Chapter 12: Data Analysis - Overview

In today’s data-driven world, analyzing and interpreting data has become a crucial skill. Chapter 12 introduces key tools in Python that allow you to handle, clean, transform, and extract insights from data efficiently. Whether you're a developer, data analyst, or aspiring data scientist, this chapter will equip you with practical skills that can be applied in real-world projects.

Topics Covered

• NumPy: The foundation of scientific computing in Python. You'll learn how to create and manipulate arrays, perform mathematical operations, and apply powerful broadcasting rules to process large datasets efficiently.
• Pandas: A high-level data manipulation library built on NumPy. You'll learn how to read and write tabular data, handle missing values, merge datasets, group data, and perform statistical analysis.
• DuckDB: An embedded SQL database engine designed for fast analytics. You'll explore how to run SQL queries directly on Pandas DataFrames and large files without loading everything into memory.
• SQL for Data Analysis: Learn how to use SQL syntax for querying, joining, filtering, and aggregating data. We'll apply SQL on structured files (like CSV) and databases using Python interfaces.

Real-World Applications

• Analyze sales, stock, or customer data to identify trends and patterns
• Clean and preprocess data before training machine learning models
• Automate reporting tasks and generate summaries
• Run fast analytical queries over gigabytes of tabular data

Skills You'll Gain

• Efficient data loading and exploration using Pandas and NumPy
• Performing SQL queries on local files using DuckDB
• Filtering, grouping, and summarizing data using Pandas and SQL
• Handling real-world messy datasets: missing data, duplicates, and outliers

Prerequisites

• Comfortable with basic Python (lists, dictionaries, functions)
• Understanding of file handling and simple Python programs
• Optional: Basic familiarity with tabular data and statistics

With the tools and techniques introduced in this chapter, you'll be able to make sense of complex datasets, automate common data workflows, and prepare your data for deeper analysis or machine learning.