Contents

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
1-2-1-0-3 Tests for 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
1-2-1-1-4-0 Regression analysis of Constant variance
1-2-1-1-4-1 Prediction interval of Proportional variance
1-2-1-1-4-2 Constant variance and Proportional variance
1-2-1-1-5 Multiplicative model of Errors
1-2-1-1-5-1 Additive model and multiplicative model
1-2-1-1-5-2 Additive model and Proportional variance
1-2-1-1-5-3 Tests for correlation
1-2-1-1-6 Generalized Linear Model
1-2-1-1-6-1 Poisson regression 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
1-2-1-4-2-1 Proportional-proportional variance model
1-2-1-4-2-2 Generalized proportional variance model
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)
1-4Graphical 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 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
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)
1-9Analysis 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
1-9-1-2-6-1 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
1-9-2-1-3 Meta-thinking
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
1-9-2-3-4 Balance between symmetry and asymmetry
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