[Chapter 1 | Data Literacy: Speaking with Data]
1.1 What is Data Literacy? 012
__7 Characteristics of Data for Beginners to Data Literacy 013
Understanding the Competencies That Build Data Literacy 022
1.2 Data Analysis Use Cases for Businesses and Organizations 026

[Chapter 2 | Where to Find and How to Retrieve the Data You Need]
2.1 Using Public Data Available to Everyone 032
__Using Domestic Public Data 033
__Using Global Public Data 040
2.2 Collecting Webpage Data with Data Crawling 043
__Preparing Excel Power Query 043
Crawling music ranking data 044
2.3 Easily load big data with API 047
__Apply for API use for public data use 048
__Importing API Data into Excel 053

Chapter 3: Processing Data Using Excel Power Query
3.1 Data Preprocessing is More Important Than Analysis 060
__The Core of Data Preprocessing: ETL Automation 061
__Learning Excel Power Query 062
3.2 Integrating Data by Splitting Columns and Extracting Text 064
Merging Data with Sheet Integration 065
Splitting columns for easier analysis 070
__Extract only the desired text 072
Formatting and Saving Data 074
3.3 Merging sheets in different files 078
3.4 Using the Condition Column Feature to Combine Multiple Range Data 087
3.5 Changing the incorrect structure to column-wise cumulative data 095
__Change data rows/columns 095
__Add condition columns and merge columns 098
__Unpivoting and Splitting Columns 102
Aggregating with the Pivot Column Function 105

[Chapter 4 | Understanding Descriptive Statistics with Excel]
4.1 Statistics for Data Analysis 110
Statistics and Sampling 110
__Principles and Applications of Descriptive Statistics 114
4.2 Measuring Descriptive Statistics with Excel 122
__Preparing descriptive statistics tables and analyzing histograms for iron ore data 122
__Descriptive Statistics Results Analysis 128
4.3 Understanding Statistical Inference for Problem Solving 131
__The concept of statistical inference defined by various researchers 131
__Case 134 of Statistical Inference
__Statistical Inference and Problem Solving 135

[Chapter 5 | Techniques for Comparing Data and Identifying Significant Differences]
5.1 Z test and T test to test for significant differences in means 138
Building Preliminary Knowledge for the Z-Test and T-Test 138
__Practicing the Z-Test in Excel 140
__T Test Practice 1 146
__T Test Practice 2 152
5.2 Analysis of Variance Comparing Samples from More Than Three Groups 157
__One-way and two-way arrangements 158
__Analysis of Variance Practice 1 160
__Analysis of Variance Practice 2 169
5.3 Normalization and Standardization for Comparing Data with Different Characteristics 178
__Understanding the Meaning of Normalization and Standardization 178
__Normalization and Standardization Practice 1 180
__Normalization and Standardization Practice 2 189

[Chapter 6 | Correlation Analysis and Regression Analysis: Finding Relationships Between Variables]
6.1 Understanding Correlation Analysis Using Public Data 198
__Understanding the Types of Correlation Coefficients 198
__Correlation Analysis Practice Using Public Data 200
__Correlation and Causation 209
6.2 Regression Analysis: Estimating Other Variables Using One Variable 211
__Learning Regression Analysis 211
__Practicing Simple Regression Analysis with Excel Data Analysis Functions 213
__Understanding Terminology for Validating Regression Analysis Results 217
6.3 T-test and F-test for Simple Regression Analysis Verification 222
Significance Test by Regression Coefficient Using the __T Test 222
Diagnosing Statistical Significance of Regression Models Using the __F Test 225
__Practice Predicting the Future Using Regression Analysis 228
6.4 Multiple Regression Analysis with More than One Independent Variable 231
__Learning Multiple Regression Analysis 231
__Practicing Multiple Regression Analysis Using Excel 232
__Verifying Regression Analysis Results 236
6.5 T-test and F-test for multiple regression analysis validation 239
Significance Test by Regression Coefficient Using the __T Test 239
Diagnosing the Statistical Significance of Regression Models Using the __F Test 242
__Predicting the Future Using Multiple Regression Analysis 244

[Chapter 7 | Visualization for Exploratory Data Analysis]
7.1 Exploratory Data Analysis 248
What is Exploratory Data Analysis? 249
What is Confirmatory Data Analysis? 251
7.2 Adding a Chart for Data 254
__Bar chart suitable for ranking comparison 254
__Scatter chart 256 for easy correlation analysis
__Line chart to understand changes over time 257
__Map chart 259 for easy comparison by geographic location
__A pie chart that makes it easy to understand the proportion of 260
__Treemap chart 262 divided by square size

Data analysis is no problem as long as you have Excel.
Excel is an essential tool for office workers.
Yet, not many people know that Excel includes data analysis features.
In addition to the commonly used arithmetic operations and functions such as SUM and AVERAGE, there is a function called ‘Data Analysis’ that can be used in the [Data] tab.
This feature allows you to perform various data analyses, such as predicting the future, analyzing causes, and analyzing relationships using data.

You can learn basic statistical knowledge that forms the basis of data analysis.
Even if you obtain results using Excel's data analysis function, you cannot properly read and analyze the results without basic knowledge of statistics.
This book also explains the basic statistical knowledge that is essential for data literacy.

You'll also learn how to utilize public data, crawl it, and process it using Power Query.
You can learn step-by-step how to utilize public data, from the easiest to use, to data crawling using Excel, and data processing using Excel Power Query.
Next, you can systematically build the foundation for data analysis, including Excel data analysis functions and visualization functions using charts.

The first guidebook from Ableun, a data utilization education institute that even liberal arts students can use.
These days, even ordinary office workers who haven't majored in statistics or computers have to deal with data.
This is because leveraging data is no longer an option but a necessity, increasing the reliability of reports and plans and improving work and daily life.
Let's get started on utilizing data with the first guidebook from Ableun, which has already attracted over 10,000 trainees and 300 corporate clients to provide "Data Literacy for Non-Majors" training.


Target audience for this book
● Office workers who want to write reliable reports
● Planners who want to provide supporting data for new ideas
● Job seekers who want to develop essential skills in the big data era
● Office workers who want to utilize Excel Power Query