=======================
01 Growing into a Data Analyst
=======================

01-1 Becoming a Competitive Data Analyst
01-2 Creating a Data Analysis Environment
01-3 Running an R script
01-4 Overview of the Project Implementation Process

==========================
02 Things to know before collecting data
==========================

02-1 Where can I get the data?
02-2 Obtaining an API authentication key
02-3 Requesting data from the API
02-4 Checking API Response

=========================
03 Data Collection: Creating an API Crawler
=========================

03-1 Preparing for Crawling: What to Prepare?
03-2 Creating a Request List: How to Request Materials?
03-3 Building a Crawler: Automatically Collecting Data
03-4 Data Organization: Integrating Data

=====================
04 Preprocessing: Prepare the data appropriately
=====================

04-1 Delete unnecessary information
04-2 Item-by-item data trimming
04-3 Saving preprocessed data

==========================
05 Geocoding with Kakao Map API
==========================

05-1 Preparing for Geocoding
05-2 Geocoding to convert addresses into coordinates

=======================
06 Creating a Geodata Frame
=======================

06-1 Coordinate System and Geodata Format
06-2 Combining addresses and coordinates
06-3 Creating a Geo Data Frame

==========================
07 Visualizing the Analysis Topic on a Map
==========================

07-1 Which area is the most expensive?
07-2 What are the hottest areas these days?
07-3 Is our neighborhood more expensive than the neighborhood next door?

=================
08 Statistical Analysis and Visualization
=================

08-1 Extracting only region of interest data
08-2 Probability Density Function: Are apartments in this area expensive?
08-3 Regression Analysis: How much will this area rise in a year?
08-4 Principal Component Analysis: What are the characteristics of each complex in this neighborhood?

===============
09 Getting Started with SHINee
===============

09-1 First meeting with SHINee
09-2 Input and Output
09-3 Building a Responsive Web Application
09-4 Defining the Layout

==============================
10 Developing Data Analysis Applications
==============================

10-1 Creating a Responsive Map
10-2 Creating a Map Application
10-3 Completing the Responsive Map Application
10-4 Creating a Seoul City Apartment Transaction Application

====================
11 Deploying the Application
====================

11-1 Preparing for Distribution
11-2 Deploying to Shiny Cloud
11-3 Using the Application

==========================
12 Use Cases of Shiny Applications
==========================

12-1 Analyzing Apartment Price Correlation
12-2 Comparing correlations across regions
12-3 Analyzing Earthquake Occurrence
12-4 Analyzing Accessibility to Coffee Shops

======================
[Introducing the Contest Winners]
Transportation Card Data Analysis Case Study
======================

1.
Introduction to the 'Optimal City Bus Route Proposal' Project
2.
Data Preprocessing 1: Regional Information
3.
Data Preprocessing 2: Transportation Card Data
4.
Basic Analysis 1: Usage by Route and Time Zone
5.
Basic Analysis 2: Mobility Characteristics by Aggregation District
6.
Traffic Flow Analysis 1: Commute Hours
7.
Traffic Flow Analysis 2: Non-Commute Hours
8.
Comprehensive analysis
Search

==========
Features of this book
==========

- You can immediately apply the experience gained through practical training in projects that have won actual competition awards or been selected for national projects to prepare for competitions or in your own work.
- You can naturally learn essential R packages and various analysis techniques by practicing the entire process of a data analysis project.
- It will help you increase your competitiveness by covering not only data analysis and visualization, but also how to build and deploy web applications with Shiny.
- The entire practical course is provided as a video lecture with the author (updated sequentially).
- We provide a 15-lesson lecture progress chart to help you create a lecture plan or help self-students make plans and check their progress.

- Data analysis experts and beta testers have verified the entire course and source code with the latest versions of development tools.

▶ Learn data analysis methods that drive performance in the big data era!
This book develops your ability to read and understand data by teaching you the entire process of analyzing data step by step.
We cover the entire process in detail, from collection and preprocessing to statistical analysis, visualization, web application development, and deployment, so you can develop data analysis skills at the level required in the field.

▶ Learn through 'high-level projects' selected for contests and national support projects!
The 'Apartment Transaction Analysis' practiced in this book was selected for the Ministry of SMEs and Startups' preliminary startup package, and 'Apartment Correlation Analysis' was selected for the SH New Researcher Support Project.
Additionally, the 'Optimal Bus Route Proposal' project is the winner of the LH Data Analysis Competition.
Gain a competitive edge with industry-recognized, high-quality project experience.

▶ Learn about topics closely related to everyday life, such as housing prices, transportation, and commercial district analysis, in a 'fun' way!
We cover topics closely related to daily life, such as actual apartment transactions, earthquake occurrences, coffee shop locations, and public transportation usage, to stimulate interest and motivate learning.
You'll see how data analytics can be used to answer questions like, "Are apartments in this area expensive?", "What are the characteristics of each apartment complex?", "What coffee shops are near the subway station?", and "What's the best bus route for my current needs?"


▶ Learn various ways to use public data!
We'll show you how to leverage publicly available data, from automatically collecting public data to marking specific locations on a map with the Kakao Map API.
This provides solid experience in preparing for competitions utilizing public data, applying it to work, or creating new business models.

▶ Learn geocoding, essential for spatial data analysis!
This article covers in detail the geocoding technique of obtaining latitude and longitude coordinates from proper nouns such as addresses and place names.
We cover everything in detail, from the concept of the coordinate system to how to obtain coordinates using the Kakao Map API and how to display these coordinates on a map.
It provides valuable experience to anyone who wants to analyze map-based spatial data.

▶ Learn how to commercialize the results you analyze and visualize!
Going forward, data analysts will need to be able to implement their analysis results into applications and share them with others to remain competitive.
Shiny, provided as an R package, allows you to create web applications from data analysis results without having to learn a separate web development language.
This book introduces the method and process in detail.
Distribute your analysis so that it can be viewed on any device that runs a web browser.

▶ Learn 'thoroughly' with an easy-to-read book!
The exercises in this book include comments and speech bubbles for each major piece of code to help you analyze it.
We also provide helpful explanations of the execution results and links to view the application results directly online.
In "Good to Know!", we present problems and solutions frequently encountered in data analysis, and in "Regular Code Summary," you can check out coding patterns that frequently appear in data analysis.


==============
Target audience for this book
==============

This book guides you through all the exercises step-by-step, so even if you lack coding experience, you can easily follow along and see the results.
You can further enhance your application skills by studying it together with an introductory book on R or statistics.
- Students or job seekers hoping to become data analysts
- People who want to experience the entire process of actual data analysis after reading the R introductory book.
- People who want to apply data analysis in competition preparation or in practice
- People who want to commercialize data analysis results or share them with others

==============
Practice environment for this book
==============

The source code for this book has been tested and works fine on Windows and macOS.
If there are any updates to R or packages, you can check the latest news at the Do it! Study Room (cafe.naver.com/doitstudyroom).
- R version 4.1.2 (2021-11-01) -- "Bird Hippie"
- RStudio 2021.09.1+372 "Ghost Orchid" Release