Jain Aniket. Python for Data Analysis: A Beginner's Guide to Data Science

Independently published, 2025. — 170 p. — ISBN-13 979-8309129836.Unlock the power of Python for data analysis with "Python for Data Analysis: A Beginner's Guide to Data Science". This comprehensive guide is designed for beginners who want to master the art of analyzing data using Python, one of the most popular programming languages in the world. Whether you're a student, a professional, or someone looking to break into the field of data science, this book will equip you with the essential skills to transform raw data into actionable insights.Starting with the basics, you'll learn how to set up your Python environment, work with essential libraries like NumPy, Pandas, Matplotlib, Seaborn, and SciPy, and perform data manipulation and cleaning. As you progress, you'll dive into exploratory data analysis (EDA), data visualization, and machine learning, gaining hands-on experience with real-world datasets and case studies. The book also covers advanced topics such as time series analysis, web scraping, SQL integration, and automating data workflows, ensuring you have a well-rounded understanding of Python's capabilities for data analysis.This book makes complex concepts easy to understand with clear explanations, practical examples, and step-by-step tutorials. You'll also find valuable resources, including a Python cheat sheet, recommended books and courses, and practice projects to help you continue your learning journey.Whether you're analyzing data for business, research, or personal projects, "Python for Data Analysis: A Beginner's Guide to Data Science" is your ultimate companion for mastering Python and unlocking the full potential of data analysis. Start your journey today and turn data into insights with confidence!
Preface
Introduction to Data Analysis
Setting Up Your Python Environment
Python Basics for Data Analysis
Essential Python Libraries
Working with NumPy Arrays
Data Manipulation with Pandas
Data Cleaning and Preprocessing
Working with Dates and Times
Introduction to Data Visualization
Basic Visualization with Matplotlib
Advanced Visualization with Seaborn
Exploratory Data Analysis (EDA)
Case Study: EDA on a Real Dataset
Introduction to Machine Learning
Data Preparation for Machine
Building Your First ML Model
Working with Large Datasets
Time Series Analysis
Data Storytelling and Reporting
Web Scraping for Data Collection
Integrating SQL with Python
Real-World Case Studies
Advanced Python Libraries
Automating Data Workflows
Next Steps and Resources