A comprehensive guide to mastering data structures and program design using Python, perfect for self-learners and students alike.

Key Features

Hands-on exercises to reinforce learning

Comprehensive coverage of fundamental and advanced topics

Practical examples to apply concepts in real-world scenarios

Book Description

This book, part of the Pocket Primer series, introduces the basic concepts of data science using Python 3 and other applications. It offers a fast-paced introduction to data analytics, statistics, data visualization, linear algebra, and regular expressions. The book features numerous code samples using Python, NumPy, R, SQL, NoSQL, and Pandas. Companion files with source code and color figures are available. Understanding data science is crucial in today’s data-driven world. This book provides a comprehensive introduction, covering key areas such as Python 3, data visualization, and statistical concepts. The practical code samples and hands-on approach make it ideal for beginners and those looking to enhance their skills. The journey begins with working with data, followed by an introduction to probability, statistics, and linear algebra. It then delves into Python, NumPy, Pandas, R, regular expressions, and SQL/NoSQL, concluding with data visualization techniques. This structured approach ensures a solid foundation in data science.

What you will learn

Understand the basics of data structures

Learn Python programming essentials

Implement and manipulate various data structures

Analyze algorithms and their efficiency

Learn about arrays, trees, linked lists, stacks, etc.

Apply data structures to solve real-world problems

Who this book is for

Ideal for students, self-learners, and programming enthusiasts, this book requires a basic understanding of programming concepts. No prior knowledge of Python is needed as the book covers Python essentials.