new career in data science or turbocharge your current role with Python and AI skills, this course delivers a complete, practical path forward.

What You’ll Learn:

  • Build intelligent, data-driven Python applications from scratch

  • Use pandas, NumPy, and Python to wrangle and explore data

  • Visualize insights using Matplotlib and Seaborn like a pro

  • Master descriptive and inferential statistics for decision-making

  • Apply machine learning for real-world classification and regression

  • Leverage NLP, feature engineering, and model evaluation techniques

  • Think like a data scientist: ask the right questions and build solutions

Lessons Overview:

  1. Python for Data Science – Advanced data types, control flow, and functions

  2. Data Wrangling with pandas – Clean, manipulate, and explore datasets

  3. Exploratory Data Analysis (EDA) – Uncover hidden trends and patterns

  4. Functional Programming and Efficiency – Write clean, modular Python code

  5. APIs and Web Data – Connect your app to the real world

  6. Text Analytics and NLP – Turn messy text into structured insight

  7. Data Visualization – Build powerful plots with Matplotlib and Seaborn

  8. Probability & Statistics – Go from intuition to statistical confidence

  9. Machine Learning Fundamentals – Classification, regression, and more

  10. Real-World ML Workflows – Pipelines, validation, deployment strategies

Capstone Project:

Build a Data-Driven Python Application
In your final project, you'll apply everything you’ve learned to build a complete, professional-grade Python application that solves a real-world data challenge—end to end. From ingesting and cleaning data to modeling and visualizing insights, you’ll walk away with a portfolio-ready project to showcase your skills.

Who Should Enroll:

  • Aspiring data scientists and analysts

  • Python developers looking to enter the AI/ML space

  • Business professionals who want to make data-driven decisions

  • Career switchers ready for the world of applied data science

Prerequisites:

  • Basic understanding of Python programming

  • Curiosity, persistence, and a desire to work with real data

Category: Software Development
Level: Intermediate
Course Code: PY-AI-DS
Duration: 1d 13h 0m
Price: $99.99 for 6 months
Tokens Required: 1
Access Duration: 6 months
Lab Environment: ✓ Included
Programming in Python: Foundations for AI and Data Science Achievement Badge
Join to Start Learning

Course Curriculum

Lesson 1: Introduction to Python and Programming Fundamentals

Start your Python journey with a solid foundation in coding essentials. Learn how to write your first programs using variables, control flow, loops, and Pythonic conventions.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Python Environment Setup and Basic Syntax
Verify your Python installation and explore basic Python syntax, variables, and data types. Understanding data types is crucial for data science as different types of data (numbers, text, boolean values) require different processing approaches.
Estimated Time: 45 minutes
Exercise 2: Control Flow with Conditional Statements
Learn to use if, elif, and else statements to create decision-making logic in Python programs. Conditional statements are fundamental to data science because they enable data filtering, classification, and automated decision-making based on data patterns.
Estimated Time: 45 minutes
Exercise 3: Loops and Iteration
Master for and while loops to create repetitive processes and iterate through data collections. Loops are fundamental to data science because they enable automated processing of large datasets, repetitive calculations, and iterative algorithms that form the backbone of machine learning.
Estimated Time: 45 minutes
Exercise 4: Building Complete Applications - Calculator and Number Guessing Game
Combine all learned concepts (variables, conditionals, loops, input/output) to build two complete Python applications: an advanced calculator and an intelligent number guessing game. This exercise demonstrates how individual programming concepts integrate to create sophisticated data science tools and interactive applications.
Estimated Time: 45 minutes

Lesson 2: Data Structures and Algorithms in Python

Unlock Python’s built-in power tools—lists, dictionaries, sets, and tuples—and apply them to real problem-solving. You’ll also explore simple algorithms and Big O basics.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Lists and Tuples - Sequential Data Structures
Master Python's ordered data structures (lists and tuples) for handling sequential data common in time series analysis, sensor data, and ordered datasets. Learn efficient manipulation techniques and understand when to use mutable vs immutable structures.
Estimated Time: 30 minutes
Exercise 2: Dictionaries and Sets - Key-Value Mappings and Unique Collections
Master Python's dictionaries and sets for efficient data lookups, mappings, and unique value operations. These structures are essential for data indexing, feature encoding, deduplication, and creating fast lookup tables that are fundamental to data science workflows.
Estimated Time: 30 minutes
Exercise 3: Stacks, Queues, and Collections Module
Master advanced data structures including stacks, queues, and Python's collections module for specialized data processing tasks. These structures are essential for algorithm implementation, data processing pipelines, and handling ordered operations in data science workflows.
Estimated Time: 30 minutes
Exercise 4: Searching and Sorting Algorithms
Master fundamental algorithms for searching and sorting data, which form the backbone of data analysis, database operations, and machine learning preprocessing. Learn to implement, optimize, and apply these algorithms to real-world data science scenarios.
Estimated Time: 30 minutes

Lesson 3: Functions, Modules, and Error Handling

Build cleaner, reusable code using functions and modules. Learn how to gracefully handle errors and tap into Python’s standard library to accelerate your workflow.

Estimated Duration: 3 h 0 m

Hands-On Exercises

Exercise 1: Functions and Scope - Building Reusable Data Processing Components
Master Python functions for creating modular, reusable data processing components. Learn about function parameters, return values, scope, and best practices for writing maintainable code that scales across data science projects.
Estimated Time: 30 minutes
Exercise 2: Lambda Functions and Python Standard Library
Master Python's lambda functions and explore essential standard library modules for data science applications. Learn when and how to use anonymous functions effectively, and discover powerful built-in tools that accelerate data processing workflows.
Estimated Time: 30 minutes
Exercise 3: Exception Handling and Error Management
Master Python's exception handling mechanisms to build robust, fault-tolerant data science applications. Learn to anticipate, catch, and gracefully handle errors that commonly occur in data processing workflows, ensuring your programs can continue operating even when encountering unexpected conditions.
Estimated Time: 30 minutes

Lesson 4: Working with Files and Data Serialization

Bridge your code to the outside world. Read and write text, CSV, and JSON files, and pull live data from APIs to power your Python apps.

Estimated Duration: 3 h 0 m

Hands-On Exercises

Exercise 1: File I/O Operations - Reading and Writing Files
Master Python's file I/O operations for handling various data formats commonly encountered in data science workflows. Learn proper file handling techniques, encoding management, and error-safe file operations that form the foundation of data processing pipelines.
Estimated Time: 30 minutes
Exercise 2: CSV and JSON Data Processing
Master working with CSV and JSON data formats, which are among the most common data interchange formats in data science. Learn to read, write, validate, and transform data in these formats while handling real-world data quality issues and performance considerations.
Estimated Time: 30 minutes

Lesson 5: Object-Oriented Programming in Python

Level up with object-oriented programming. Master classes, inheritance, and encapsulation to model complex systems like a pro.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Basic Classes and Objects - Data Point Modeling
Learn the fundamental concepts of class definition and object creation by building a simple data point class. Master basic class syntax, object instantiation, and instance methods while understanding how objects represent real-world data entities.
Estimated Time: 45 minutes
Exercise 2: Inheritance and Polymorphism - Sensor Hierarchy
Learn inheritance concepts by creating a hierarchy of sensor classes. Understand how subclasses inherit from parent classes, override methods, and implement polymorphism to handle different types of sensors uniformly.
Estimated Time: 45 minutes
Exercise 3: Encapsulation and Properties - Secure Data Management
Learn encapsulation concepts by creating classes that protect internal data and provide controlled access through properties. Understand private attributes, getter/setter methods, and data validation in object-oriented design.
Estimated Time: 45 minutes
Exercise 4: Composition and Aggregation - Building Complex Systems
Learn composition and aggregation concepts by building complex systems from simpler objects. Understand the difference between "has-a" relationships, object lifetime management, and how to create sophisticated data science workflows using object collaboration.
Estimated Time: 30 minutes

Lesson 6: Working with Data using Pandas and NumPy

Get hands-on with the tools that power data science. Clean, slice, and transform real datasets using Pandas and NumPy—the backbone of modern analytics.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: NumPy Array Fundamentals - Scientific Data Structures
Master the fundamental concepts of NumPy arrays and understand how they differ from Python lists. Learn array creation, indexing, slicing, and basic operations that form the foundation of all numerical computing in Python.
Estimated Time: 45 minutes
Exercise 2: Advanced NumPy Operations - Matrix Operations and Linear Algebra
Master advanced NumPy operations including matrix operations, linear algebra, advanced indexing, and array manipulation techniques. Learn how to perform sophisticated mathematical operations that form the backbone of machine learning and scientific computing.
Estimated Time: 45 minutes
Exercise 3: Pandas DataFrames and Series - Structured Data Manipulation
Master Pandas DataFrames and Series for structured data manipulation. Learn how to import, clean, transform, and analyze real-world datasets using Pandas' powerful data structures and methods that form the core of data science workflows.
Estimated Time: 45 minutes
Exercise 4: Advanced Pandas and Data Integration - Real-World Data Analysis
Master advanced Pandas techniques for real-world data analysis including data merging, reshaping, advanced transformations, and performance optimization. Learn to integrate multiple data sources and perform complex analytical workflows that mirror professional data science projects.
Estimated Time: 30 minutes

Lesson 7: Data Visualization with Matplotlib and Seaborn

Tell compelling stories with data. Create insightful visualizations that make trends, patterns, and relationships easy to understand.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Matplotlib Fundamentals - Creating Professional Visualizations
Master the fundamental concepts of Matplotlib for creating high-quality data visualizations. Learn the anatomy of a matplotlib figure, basic plot types, customization techniques, and best practices for creating publication-ready graphics.
Estimated Time: 45 minutes
Exercise 2: Advanced Matplotlib and Statistical Graphics - Complex Visualizations
Master advanced matplotlib techniques for creating sophisticated statistical visualizations and complex multi-dimensional data representations. Learn specialized plot types, statistical graphics, and advanced customization techniques that are essential for professional data analysis and scientific visualization.
Estimated Time: 45 minutes
Exercise 3: Seaborn Statistical Visualization - High-Level Statistical Graphics
Master Seaborn for creating beautiful, informative statistical visualizations with minimal code. Learn how Seaborn builds on matplotlib to provide high-level interfaces for statistical graphics, built-in statistical functions, and attractive default styling that makes professional visualizations accessible.
Estimated Time: 45 minutes
Exercise 4: Real-World Data Visualization Projects - Complete Analytical Workflows
Apply all matplotlib and seaborn skills to complete real-world data visualization projects that mirror professional data science workflows. Create comprehensive analytical dashboards, scientific publications, business intelligence reports, and interactive presentations that demonstrate mastery of advanced visualization techniques.
Estimated Time: 30 minutes

Lesson 8: Introduction to Probability and Statistics in Python

Develop statistical intuition for data science. Use Python to explore distributions, correlation, and probability concepts critical for AI and analytics.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Statistical Fundamentals and Descriptive Statistics
Master the fundamental concepts of descriptive statistics and their implementation in Python. Learn to summarize and describe datasets using measures of central tendency, variability, and distribution shape, while understanding when and how to apply different statistical measures.
Estimated Time: 45 minutes
Exercise 2: Probability Theory and Distributions - Understanding Random Events
Master probability theory fundamentals and explore common probability distributions using Python. Learn to model random events, calculate probabilities, and understand how different distributions apply to real-world scenarios in data science and machine learning.
Estimated Time: 45 minutes
Exercise 3: Correlation and Hypothesis Testing - Statistical Inference
Master correlation analysis and hypothesis testing techniques to draw statistical inferences from data. Learn to quantify relationships between variables, test scientific hypotheses, and make data-driven decisions with appropriate statistical rigor.
Estimated Time: 45 minutes
Exercise 4: Real-World Statistical Applications - Complete Statistical Analysis Projects
Apply all statistical concepts learned in previous exercises to complete real-world analytical projects. Develop end-to-end statistical analysis workflows that mirror professional data science practices, from data exploration through statistical inference to actionable insights.
Estimated Time: 45 minutes

Lesson 9: Getting Started with Machine Learning (Scikit-learn Intro)

Take your first steps into AI. Build simple machine learning models using scikit-learn to make predictions from data.

Estimated Duration: 4 h 0 m

Hands-On Exercises

Exercise 1: Machine Learning Fundamentals and Data Preprocessing
Master the fundamental concepts of machine learning and learn essential data preprocessing techniques. Understand different types of ML problems, the importance of data quality, and how to prepare datasets for modeling using scikit-learn's preprocessing tools.
Estimated Time: 45 minutes
Exercise 2: Supervised Learning - Classification Algorithms
Master classification algorithms and learn to build, evaluate, and optimize classification models using scikit-learn. Understand different classification techniques, performance metrics, and how to select the best algorithm for specific problems.
Estimated Time: 45 minutes
Exercise 3: Supervised Learning - Regression Algorithms
Master regression algorithms and learn to build, evaluate, and optimize regression models using scikit-learn. Understand different regression techniques, performance metrics, and how to handle various regression challenges including non-linearity and overfitting.
Estimated Time: 30 minutes

Lesson 10: Introduction to AI Workflows and Tools

Explore the broader AI landscape and professional tools used in the industry, including Jupyter, GitHub, VS Code, and ethical AI best practices.

Estimated Duration: 3 h 0 m

Hands-On Exercises

Exercise 1: Clustering Algorithms
Master clustering algorithms and learn to discover hidden patterns in unlabeled data using scikit-learn. Understand different clustering techniques, evaluation metrics, and how to select optimal cluster numbers and algorithms for various data structures.
Estimated Time: 45 minutes
Exercise 2: Dimensionality Reduction
Master dimensionality reduction techniques to handle high-dimensional data, reduce computational complexity, and visualize complex datasets. Learn to apply Principal Component Analysis (PCA), t-SNE, and other reduction methods for data exploration, visualization, and preprocessing in machine learning pipelines.
Estimated Time: 45 minutes
Exercise 3: Association Rules and Market Basket Analysis
Master association rule mining and market basket analysis to discover relationships between items in transactional data. Learn to identify patterns, generate actionable insights, and apply these techniques to real-world business problems.
Estimated Time: 45 minutes