Dive into Python 3 and key data analytics libraries with this pocket primer. Learn to preprocess, analyze, and visualize data efficiently.

Key Features

• Concise introduction to Python for data analytics
• Practical examples and exercises for hands-on learning
• Covers NumPy, Pandas, Matplotlib, and more

Book Description

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.

What you will learn

• Understand basic Python 3 syntax
• Preprocess various data types
• Utilize NumPy for numerical operations
• Manipulate data with Pandas
• Visualize data using Matplotlib and Seaborn
• Handle regular expressions in Python

Who this book is for

The book is ideal for Python developers who want to delve into data analytics. A basic understanding of Python is required, as the book progresses from fundamental concepts to more advanced topics. No prior knowledge of data analytics is needed, but familiarity with programming concepts will be beneficial.

Dive into Python 3 and key data analytics libraries with this pocket primer. Learn to preprocess, analyze, and visualize data efficiently.

Key Features

• Concise introduction to Python for data analytics
• Practical examples and exercises for hands-on learning
• Covers NumPy, Pandas, Matplotlib, and more

Book Description

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.

What you will learn

• Understand basic Python 3 syntax
• Preprocess various data types
• Utilize NumPy for numerical operations
• Manipulate data with Pandas
• Visualize data using Matplotlib and Seaborn
• Handle regular expressions in Python

Who this book is for

The book is ideal for Python developers who want to delve into data analytics. A basic understanding of Python is required, as the book progresses from fundamental concepts to more advanced topics. No prior knowledge of data analytics is needed, but familiarity with programming concepts will be beneficial.

Dive into Python 3 and key data analytics libraries with this pocket primer. Learn to preprocess, analyze, and visualize data efficiently.

Key Features

• Concise introduction to Python for data analytics
• Practical examples and exercises for hands-on learning
• Covers NumPy, Pandas, Matplotlib, and more

Book Description

This book, part of the best-selling Pocket Primer series, introduces readers to the fundamental concepts of data analytics using Python 3. The course begins with a concise introduction to Python, covering essential programming constructs and data manipulation techniques. This foundation sets the stage for deeper dives into data analytics, emphasizing the importance of data cleaning, a critical step in any data analysis process.Following the Python basics, the course explores powerful libraries such as NumPy and Pandas for efficient data handling and manipulation. It then delves into statistical concepts, providing the necessary background for understanding data distributions and analytical methods. The course culminates in data visualization techniques using Matplotlib and Seaborn, demonstrating how to effectively communicate insights through graphical representations.Throughout the course, numerous code samples and practical examples are provided, reinforcing learning and offering hands-on experience. Companion files with source code and figures are available online, supporting the learning journey. This comprehensive guide equips both beginners and seasoned professionals with the skills needed to excel in data analytics.

What you will learn

• Understand basic Python 3 syntax
• Preprocess various data types
• Utilize NumPy for numerical operations
• Manipulate data with Pandas
• Visualize data using Matplotlib and Seaborn
• Handle regular expressions in Python

Who this book is for

The book is ideal for Python developers who want to delve into data analytics. A basic understanding of Python is required, as the book progresses from fundamental concepts to more advanced topics. No prior knowledge of data analytics is needed, but familiarity with programming concepts will be beneficial.