Recommendation
prolog

PART 1: Anyone can do AI without coding

Chapter 1: Three People Living in a Data World: Encountering the Unexpected Appearance of AI
01 Viewing the world of data in your own way
02 Data Scientist, Data Developer, Domain Technician
03 The Data World Transformed by AI - A New Era Awaits Us

Chapter 2: AI for the Humanities and the Working Class - No-Code Data Analysis: When Speech and Writing Meet AI
01 AI is created by science, but opportunities lie with liberal arts.
02 HR Manager - AI can complete peer evaluations that used to take a month in a day.
03 Publishing Editor - Planning Bestselling Publishing with AI
04 Fund Manager - From intuition to data-driven quantitative investment

Chapter 3: AI for Science Professionals - No-Code Data Analysis: When Domain Knowledge Meets AI
01 Everyone is an 'Application-X' in their own field.
02 Bio Developer - Image Classification Analysis that Teach AI How to See
03 Automotive Designer - Bringing the Experience of Veteran Designers to AI
04 Simulation Researcher - AI learns on its own, from simulation to optimization.

PART 2: Why Everyone Should Use AI

Chapter 4: AI and Data Analysis Orientation
01 Direction and Starting Point of AI Learning
02 You must use AI
03 AI - 10 No-Code Data Analysis Tips

Chapter 5 AI - AI for No-Code Data Analysis - Nosu-Sik Statistics
01 AI - Noh Su-sik Statistics - Statistics that look at the big world from a small world.
02 AI - Nosu-sik Probability - The probability of looking at the small world from the big world
03 AI - Nosushik Distribution - The distribution that most naturally explains past data
04 AI - Nosu-sik Trust - Trust that predicts future data from past data

Chapter 6 AI is not a tool, it's Jarvis!
01 AI that can do everything except what it can't do - Human Language (Natural Language)
02 AI that can do everything except what it can't do - Machine Language (Computer Code)
03 ChatGPT Advanced Data Analysis Settings Revealed - The difference before and after the settings is like heaven and earth!

PART 3: AI-No-Code Data Analysis Anyone Can Do

Chapter 7 Step 1 - Identifying the Problem: The One Thing People Do Better Than AI
01 Distinguishing between a problem and a phenomenon - The problem is not knowing what the problem is.
02 Problem Exploration - In school, you solve problems, but in practice, you have to create problems.
03 Problem Creation - In the AI ​​era, those who create problems will succeed.

Chapter 8 Step 2 - Preparing Data: What AI and People Need to Do Together
01 Data Upload - The First Step to Data Analysis
02 Data Preprocessing - Leave it to AI
03 Exploratory Data Analysis (EDA) - Understanding Data with AI
04 The Reality and Expectations of Practical Data Analysis - Underestimated Data Preprocessing

Chapter 9 Step 3 - Finding Patterns in Data: What AI Does Far Better Than Humans
01 AI - No-Code Visualization 1 - Spirit Did I draw this graph!
02 AI - No-Code Visualization 2 - Comparative Visualization
03 AI - No-Code Visualization 3 - Relationship Visualization
04 AI - No-Code Visualization 4 - Distribution Visualization
05 Good visualization can make even a whale dance.
06 Practical Data Analysis Techniques - Top 5 Most Useful Data Analysis Techniques
07 Rethinking - Things to Rethink in Data Analysis

References

The Future of Practical Data Analysis: ChatGPT and AI

Data analytics is no longer the domain of select experts.
This book is the result of the author's 13 years of experience as a simulation researcher at Hyundai Motor Group, solving data-based problems and developing AI solutions. The goal is to help both non-specialists and practitioners leverage AI and data without coding.

Through a revolutionary approach called AI-no-code analytics, we offer a way for anyone to leverage data to make decisions without needing complex coding or difficult statistics.
Using ChatGPT and no-code tools, we have presented concrete examples of how liberal arts professionals can achieve practical innovation by incorporating AI and data into their respective tasks, such as personnel evaluations and publication planning, while science professionals can achieve practical innovation by incorporating AI and data into their respective tasks, such as automobile design and biotechnology development.

The book's greatest strength is its step-by-step explanation of the data analysis process, where AI and humans collaborate to leverage each other's strengths.
This three-step approach, which harmoniously leverages human insight to uncover problems, human-AI collaboration to prepare data, and AI's strengths to identify patterns, provides readers with practical guidance they can immediately apply in their own work.

In an era where AI permeates every aspect of work, data analysis is no longer a difficult skill but a daily routine anyone can do.
This book will serve as a solid guide for anyone preparing for the future ushered in by AI and no-code data analytics.

I recommend this to these people!

● Office workers who need data analysis but are afraid of coding!
● Professionals who want to improve work efficiency by utilizing AI and data analysis!
● Non-major students who want to develop data analysis skills!

What you'll get from this book

● Practical tips for analyzing data without coding using ChatGPT and no-code tools
● How to make data-driven decisions in your field, regardless of whether you are in the humanities or sciences.
● Step-by-step analysis know-how that collaborates with AI, from problem discovery to data preparation and pattern analysis.

As AI transforms everything, break the stereotype that "data analysis is difficult" and learn how anyone can work with data and AI!

"ChatGPT No-Code Data Analysis for Real-World Practice" will accompany you on your data journey!