Note
__Learn 100% from "Data Analysis with Python, Completely Revised Edition"
__Introduction to the "Starting with Python" series
__Learning Roadmap for IT Careers

Chapter 1.
Getting Started with Python
__1.1 What can you do with Python?
____1.1.1 Why should I start data analysis with Python?
____1.1.2 Python, what are its features?
____1.1.3 What can you do with Python?
____1.1.4 What can I do after studying this book?
__1.2 Setting up the development environment
____1.2.1 What is Anaconda?
____1.2.2 Download the Anaconda installation file
____1.2.3 Installing Anaconda
__1.3 Jupyter Notebook
____1.3.1 Hello Python
____1.3.2 Jupyter Notebook Features
____1.3.3 Jupyter Notebook Key Features
____1.3.4 Using Jupyter Notebooks

Chapter 2.
Basic Python Grammar for Data Analysis
__2.1 Variables and data types
____2.1.1 Variables
____2.1.2 Basic data types
____2.1.3 Container Data Types - Lists
____2.1.4 Container Data Types - Tuples and Sets
____2.1.5 Container Data Types - Dictionary
__2.2 Conditional statements and loops
____2.2.1 Conditional statements
____2.2.2 Loops
__2.3 Functions and Modules
____2.3.1 Function
____2.3.2 Lambda expressions
____2.3.3 Input/Output
____2.3.4 File I/O
____2.3.5 String
____2.3.6 Modules, Packages, and Classes
____2.3.7 Exception Handling

Chapter 3.
Python Applied Grammar for Data Analysis
__3.1 NumPy
____3.1.1 Array Concept
____3.1.2 Creating and Manipulating Arrays
____3.1.3 Indexing and Slicing Arrays
____3.1.4 Array operations and transformations
__3.2 Pandas
____3.2.1 Series and DataFrame concepts
____3.2.2 Creation and Deletion
____3.2.3 Data Selection - Indexing and Condition-Based Selection
____3.2.4 Data Transformation - Creating Derived Variables and Applying Functions
____3.2.5 Data Exploration and Aggregation Operations
__3.3 Stock Price Data Exploration Practice
____3.3.1 Library Installation and Issues
____3.3.2 Stock Data EDA

Chapter 4.
Data visualization
__4.1.
How to visualize data
____4.1.1.
Python data visualization package
__4.2 seaborn
____4.2.1 Tips Dataset
____4.2.2 Scatterplot
____4.2.3 Regression line (regplot)
____4.2.4 Line graph (lineplot)
____4.2.5 Barplot
____4.2.6 Boxplot, Violinplot
____4.2.7 Histogram (histplot)
____4.2.8 Heatmap
__4.3 matplotlib
____4.3.1 Drawing a Graph
____4.3.2 Setting up the drawing paper
____4.3.3 Frequently Encountered Problems
__4.4 plotly
____4.4.1 Interactive Graphs
____4.4.2 Basic Graph Types
__4.5 Data Visualization Practice
____4.5.1 Visualization of Health Checkup Information Data

Chapter 5.
Data Analysis Dashboard
___5.1 Streamlit
____5.1.1 What is Streamlit?
____5.1.2 Basic UI Components
____5.1.3 Page Setup and Layout
____5.1.4 Data Display and Visualization
__5.2 Stock Analysis Dashboard
____5.2.1 Basic settings and library imports
____5.2.2 Importing Stock Data
____5.2.3 Company Information Display Function
____5.2.4 Stock chart generation function
____5.2.5 Volume chart generation function
____5.2.6 Technical Indicator Calculation Functions
____5.2.7 Main Application Function

This book is for readers who have encountered situations where they need to analyze data or have been asked to learn data analysis.

__More specifically, you can understand the core Python grammar (NumPy, Pandas) required for data analysis, or
__If you want to show a demo dashboard (Streamlit) directly using visualization methods (seaborn, matplotlib).

"Recently, companies are demanding full-stack capabilities that can directly implement analysis results into dashboards and turn them into services.
Accordingly, in this fully revised edition, we have added a new feature for dashboard development using Streamlit.”

* 51 video lectures (10 hours) provided via QR code
We provide video lectures via QR codes for areas requiring conceptual clarity, in-depth content, and practical tips.

* 50 problems to maximize learning effectiveness
We've included practice problems so you can immediately check what you've learned.

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The goal of this book is to provide readers with a foundation for taking their first steps in data analysis and growing through problem-solving on their own.
For those who want to get started right away, this will be a solid starting point, and for those who want to learn more in depth, it will serve as a stepping stone to the next level.

Q: With the advent of generative AI like ChatGPT, doesn't that mean data analysts have less work to do?
A: While we've entered an era where AI writes code, paradoxically, the ability to correctly understand and interpret data has become even more crucial. No matter how perfect AI code is, humans still have to decide what to analyze, how to interpret the results, and what decisions to make.
Rather, in the AI ​​era, the ability to understand the essence of data has become a key competitive advantage.

Q: What are the major changes in the revised edition?
A: Today's companies are demanding full-stack capabilities that go beyond simply performing analysis, enabling them to directly implement analysis results into dashboards and turn them into services.
Accordingly, we have added a new feature for dashboard development via Streamlit.
This book will enable you to not only analyze data, but also intuitively visualize and share your results.

Q: What is the connection between this book and "Machine Learning + Deep Learning with Python (Completely Revised Edition)"?
A: Volume 1 is a preparatory course for understanding and handling data, from Python basics to data exploration and dashboard creation. Volume 2 covers machine learning and deep learning algorithms, including creating data analysis models.
Volume 2 aims to enable even non-majors to create machine learning and deep learning models on their own. This book is designed for readers who want to work in AI, or who need to.

This book systematically covers the core Python grammar required for data analysis.
We've explained it using everyday terms and concepts so that even beginners can easily understand it.
We focused on conveying the essence of coding using familiar expressions rather than complex technical terms.
If you follow along step by step, anyone will be able to perform basic data analysis with Python.
Learning data analysis is like driving a car.
It is a process of acquiring basic knowledge and building skills through sufficient practice.
Just as you don't need to fully understand engine operation principles to drive, you don't need to master complex mathematical theories to analyze data.
The important thing is to just get started.
Even if it's awkward, you can experience data analysis yourself and supplement the theory as needed.
Approaching it this way significantly lowers the barrier to entry to advanced techniques like machine learning and deep learning.
Data analysis skills are a powerful competitive advantage in any industry.
It enables new business planning, improved efficiency of existing businesses, and even the discovery of innovative solutions.

1.
This book is an 'Introduction to Python Programming' for exploratory data analysis.
There are already many books on the market that explain the basics of Python and books that cover data analysis theory.
Most books cover a lot of the material Python has to offer in detail, making them a good choice for in-depth study.
However, a quick overview of the data analysis process requires a significant investment of time.
So I prepared this book.
This book covers only the core functions frequently used in data analysis among the numerous functions provided by Python.
To be precise, this explains the process of 'exploratory data analysis', which involves collecting, exploring, and refining data before proceeding with full-scale data analysis.
And we've structured it so that you can understand the overall process of exploratory data analysis by solving various practical examples.

2.
This book is optimized for readers who want to start data analysis with Python.
This book is optimized for readers who have just finished an introductory Python course, have mastered basic Python syntax, and are now moving on to data analysis.
This book will guide you through the basic Python you've learned so far, so you can quickly apply it to data analysis.
Therefore, it is not suitable for readers who want to learn (advanced use) in-depth numerical algorithms for data analysis, big data processing algorithms considering performance, and advanced Pandas syntax.
For readers with this goal, I recommend "Machine Learning + Deep Learning with Python, Completely Revised Edition" (2025, Iripo).

3.
If you are new to programming, I recommend reading the book in order.
This book sequentially explains Python basics (Chapter 2) - grammar for data analysis (Chapter 3: NumPy, Pandas) - visualization methods for data exploration (Chapter 4: seaborn, matplotlib) - how to create a dashboard to display data analysis results (Chapter 5: Streamlit).
Since each chapter assumes an understanding of the previous chapter, if you are new to programming, it is easier to understand the content by reading the book in order.

__Chapter 1: Setting up the development environment.
__Chapter 2: Learn basic Python syntax.

__Chapter 3: Learn various ways to load and modify previously saved data.

__Chapter 4: Learn visualization methods to easily check the distribution and trends of data by drawing various graphs.
__Chapter 5: Learn how to create a dashboard using Streamlit.

4.
If you are new to Python, you can start reading from Chapter 3.
If you have experience using Python for web programming or other purposes, you can quickly skip to Chapter 2, which covers basic Python syntax.
If you only have experience with programming languages ​​other than Python, I recommend skimming Chapter 2 as well.
Because Python was created with the goal of making existing programming languages ​​simple and concise, users who are familiar with traditional programming languages ​​such as Java or C may find the concise Python syntax awkward.
Even if you are a native Korean, abbreviations like ‘kebakke, ttaa, dapjeongneo’ feel awkward when you first see them.
So, we need to check how Python's syntax differs from other languages.

5.
Be sure to solve the practice problems and chapter quizzes on your own.
Programming languages ​​are simply a means of communication between computers and humans; what's really needed is to create logic to solve problems.
This book is structured to minimize explanations of theory and to encourage practical application of grammar.
Since it's your first time, it's natural that it's difficult and unfamiliar.
However, if you think about how to structure your logic and write code before looking at the solutions provided in the book, you will improve your skills much faster.