Part 1: Preparing for Data Analysis

Chapter 1 Before starting data analysis
_1.1 Overview of the data analysis process
_1.2 Exploring Data Analysis Tools

Chapter 2: Setting Up the Development Environment
_2.1 Installing Anaconda
_2.2 Using Jupyter Notebook

Chapter 3: Python for Data Analysis
_3.1 Python Basic Grammar
_3.2 Python Data Structures
_3.3 Practice: Using Python's Basic Grammar and Data Structures

Part 2: Working with Pandas

Chapter 4: Learning Pandas Basics
_4.1 Using Pandas
_4.2 Handling the Series
_4.3 Handling Data Frames

Chapter 5: Using Pandas Functions
_5.1 Loading data
_5.2 Checking the data
_5.3 Obtaining individual statistics

Chapter 6 Data Preprocessing
_6.1 Handling missing values
_6.2 Converting data types
_6.3 Reconstructing Data
_6.4 Data Cleaning
_6.5 Merging Data

Part 3: Practicing Data Analysis and Visualization

Chapter 7: Analyzing Movie Data
_7.1 Loading Data
_7.2 Checking the data
_7.3 Analyzing movie ratings and number of participants

Chapter 8: Analyzing Real Estate Data
_8.1 Checking data
_ 8.2 Analyzing Real Estate Sales Prices

Chapter 9: Visualizing Data with Plotly
_9.1 Plotly Overview
_9.2 Visualizing with histograms
_9.3 Visualizing with a line graph
_9.4 Visualizing with a Bar Graph

Chapter 10: Analyzing Marketing Data
_10.1 Load and check data
_10.2 Analyzing Data
_10.3 Visualizing Data

Chapter 11: Deepening Data Analysis and Visualization
_11.1 Deep Analysis of Movie Data
_11.2 Deep dive into real estate data
_11.3 Analyzing and Visualizing E-Commerce Data

Appendix A Future Study Guide

A.1 Learning Pandas and Plotly for Beginners
A.2 Data Field Learning Roadmap
A.3 Data Analysis and Careers
Appendix B: How to Use Generative AI
B.1 ChatGPT and Claude

[Recommendation]

This book systematically covers everything from the basics of data analysis and visualization to practical applications, making it extremely useful for those entering the data field or looking to improve their skills.
What's particularly impressive is the focus on hands-on training using real data and strengthening data-driven decision-making skills.
It emphasizes the importance of data literacy and DDD, and presents a path for readers to grow into data experts.
I am very satisfied with the content and would recommend it to anyone interested in data analysis.
_Park Sang-gil

I'm studying Python and using it to automate tasks like work and stock investment.
This book is suitable for beginners as well as those like me who want to analyze large amounts of data with Python to gain insights.
Anyone seeking a career in data analysis, in particular, is encouraged to read the Future Study Guide in Appendix A first.
_Choi Gyu-min

This book is designed to be easy to understand and follow, even for those unfamiliar with data analysis.
It provides interpretation of code execution results and comprehensive analysis of data, providing guidance on how to write Python code and analyze results using the right logic for future data analysis.
I highly recommend this book to anyone who wants to learn data preprocessing and data visualization, essential for machine learning and deep learning, and derive new insights.
_Im Seung-min

Personally, I found the latter half of the book to be very helpful, including examples of using Pandas and analysis.
There are many books on Pandas and data analysis on the market, but few provide as many real-world examples and variety as this book.
Compared to the books I have, I think this book is far superior in terms of variety and detail.
_Ji Yong-ho

Studying with this book allowed me to thoroughly learn even the parts I had missed while learning data analysis before, and to fill in all the parts I had missed.
Covering a wide range of topics, including Python, Pandas, and Plotly, this book focuses on data preprocessing and analysis, allowing you to delve into the core concepts quickly.
I especially liked the detailed explanations of concepts and terms.
I also really enjoyed being able to practice with real-world data like marketing data, movies, and real estate data.
_Kim Soo-jung

Using Plotly instead of Matplotlib allowed me to take data visualization in a new direction.
The process of data import, interpretation, and missing value correction required for data analysis was organic, and detailed explanations were provided for each step, making the functions easy to remember and reference.
The explanation wasn't difficult, so I was able to follow along easily.
_Son Gye-won