1 **Data Science**

1-0 How to proceed with data utilization

1-0-1 Data Science Jobs

1-0-1-1 Data Analysis Jobs

1-0-1-2 Data Analysis for Bad Data

1-0-1-3 Image of Dark Data

1-0-2 Mathematical Science in Data Science

1-0-3 Softwares of Data Science

1-0-4 Isolation of Data, Methods and Indexes

1-0-5 Impossibility of data science

*Find From Data (1)*

1-1 **Statistics**

1-1-0 Strength and Weakness of Big Data

1-1-1 Normal Distribution and Others

1-1-1-1 Made by Normal Distribution But Not Normal Distribution

1-1-1-2 Extreme Value Statistics

1-1-1-3 Chebyshev's inequality

1-1-1-4 Proportional variance

1-1-2 Statistical Value

1-1-2-1 Average and Median

1-1-2-2 Standard Deviation

1-1-2-2-1 Standard Error

1-1-2-3 Unbiased Variance

1-1-3 Hypothesis Testing

1-1-3-0 Type I error and Type II error

1-1-3-1 Hypothesis Testing for Diffrence of Average

1-1-3-1-0 Reinforcement the test in the meaning of difference

1-1-3-1-1 ANOVA

1-1-3-1-1-1 ANOSS

1-1-3-1-2 Paired t-test

1-1-3-1-2-1 Testing for distribution deviation

1-1-3-1-3 Test for Normal Distribution Differences

1-1-3-1-4 Rrelationship between the difference in true means and the analysis method

1-1-3-1-5 Effect size of small data test

1-1-3-2 Hypothesis Testing for Diffrence of Dispersion

1-1-3-2-1 Relationship between dispersion ratio, p-value, and number of samples

1-1-3-2-2 Test for differences in normal distribution variability

1-1-3-2-3 Assessing differences in variation for small data

1-1-3-3 Hypothesis Testing for Difference of Ratio

1-1-3-3-1 Test for differences in Proportional variances

1-1-3-4 Hypothesis Testing from 21 century

1-1-4 Estimation

1-1-4-1 Confidence intervals and Credible intervals

1-1-4-1-1 From Estimation to Hypothesis Testing

1-1-4-2 Prediction intervals

1-1-5 Information Theory

1-1-5-1 Information Statistical Mechanics

1-1-6 Bayesian Statistics

1-1-6-1 Generative Model and Discriminative Model

1-1-6-2 Hierarchical Bayes Model

1-1-7 Computational Statistics

*Find From Data (2)*

1-2 **Multi-Variable Analysis**

1-2-0 Homogeneity Analysis

1-2-1 Regression Analysis

1-2-1-0 Scatter Plot and Correlation

1-2-1-0-1 Faults When We Find Correlation

1-2-1-0-2 Application of Suspected Correlation

1-2-1-1 Single Regression Analysis

1-2-1-1-1 Regression Analysis for Curve

1-2-1-1-2 Prediction Interval of Regression Analysis

1-2-1-1-3 Measurement Errors in Regression Analysis

1-2-1-1-4 Regression analysis of Proportional variance

1-2-1-1-4-1 Prediction interval of Proportional variance

1-2-1-1-5 Additive model and multiplicative model

1-2-1-2 Multi-Regression Analysis

1-2-1-2-0 Multi vs Single

1-2-1-2-1 Multicollinearity

1-2-1-2-1-1 Principal Component Regression Analysis

1-2-1-2-2 Selection of Variables

1-2-1-2-2-1 Sparce Modeling

1-2-1-2-3 Linearity

1-2-1-2-4 Partial Correlation

1-2-1-2-5 Evaluation of Variable Importance

1-2-1-2-6 Types of interactions

1-2-1-2-6-1 Force of Interaction term

1-2-1-2-6-2 Analysis of interactions

1-2-1-3 Path Analysis

1-2-1-4 Generalized Linear Mixed Model

1-2-1-4-1 Interval High-dimensional regression analysis

1-2-1-4-2 Linear mixed model of Proportional variance

1-2-1-5 Gaussian Process Regression Model

1-2-1-6 Spline

1-2-2 Label Classification

1-2-2-0 Confusion Matrix

1-2-2-0-1 ROC Curve and AUC

1-2-2-1 Discriminant Analysis

1-2-2-2 Logistic Regression Analysis

1-2-2-3 Support Vector Machine

1-2-2-3-1 Kernel Method

1-2-2-3-2 One-Class SVM

1-2-2-4 MT method

1-2-2-4-1 Unit Space

1-2-2-4-2 Mahalanobis' Distance

1-2-2-4-2-1 Difference between MT method and Hotering theory

1-2-2-4-3 Process of MT method

1-2-2-4-3-1 Aprroach for Chance of MT

1-2-2-4-3-2 Principal Component MT

1-2-2-4-3-3 Kernel Principal Component MT

1-2-2-4-3-4 Mixture distribution MT

1-2-2-4-4 Decision of Abnormal by MT method

1-2-2-4-5 Analysis of the cause of abnormalities by MT method

1-2-2-5 Difference of Good Distribution by Methods

1-2-2-6 Difference of Output by Methods

1-2-3 Analyzing variable companioning

1-2-3-1 Correlation Analysis for Multi-Variable

1-2-3-1-1 Graphical Lasso

1-2-3-1-2 LiNGAM

1-2-3-1-2-1 Distribution for which LiNGAM is effective

1-2-3-1-2-2 Why LiNGAM is valid with normal distribution

1-2-3-1-2-3 Limit of LiNGAM

1-2-3-1-2-4 Relationship analysis of variables with LiNGAM

1-2-3-2 Principal Component Analysis

1-2-3-2-1 Canonical Correlation Analysis

1-2-3-3 Contingency Table

1-2-3-3-0 Correlation of Qualitative Variable

1-2-3-3-1 Independence Test

1-2-3-3-2 Log-linear analysis

1-2-4 Factor Analysis

1-2-4-1 SEM and Covariance Structure Analysis

1-2-4-2 Independent Component Analysis

1-2-4-3 Item Response Theory

*Find From Data (3)*

1-3 **Data Mining**

1-3-0 EDA(Exploratory Data Analysis)

1-3-1 Decision Tree

1-3-1-1 Classification tree and regression tree

1-3-1-2 N-try tree

1-3-1-3 Random forest

1-3-1-4 Model tree

1-3-2 Analysis of individual category grouping

1-3-2-1 Associations Analysis

1-3-2-2 Correlation analysis of individual categories

1-3-2-3 Rough Sets Analysis

1-3-3 Analysis of Similarity of Samples

1-3-3-1 Visualization by compressing high dimensions into two dimensions

1-3-3-1-1 Multi Dimensional Scaling

1-3-3-1-1-1 Networked multidimensional scaling

1-3-3-1-2 Self Organizing Map

1-3-3-1-3 With Regression Analysis

1-3-3-1-4 With Canonical Correlation Analysis

1-3-3-2 Cluster Analysis

1-3-3-2-1 Cluster analysis using 2-Dimension Scatter Plot

1-3-3-2-2 Analysis of clustering by decision tree

1-3-3-2-3 Outlier detection with cluster analysis

1-3-3-2-4 Prediction by cluster analysis

1-3-3-2-4-1 Analyzing Cluster Predictions

1-3-3-2-4-2 Vector quantization label classification

1-3-3-3 Outlier Detection

1-3-4 Analysis of Many vs Many

1-3-4-1 AA type analysis

1-3-4-1-1 Network Analysis

1-3-4-1-2 Eigenvalue Analysis

1-3-4-2 AB type analysis

1-3-4-2-1 Cross Tabulation

1-3-4-2-2 Matrix Decomposition

1-3-4-2-3 Q-Analysis

1-3-4-2-4 Correspondence analysis

1-3-4-2-4-1 Multidimensional simultaneous attachment diagram and bipartite graph

1-3-4-3 Many-to-many-to-many analysis (ABC type analysis)

1-3-5 Neighboring method

1-3-5-1 k-NN

1-3-5-2 LOF

1-3-5-3 One-Class Minimum distance method

1-3-6 Quantification theory

1-3-6-1 Broadly defined Quantification theory 1

1-3-6-2 Broadly defined Quantification theory 2

1-3-6-3 Broadly defined Quantification theory 3

1-3-7 Text Mining

1-3-7-1 From Sentences to Words

1-3-7-1-1 Word Cloud

1-3-7-2 Software of Text Mining

1-3-7-3 Without Dictionary

1-3-7-4 Co-occurrence

1-3-7-5 Analyze Unit of Text

*Find From Data (4)*

1-4**Graphical Statics**

1-4-0 Software of Graphical Statics

1-4-1 Stratified Graph

1-4-1-1 Bar Plot

1-4-2 Graphs of 1-Dimension Disribution

1-4-2-1 1-Dimension Scatter Plot

1-4-2-2 Histogram

1-4-2-3 Box Plot

1-4-3 Graph for Change

1-4-3-1 Line Graph

1-4-3-2 Heat Map

1-4-4 Graphs for 2-Dimension Distribution and Relationship

1-4-4-1 2-Dimension Scatter Plot

1-4-4-1-1 Scatter Plot of Words

1-4-4-2 Graph of Network

1-4-4-2-1 Bipartite graph

1-4-4-2-2 ISM and DEMATEL

1-4-5 Graph for 3-Dimension

*Link and Arrange Data*

1-5 **Data Literacy**

1-5-0 Linking different source data

1-5-1 Database

1-5-2 Programming

1-5-2-1 Reading and Writing Programs

1-5-2-2 Program Languages

1-5-3 Feature Engineering

1-5-3-1 Probability and Random Variable Transformation

1-5-3-1-1 Logit Transformation and Probit Transformation

1-5-3-1-2 Odds Ratio

1-5-3-2 Transformation Quantity and Quality

1-5-3-2-1 Dummy Variable

1-5-3-2-1-1 Binary Number Transformation

1-5-3-2-2 Fuzzy Theory

1-5-3-2-3 Converting a group of binary variables into a single continuous variable

1-5-3-2-4 Converting a group of qualitative variables into one continuous variable

1-5-3-3 Standardization and Normalization

1-5-3-3-1 Standardization and Normalization with PCA

1-5-3-4 Analysis Using Intermediate Layer

1-5-3-5 Analysis Using Category Data

1-5-3-5-1 One-dimensional clustering

1-5-3-5-2 Vector quantization

1-5-3-6 Differentiation Data and Integration Data

1-5-3-6-1 Velocity Data (Difference Data)

1-5-3-6-2 Numerical Integration

1-5-3-7 Fourier Transformation and Laplace Transformation

1-5-3-7-1 Spectrum Analysis

1-5-3-8 Treatment of Time

1-5-4 Data Physics

1-5-4-1 Data of Physics

1-5-4-2 Data of Time and Space

1-5-4-3 High dimensional data

1-5-4-4 Money data

1-5-5 Meta Data and Meta Knowledge

1-5-6 Outlier and Missing Value

1-5-6-1 Cause and Effect Analysis of Outlier and Missing Value

1-5-6-2 Analysis with Outlier and Missing Value

1-5-7 Effective dimension number

*Predict Data*

1-6 **Prediction and Simulation**

1-6-1 Prediction by Statistical Model

1-6-1-1 Over Fitting

1-6-1-2 Extrapolation

1-6-1-3 Time Depended Extrapolation

1-6-1-4 Software for Prediction

1-6-1-5 Gap between Models of Statistics and Real

1-6-1-6 Difficulty of Complicated Models

1-6-1-7 Robust Analysis

1-6-2 Mathematical Modeling

1-6-2-1 Dimension Analysis

1-6-2-2 Addition Model and Division Model

1-6-2-3 Probability Model and Deterministic Model

1-6-2-3-1 Mathematical Programming

1-6-2-3-2 Genetic Algorithm

1-6-2-4 Differential Equation Model

1-6-2-4-1 System Dynamics

1-6-3 Dispersion Model

1-6-3-1 Make Dispersion Data

1-6-3-2 Random Walk Model

1-6-4 Model of Abnormal

1-6-4-1 Model of Outlier

1-6-4-2 One-Class Model

1-6-4-3 Model of Abnormal but not Outlier

1-6-4-3-1 Outliers in time-series data

1-6-4-3-2 Residual Outliers

*Collect Data*

1-7 **Measurement**

1-7-0 Distinguishing and using data

1-7-1 Errors

1-7-1-1 Gauge R and R

1-7-1-2 Causes of Errors

1-7-1-3 Propagation of Errors

1-7-1-4 Errors and Sample Number

1-7-1-5 Use of Double Measured Data

1-7-1-5-1 Estimation of Repeatability by Double Measured Data

1-7-1-5-2 Use Double Measured Data for Pattern Recognition

1-7-2 Design of Experiments

1-7-2-1 Sampling of Experiment Data

1-7-2-1-1 Location Experiment

1-7-2-1-2 Interaction

1-7-2-1-3 Orthogonal Array

2-2-3-1-4-2 Mixed Orthogonal Array

1-7-2-1-4 Use of Category Data and Quantity Data

1-7-2-2 Analysis of Experiment Data

1-7-2-2-1 Response Surface Method

1-7-2-2-2 Think Output of Experiment

1-7-3 Sampling

1-7-3-1 Stratified Sampling

1-7-4 Significant Figures and Resolution

1-7-4-1 Errors by Significant Figures and Resolution

1-7-4-2 Statistics with minimum confidence interval (Impossibility of statistics)

1-7-4-3 Estimation of the capacity required for the measurement system (Estimation of significant figures and resolution)

1-7-5 Questionnaire

1-7-6 Scaling Method

1-7-6-1 Pair evaluation by ratio or difference

1-7-7 Ferimi Estimation

1-7-8 Measure Theory and Data Science

*Think With Data*

1-8 **Artificial Intelligence**

1-8-1 Expert System

1-8-1-1 Ontology

1-8-2 Machine Learning

1-8-2-1 Supervised and Non-Supervised Learning

1-8-2-2 Reinforcement Learning

1-8-2-3 Ensemble Learning

1-8-2-4 Batch learning and Online learning

1-8-2-5 AI explainability and interpretability (XAI)

1-8-2-6 AutoML (Automated Machine Learning)

1-8-3 Bayesian Network

1-8-3-1 Calculation of Probability by Bayesian Network

1-8-3-2 Structure Analysis by Bayesian Network

1-8-3-2-1 Differences of Bayesian network algorithm

1-8-3-3 Pattern Recognition by Bayesian Network

1-8-4 Neural Network

1-8-4-1 Deep Learning

1-8-4-2 Auto Encoder

1-8-4-3 Convolutional Neural Network (CNN)

1-8-4-4 Recurrent Neural Network (RNN)

1-8-5 Cognition of Graphics and Voices

1-8-6 Autocomposition

1-8-7 Artificial Mind

*Find From Data (5)*

1-9**Analysis of Real World**

1-9-1 Cause and Effect Analysis

1-9-1-1 Quality Way of Making Hypothesis

1-9-1-1-1 Kinds of Cause and Effect (AND, OR)

1-9-1-1-2 Types of basic principles of causality

1-9-1-1-3 Logical Inference

1-9-1-1-4 Logical Thinking

1-9-1-1-5 MECE

1-9-1-1-5-1 MECE Framework

1-9-1-2 Quantity Way of Making Hypothesis

1-9-1-2-1 Making Hypothesis by Correlation

1-9-1-2-1-1 Search for hidden variables

1-9-1-2-2 Structure of data that becomes a directed graph

1-9-1-2-2-1 If-Then data structure

1-9-1-2-2-2 Conditionally independent data structure

1-9-1-2-2-3 Regression model data structure

1-9-1-2-2-4 Proportional variance model data structure

1-9-1-2-3 Time Difference of Cause-Effect

1-9-1-2-4 Cause-Effect Analysis of Indivisual Samples

1-9-1-3 Analysis of Proof

1-9-2 System Theory

1-9-2-1 Systems Thinking

1-9-2-1-1 Two major structures of the system (Tree structure and Network structure)

1-9-2-1-2 Concept Analysis

1-9-2-2 Direct problem and Inverse problem

1-9-2-3 System Thoughts

1-9-2-3-1 Analogy

1-9-2-3-2 Balance

1-9-2-3-3 Diversity

1-9-2-4 Mathematical Models for System

1-9-2-4-1 Control Engineering

1-9-2-4-1-1 Kalman Filter

1-9-2-4-1-2 Sequential Control

1-9-2-4-2 Category Theory

1-9-2-4-3 Group Theory

1-9-2-5 Systems Engineering

1-9-2-5-1 System Requirements Definition

1-9-3 Time Series Analysis

1-9-3-1 Condition Analysis

1-9-3-2 Time Analysis

1-9-3-2-1 Survival Analysis

1-9-3-3 Passing Analysis

1-9-3-3-1 Trend Analysis for Many Variables

1-9-3-3-2 Trend analysis with small data

1-9-3-3-3 Reverse Time Aggregation

1-9-3-4 Self Correlation Analysis

1-9-3-4-1 Single Self Correlation Analysis

1-9-3-4-2 AR Model and Others

1-9-3-5 Moving Analysis

1-9-3-5-1 Moving Window without Overlap

1-9-3-5-2 0-1 Data Analysis

1-9-3-6 Quasi-periodic Data Analysis

1-9-3-6-1 Sampling from Database

1-9-3-6-2 Analysis of Type 1

1-9-3-6-3 Analysis of Type 1.5

1-9-3-6-4 Analysis of Type 2 (Feature Data)

1-9-3-6-5 Analysis of Type 3

1-9-3-7 Point Process Analysis

1-9-3-7-1 Poisson Process

2 **Environment and Quality**

2-1 **Environment**

2-1-0 Environmental Problems

2-1-1 *Natural Environment*

2-1-1-0 Natural Science by Machine Learning

2-1-1-1 Eco System

2-1-1-2 Flow (Fluid Mechanics)

2-1-1-3 Change (Phase Transition and Chemical Reaction)

2-1-1-4 Energy (Thermodynamics and Exergy)

2-1-1-5 Transformation (Continuum Mechanics)

2-1-1-6 Atoms and Molecules

2-1-1-6-0 Four Elements and Chemistry

2-1-1-6-1 First Principle

2-1-1-6-1-1 Lead Free and First Principle Calculation

2-1-1-6-2 Molecular Dynamics

2-1-1-6-3 Simulated Annealing

2-1-1-6-4 Quantum Computer

2-1-1-6-5 Materials Informatics and Chemometrics

2-1-1-7 Chaos

2-1-1-7-1 Attractor

2-1-1-7-2 Complex Systems

2-1-2 *From Environment To Human Beings*

2-1-2-1 Environmental Physiology

2-1-2-2 Environmental Psychology

2-1-2-3 Honzo and Natural History

2-1-2-4 Stone

2-1-3 *From Human Beings To Environment*

2-1-3-1 Environmental Assessment

2-1-3-1-0 Index of Environmental Assessment

2-1-3-1-1 LCA (Life Cycle Assessment)

2-1-3-1-2 HEP

2-1-3-2 Trash

2-1-3-3 Trades of Crops

2-1-3-4 Purifying Technology

2-1-4 *Sustainable Society*

2-1-4-0 Mathematical Sociology

2-1-4-1 Environmental Thought

2-1-4-1-1 Oriental Medicine

2-1-4-1-2 Yin-Yang and Wu Xing

2-1-4-1-3 Feng shui

2-1-4-2 Environmental Laws

2-1-4-2-1 Licenses related with environmental problems

2-1-4-3 Environmental Economics

2-1-4-3-1 Kinds of Environmental Economics

2-1-4-3-2 Inclusion of Externality

2-1-4-3-3 Methods of Inclusion

2-1-4-3-3-1 Money as an index

2-1-4-3-4 Fusion of Ecology and Economy

2-1-4-3-5 Intermediate System

2-1-4-3-6 Index of Economical Assessment

2-1-4-3-7 Economical Data Analysis

2-1-4-3-8 Environmental Kuznets Curve

2-1-4-3-9 Books of Environmental Economics

2-1-4-4 Region

2-1-4-4-1 Fuudo Technology

2-1-4-4-2 Urban Planning

2-1-4-4-3 Regional Revitalization

2-1-4-4-3-1 Mingei (Works for daily use)

2-1-4-4-4 Location (Spatial Economics)

2-1-4-4-5 Quantitative Geography

2-1-4-4-5-1 GIS

2-1-4-4-5-2 Landscape Ecology

2-1-4-4-5-3 Spatial Statistics

2-1-4-4-6 Network

2-1-4-4-6-1 Propagation on Network

2-1-4-4-6-2 Growth of Network

2-1-4-5 Behavioral Science

2-1-4-5-1 Individual Behavior

2-1-4-5-1-1 Applied Behavior Analysis

2-1-4-5-1-2 Autism Spectrum Disorder (ASD)

2-1-4-5-1-3 Qualitative Social Research

2-1-4-5-1-4 Personnel Research

2-1-4-5-1-5 Psychotherapy and Clinical Psychology

2-1-4-5-2 Common Human Behavior

2-1-4-5-2-1 Behavioral Economics

2-1-4-5-2-1-1 Prospect Theory

2-1-4-5-2-1-2 Game Theory

2-1-4-5-2-2 Social Psychology

2-1-4-6 Brain Science

2-1-4-6-1 Cognition and Learning

2-1-4-6-1-1 Imitation and Counter-imitation

2-1-4-6-2 Thinking and Judgment

2-1-4-7 Semiotics

2-1-4-7-1 Philosophy

2-1-4-7-2 Analytical Philosophy

2-1-4-7-3 Linguistics

2-1-4-7-3-1 Language Learning

2-1-4-7-4 Logic

2-2 **Quality**

2-2-1 SPC

2-2-1-1 Process Analysis for Normal Condition

2-2-1-1-1 Process Capacity

2-2-1-1-2 Sampling Inspection

2-2-1-2 Process Analysis for Abnormal Condition

2-2-1-2-1 Cause analysis of temperature and humidity

2-2-1-2-2 Data analysis to draw out the knowledge of experts

2-2-1-3 Seven QC Tools and New Seven QC Tools

2-2-1-3-0 Tools of Data Science (G7, W7, M7)

2-2-1-3-1 Pareto Chart

2-2-1-3-2 Control Chart

2-2-1-3-3 PERT

2-2-1-3-4 Fishbone diagram (Cause-and-Effect Diagram)

2-2-1-3-5 Association Diagram

2-2-1-3-6 Tree Diagram (Logic Tree)

2-2-1-3-7 Affinity Diagram and Brain Storming

2-2-1-3-8 Why-why Analysis

2-2-1-3-9 Mind map

2-2-2 TQC

2-2-2-1 TQC, TQM and Others

2-2-2-2 Data Science for the Change of Manufacturing

2-2-2-3 Factory sensor data

2-2-3 Quality Engineering

2-2-3-1 Parameter Design

2-2-3-1-1 Robust Design

2-2-3-1-1-1 SN Ratio

2-2-3-1-1-2 Two Step Analysis

2-2-3-1-1-3 Kinds of Unevennes

2-2-3-1-1-4 "Functionality" of Quality Engineering

2-2-3-1-2 Classification of Characteristics

2-2-3-1-2-1 Difference of Static and Dynamic

2-2-3-1-2-2 Characteristics and SN Ratio

2-2-3-1-2-3 Dynamic SN Ratio

2-2-3-1-2-4 Dynamic Characteristics and Non-linearity

2-2-3-1-2-5 Standard SN Ratio

2-2-3-1-2-6 Quality Engineering Approach to Proportional variance

2-2-3-1-3 Classification of factors

2-2-3-1-3-1 Disturbance and Error Factor

2-2-3-1-3-2 Applied Experiment of Signal Factor

2-2-3-1-4-1 Outside Arrangement of Orthogonal Array

2-2-3-2 Process Control of Quality Engineering

2-2-3-3 MT System

2-2-3-4 Words of Quality Engineering

2-2-3-4-1 Future of Quality Engineering

2-2-3-4-2 "Energy" of Quality Engineering

2-2-3-4-3 "T Method" of Quality Engineering

2-2-4 Reliability Engineering

2-2-4-1 Destruction Engineering

2-2-5 Industrial Engineering

2-2-5-1 IE-method

2-2-5-2 Toyota Production System

2-2-5-3 Loss Time

2-2-6 Quality for Environment

2-2-7 Quality of Non-manufactured

2-3 **Risk**

*Kinds of Risk*

2-3-1 Risk of Chemical Substances

2-3-1-1 Epidemiology

2-3-2 Risk of Machines

2-3-3 Risk of Finance

*Methods of Risk*

2-3-4 Risk Management

2-3-5 Risk Cognition

2-3-6 Risk Evaluation

2-3-7 Risk Communication

2-4 **Management**

2-4-0 Management Engineering

2-4-1 *With Management*

2-4-1-1 SR (CSR)

2-4-1-2 Standard

2-4-1-3 ESG and Corporate Governance

2-4-1-4 Corporate Value

2-4-2 *Customer Relationship*

2-4-2-1 Marketing

2-4-2-1-1 Brand

2-4-2-1-2 Marketing Science

2-4-2-2 Design

2-4-2-2-1 Value Engineering

2-4-2-2-2 QFD (Quality Function Deployment)

2-4-2-2-3 Sensitivity Engineering

2-4-2-3 Inventory Control

2-4-2-3-1 Reordering Point System

2-4-2-3-2 Kanban System

2-4-2-4 Recommendation System

2-4-2-5 Business Model

2-4-2-6 Analysis of congestion (queues)

2-4-3 *To Move the Company*

2-4-3-1 Management System

2-4-3-1-1 Data Management

2-4-3-2 Problem solving and task achievement

2-4-3-2-1 System of problem solving and task achievement

2-4-3-2-2 Problem-solving steps

2-4-3-2-2-1 Distinguishing between grasping the current situation and factor analysis

2-4-3-2-2-2 Planning of measures

2-4-3-2-2-3 Standardization and establishment of management

2-4-3-2-2-4 Task achievement steps

2-4-3-2-2-5 Six sigma

2-4-3-2-3 Data science for problem solving and task achievement

2-4-3-2-4 Digital Transformation (DX)

2-4-3-3 Project Management

2-4-3-4 Business flow

2-4-3-5 Decision Making

2-4-3-5-1 Statistical Decision Making

2-4-3-5-2 Psychology for Decision Making

2-4-3-5-3 AHP

2-4-3-5-4 Profitability Analysis

2-4-3-6 Learning Organization

2-4-4 *Analysis of Management*

2-4-4-1 Financial Analysis

2-4-4-2 Management Accounting

2-4-4-2-1 Cost Analysis

2-4-4-3 Theory Of Constraints

**Data Analysis by Excel**

About

Visualization of the entire data

Pivot table

Outlier Analysis of Residuals by Excel

Excel Graph Tips

Easy prediction and simulation in Excel

**Data Analysis by R**

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Visualization of entire data with R

Analysis of variable similarity by R

Analysis of Variable Importance by R

Analysis of hidden variables by R

Analysis of anomaly quantification by R

Analysis of similarity between items in rows and columns by R

Visualization by compressing high dimensions into two dimensions by R

Visualization by compressing high-dimensional into two dimensions with regression analysis system by R

Outlier Detection by R

Cause-Effect Analysis of Indivisual Sampless by R

Analysis of similarity of individual categories by R

Text mining by R

Analysis of quasi-periodic data by R

Analysis of periodic data by R

Dimensionality reduction analysis of time series data by R

Analysis of the presence or absence of difference by R

Analysis of normality by R

Analysis of prediction interval by R

Control chart by R

Gage R and R by R

Principal component regression analysis by R

Decision tree by R

Cluster analysis by R

Multidimensional scaling by R

Generalized linear mixed model by R

Log-Linear Analysis by R

Principal component analysis by R

Correspondence analysis by R

Factor analysis by R

LiNGAM by R

Item Response Theory by R

Logistic regression analysis by R

Bayesian Network by R

Spline by R

Survival Analysis by R

Canonical correlation analysis by R

Interval High-dimensional regression analysis by R

Vector quantization label classification by R

Standard graph function of R

ggplot2

Plotly

Network graph by R

Heatmap by R

Variable conversion by R

Cross tabulation by R

Sample data for R

Pre-processing of missing values by R

Reachability Matrix

Web app R-QCA1

**Data Analysis by Python**

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Visualization of the entire data by Python

Analysis of variable similarity by Python

Analysis of hidden variables by Python

Pandas Plot (matplotlib)

seaborn

Creating metaknowledge data by Python

Analysis of quasi-periodic data by Python

Data cut and paste

**Data Analysis by R-EDA1**

Web app R-EDA1

R-EDA1 release notes

Analysis of airquality by R-EDA1

Analysis of mtcars by R-EDA1

Analysis of UScitiesD and eurodist by R-EDA1

Analysis of warpbreaks by R-EDA1

Analysis of factory_sensor01 by R-EDA1

**Q&A**

Can you estimate yield by the sampling test data?

What is the best model of for the ralationship between occurrence data and continuous data?

Tell me the python code to calculate average mutual information.

What is the difference between Taguhi method and desighn of experiment?

What is the best number of the data?

It is not normal distribution. How should I do?

Can you make summary of the text automatically?

Do not you use moving windows for sensor data?

Do you serch strong correlation for cause and effect analysis?

If Y is quantitative, use T method. If Y is qualitative, use MT method?

Cause is this variable?

How should I do to make smalller the unevennes?

What is the best model for this data?

Do you ever make high-dimensional (multivariate) models?

Is the hypothesis wrong because the result may come earlier?

Why don't you just swap the rows and columns?

Time is not evenly spaced. How analyze?

Is it analyzed by Fourier transform?

What is the tool (software) you often use?

What is the method (model) you often use?

What spec of computer I must prepare for the data analysis?

How do you read references?

Datascientist who resolve factory incidents