Master data analysis with Python in this comprehensive guide, covering everything from cleaning datasets to visualizing data.

Key Features

Step-by-step explanations for all key concepts

Practical examples and hands-on exercises

Comprehensive coverage of data analysis, statistics, and visualization

Book Description

This book ushers readers into the world of data, emphasizing its importance in modern industries and how its management leads to insightful decision-making. Using Python 3, the book introduces foundational data tasks and progresses to advanced model training concepts. Detailed, step-by-step Python examples help readers master training models, starting with the kNN algorithm and moving to other classifiers with minimal code adjustments. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced for hands-on chart and graph rendering. The course begins with working with data, detecting outliers and anomalies, and cleaning datasets. It then introduces statistics and progresses to using Matplotlib and Seaborn for data visualization. Each chapter builds on the previous one, ensuring a comprehensive understanding of data management and analysis. These concepts are crucial for making data-driven decisions. This book transitions readers from basic data handling to advanced model training, blending theoretical knowledge with practical skills. Companion files with source code and data sets enhance the learning experience, making this book an invaluable resource for mastering data science with Python.

What you will learn

Understand the basics of data literacy

Identify and handle outliers and anomalies

Clean and preprocess datasets

Apply statistical methods

Visualize data using Matplotlib and Seaborn

Utilize Python and Pandas for data analysis

Who this book is for

This book is ideal for beginners and intermediate data analysts, data scientists, and Python programmers. A basic understanding of Python is recommended but not required, as the book includes an introduction to Python. No prior knowledge of data analysis is necessary, making it accessible to anyone interested in data literacy.