A data mindset that goes beyond formulas
 |
Description
Book Introduction
In an era where every worker needs data utilization skills, there's an abundance of books and lectures that teach this.
But will running a few lines of Python code for a few hours really make you good at data analysis?
To effectively utilize data, it is not enough to learn analysis programs or statistics, or even obtain a degree.
As a product manager and data scientist with years of experience working with data, I thought, "It would be great to have a book that organizes all of this in one place." I've compiled this book with practical know-how.
This book is a practical, hands-on guide to data analysis, step-by-step, through the six stages of data analysis for non-specialists and non-experts.
We've also compiled a data terminology white paper for non-specialists and a separate appendix on SQL and basic statistical concepts so you can refer to them at any time.
But will running a few lines of Python code for a few hours really make you good at data analysis?
To effectively utilize data, it is not enough to learn analysis programs or statistics, or even obtain a degree.
As a product manager and data scientist with years of experience working with data, I thought, "It would be great to have a book that organizes all of this in one place." I've compiled this book with practical know-how.
This book is a practical, hands-on guide to data analysis, step-by-step, through the six stages of data analysis for non-specialists and non-experts.
We've also compiled a data terminology white paper for non-specialists and a separate appendix on SQL and basic statistical concepts so you can refer to them at any time.
- You can preview some of the book's contents.
Preview
index
Introduction
Recommendation
Chapter 1.
An era where everyone analyzes data
1.1 The Future of Data Analysts
1.2 What kind of data analysis does our company need?
1.3 Practical examples of data analysis
1.4 Classification of data occupations
1.5 Data Analysis for Non-Data Occupations
1.6 Why Everyone Needs Data
Chapter 2.
Incorporate perspectives, not formulas
2.1 Why neither statistical knowledge nor hard skills are important for data analysis
2.2 Starting Data Analysis
2.3 Define the business problem you want to solve with data analysis.
2.4 The Path to a Data Mindset
Chapter 3.
Pay attention to key indicators
3.1 I created a monster with my own hands!
3.2 What is a good indicator?
3.3 Data Core Terminology White Paper Concept Encyclopedia
3.4 Data that cannot lead to action is garbage.
3.5 The Paradox of 'Countless Indicators'
Chapter 4.
6 Steps to Real-World Data Analysis
4.1 Checking available data
4.2 Defining the Most Important Indicator: North Star Indicator, OMTM
4.3 Find the real indicator
4.4 Defining the most appropriate data cycle
4.5 Defining the Comparison Criteria
4.6 Extracting Data: Why Should You Know SQL?
4.7 Multiple Frameworks for Analyzing Data
4.8 Reflecting and Improving
4.9 Data must be honest.
4.10 Now the growth begins
Chapter 5.
How to Collaborate with Data Professionals
5.1 Three Major Data Jobs: Data Analyst, Scientist, and Engineer
5.2 How Data Organizations Collaborate
5.3 Where will the work I requested go?
5.4 How to Work Effectively with Your Data Team
5.5 A Future Where We Will Work More and More Frequently with Data Professionals
Special appendix
1.
SQL Basics for Non-Majors
2.
Basic Statistics for Non-Majors
Conclusion
Recommendation
Chapter 1.
An era where everyone analyzes data
1.1 The Future of Data Analysts
1.2 What kind of data analysis does our company need?
1.3 Practical examples of data analysis
1.4 Classification of data occupations
1.5 Data Analysis for Non-Data Occupations
1.6 Why Everyone Needs Data
Chapter 2.
Incorporate perspectives, not formulas
2.1 Why neither statistical knowledge nor hard skills are important for data analysis
2.2 Starting Data Analysis
2.3 Define the business problem you want to solve with data analysis.
2.4 The Path to a Data Mindset
Chapter 3.
Pay attention to key indicators
3.1 I created a monster with my own hands!
3.2 What is a good indicator?
3.3 Data Core Terminology White Paper Concept Encyclopedia
3.4 Data that cannot lead to action is garbage.
3.5 The Paradox of 'Countless Indicators'
Chapter 4.
6 Steps to Real-World Data Analysis
4.1 Checking available data
4.2 Defining the Most Important Indicator: North Star Indicator, OMTM
4.3 Find the real indicator
4.4 Defining the most appropriate data cycle
4.5 Defining the Comparison Criteria
4.6 Extracting Data: Why Should You Know SQL?
4.7 Multiple Frameworks for Analyzing Data
4.8 Reflecting and Improving
4.9 Data must be honest.
4.10 Now the growth begins
Chapter 5.
How to Collaborate with Data Professionals
5.1 Three Major Data Jobs: Data Analyst, Scientist, and Engineer
5.2 How Data Organizations Collaborate
5.3 Where will the work I requested go?
5.4 How to Work Effectively with Your Data Team
5.5 A Future Where We Will Work More and More Frequently with Data Professionals
Special appendix
1.
SQL Basics for Non-Majors
2.
Basic Statistics for Non-Majors
Conclusion
Detailed image
GOODS SPECIFICS
- Publication date: November 24, 2023
- Page count, weight, size: 240 pages | 148*210*20mm
- ISBN13: 9791165922542
- ISBN10: 1165922541
You may also like
카테고리
korean
korean
![GQ KOREA Mark (Monthly): December [2025]](http://librairie.coreenne.fr/cdn/shop/files/8ef265dbbfbf186523ed75ba7319009d.jpg?v=1765340328&width=3840)
