Recommendation
preface

1.
Framework for Data Science

1.1.
Why Data Science?
1.1.1.
What is data science?
1.1.2.
The Path of a Data Scientist
1.1.3.
The Importance of Learning Data Science "Without Coding"

1.2.
Basic knowledge for data science
1.2.1.
data
1.2.2.
variable

1.3.
Frameworks for Data Science
1.3.1.
Classification by theoretical background
1.3.2.
Classification by analysis purpose
1.3.3.
Classification by dependent variable
1.3.4.
Classification by number of variables

2.
Tools for Data Science

2.1.
Tools for Data Science
2.1.1.
What are tools for data science?
2.1.2.
open source software
2.1.3.
commercial software

2.2.
Why SAS ODA?
2.2.1. Background of SAS ODA Service Provision
2.2.2. Reasons for adopting SAS ODA

2.3. Using SAS ODA
2.3.1.
Recommended System Environment
2.3.2.
Sign up for the service

2.4. Touring SAS Studio
2.4.1.
Basic configuration
2.4.2.
Work mode
2.4.3.
Process Flow

2.5. SAS Studio Preview
2.5.1.
Create a folder
2.5.2.
Creating a library
2.5.3.
Tasks and Utilities

3.
Data processing and handling

3.1.
Why is data processing important?
3.1.1.
Tools for Ensuring Reliability
3.1.2.
Tools for handling big data
3.1.3.
This is a great analysis tool
3.1.4.
Introduction to example data

3.2.
Data input and output
3.2.1.
File upload
3.2.2.
Check the data
3.2.3.
Export data
3.2.4.
Download file

3.3.
Data Processing Basics
3.3.1.
vaginal
3.3.2.
Query output
3.3.3.
Select column
3.3.4.
Column Filter
3.3.5.
Column Summary
3.3.6.
Column alignment

3.4.
Data structure transformation
3.4.1.
Table join
3.4.2.
Table linking
3.4.3.
Data transposition

4.
Technical data analysis

4.1.
What is technical data analysis?
4.1.1.
The Sound of Data
4.1.2.
Types of Technical Data Analysis
4.1.3.
The Need for Technical Data Analysis

4.2.
Numerical data analysis
4.2.1.
What is numeric data analysis?
4.2.2.
location
4.2.3.
Volatility
4.2.4.
shape

4.3.
Categorical data analysis
4.3.1.
What is categorical data analysis?
4.3.2.
One-way frequency analysis
4.3.3.
Missing rate analysis

4.4.
Data conversion
4.4.1.
Data ranking
4.4.2.
Recoding values
4.4.3.
Range recoding
4.4.4.
Data conversion
4.4.5.
Data standardization

5.
Visual Data Analysis I

5.1.
Visual data analysis
5.1.1.
What is visual data analysis?
5.1.2.
Common tasks

5.2.
one-dimensional graph
5.2.1.
bar graph
5.2.2.
Circle graph
5.2.3.
Histogram
5.2.4.
box plot

5.3.
2D graph
5.3.1.
Scatterplot
5.3.2.
heat map
5.3.3.
Time series plot
5.3.4.
mosaic diagram

5.4.
multidimensional graph
5.4.1.
line graph
5.4.2.
bar-line graph
5.4.3.
bubble chart

6.
Visual Data Analysis II

6.1.
What is a map graph?
6.1.1.
What is a map graph?
6.1.2.
Latitude and longitude
6.1.3.
map data
6.1.4.
Types of maps
6.1.5.
Types of map graphs

6.2.
Location Map
6.2.1.
Create latitude and longitude data
6.2.2.
Bubble Map
6.2.3.
Scatter map
6.2.4.
Time series map

6.3.
Regional map
6.3.1.
Choropleth area map
6.3.2.
Text map

Conclusion

The emergence of no-coding analysis software brought about by the advancement of artificial intelligence!
Now anyone can become a data scientist!

With the advancement of artificial intelligence, coding is becoming a tool rather than a necessity.
Therefore, rather than learning the grammar of programming languages ​​like Python or R while studying data, it is now possible to understand the framework of data science using well-made tools based on artificial intelligence technology and immediately apply data analysis techniques in practice.
This book emphasizes that what matters when approaching data science is not the programming language or individual analysis methodology.
The most important thing in data science is to correctly understand social phenomena and problems and choose which analytical methodology to use to solve them.
If you encounter problems while coding a programming language or applying a specific analysis methodology, you can solve them by utilizing the vast amount of information available on the Internet or in existing books.
However, even if you acquire various languages, if you misunderstand the problem and adopt the wrong analysis methodology, it will become an irreversible mistake.
In other words, establishing a framework for data science is more important than anything else.

This book is for beginners in data science, and those without a background in statistics or computer engineering.
It aims to serve as a guide for beginners who do not have a theoretical foundation in computing to learn data science.
Therefore, with just this one book, you can easily understand data science, and anyone can become a data scientist and make excellent decisions.
In this age of data overload, let's embark on a new journey into the world of data science.