Welcome to CDI Data Science Foundations in Python

Welcome to Complex Data Insights (CDI) — a practical learning pathway designed to teach real data skills through clear explanations, structured lessons, and hands-on practice. This guide focuses entirely on Python, the most widely used language in data analytics, machine learning, and scientific computing.

Whether you are new to data science or returning to data work, this guide will help you build skills progressively — from foundational concepts to professional workflows.

What You Will Learn

Across this guide, you will learn how to:

Think analytically about data problems
Load, clean, and structure datasets
Explore data through visualization
Apply essential statistical reasoning
Prepare data for machine learning
Work with realistic, real-world datasets
Build reproducible, end-to-end workflows

Each lesson builds on the previous one, forming a clear and logical progression.

Course Structure

The CDI Data Science Foundations path is divided into two parts.

Free Track (Lessons 01–06)

The free track introduces the core foundations of data science with Python, including:

Environment setup and workflow
Loading and inspecting datasets
Data cleaning fundamentals
Introductory data wrangling
Visualization basics
Summary statistics and insight generation

These lessons build confidence with essential data tools and concepts.

Premium Track (Lessons 07–19)

The premium track builds on the foundations and moves into applied, real-world data science, including:

Intermediate and advanced data wrangling
Working with realistic, messy datasets
Advanced exploratory data analysis
Feature engineering
Machine learning (classification and regression)
Model evaluation and tuning
End-to-end projects and deployment workflows

Note: Files labeled 06x* and 19x* are transition or completion pages, not lessons.

How to Use This Guide

This guide is designed to be:

Self-paced — learn at your own speed
Hands-on — practice with real code and datasets
Modular — revisit lessons as needed
Accessible — explained step by step, with context

Lesson Workflow

Each lesson follows a consistent structure:

Concept explanation
Guided code examples
Output interpretation
Short exercises
Key takeaways
Next steps

Data and Project Structure

Throughout the guide:

All datasets are stored in a data/ directory
Lesson 01 generates the first dataset (iris.csv)
Additional datasets are introduced as needed in later lessons

This consistent structure supports reproducibility and clarity.

About This Guide

This guide follows a human-first, carefully curated approach.
AI tools were used as assistants during drafting and refinement,
with all final content reviewed and approved by a human.

Continue to Lesson 01 — Setting Up Your Data Analysis Environment