[Part 1] Building Data Fundamentals

▣ Chapter 1: Understanding Statistics

1.1 Why should we know statistics?
1.2 Differences between Machine Learning and Traditional Statistics
1.3 Definition and Origin of Statistics
1.4 Descriptive Statistics Inferential Statistics
___1.4.1 Technical Statistics
___1.4.2 Inferential Statistics

▣ Chapter 2: Population and Sampling

2.1 Population and sample, census and sample survey
2.2 Why do we conduct sampling surveys and how do we apply data science?
2.3 Types of bias in sampling
2.4 Types of cognitive biases
___2.4.1 Confirmation bias
___2.4.2 Anchoring bias
___2.4.3 Choice-supportive bias
___2.4.4 Denominator bias
___2.4.5 Survivorship bias
2.5 Bias and Variance in Machine Learning Models
2.6 Sampling methods to minimize sampling bias

▣ Chapter 3: Variables and Scales

3.1 Types of variables
3.2 Types of variable relationships
3.3 Types of scales

▣ Chapter 4: Descriptive Statistical Measurements of Data

4.1 Measurement of central tendency
4.2 Variance and standard deviation
4.3 Scatterplot and range, quartiles, and coefficient of variation
4.4 Skewness and Kurtosis
___4.4.1 Skewness
___4.4.2 Kurtosis
4.5 Rule of thumb for standard deviation

▣ Chapter 5: Probability and Random Variables

5.1 Basic Concepts of Probability
5.2 Types of Probability
5.3 Segmentation and Bayesian Theory
___5.3.1 Split
___5.3.2 Bayesian theory
5.4 Concept and types of random variables
5.5 Simpson's Paradox

▣ Chapter 6: Probability Distributions

6.1 Definition and types of probability distributions
6.2 Discrete probability distributions
___6.2.1 Uniform distribution
___6.2.2 Binomial distribution
___6.2.3 Hypergeometric distribution
___6.2.4 Poisson distribution
6.3 Continuous probability distribution
___6.3.1 Normal distribution
___6.3.2 Exponential distribution
6.4 Central Limit Theorem

▣ Chapter 7: Hypothesis Testing

7.1 Null and alternative hypotheses
7.2 Hypothesis testing procedures
7.3 Significance level and p-value of hypothesis testing
7.4 Type 1 and Type 2 errors

[Part 2] Preparing for Data Analysis

▣ Chapter 8: Analysis Project Preparation and Planning

8.1 Overall data analysis process
___8.1.1 Three Steps of Data Analysis
___8.1.2 CRISP-DM methodology
___8.1.3 SAS SEMMA Methodology
8.2 Defining the business problem and deriving the analysis objectives
8.3 Change in analysis purpose
8.4 Domain Knowledge
8.5 External Data Collection and Crawling

▣ Chapter 9: Setting Up the Analysis Environment

9.1 Which data analysis language should I use?
9.2 Understanding the Data Processing Process
9.3 Distributed Data Processing
___9.3.1 HDFS
___9.3.2 Apache Spark
9.4 Table Joins, Definitions, and ERDs
___9.4.1 Table Joins
___9.4.2 Data Dictionary
___9.4.3 Table Definition
___9.4.4 ERD

▣ Chapter 10: Data Exploration and Visualization

10.1 Exploratory Data Analysis
___10.1.1 EDA using Excel
___10.1.2 Exploratory Data Analysis Practice
10.2 Covariance and Correlation Analysis
___10.2.1 Covariance
___10.2.2 Correlation coefficient
___10.2.3 Covariance and Correlation Analysis Practice
10.3 Time Visualization
___10.3.1 Time Visualization Practice
10.4 Comparative Visualization
___10.4.1 Comparative Visualization Practice
10.5 Distribution Visualization
___10.5.1 Distribution Visualization Practice
10.6 Relationship Visualization
___10.6.1 Relationship Visualization Practice
10.7 Spatial Visualization
___10.7.1 Spatial Visualization Practice
10.8 Box plot
___10.8.1 Box Plot Practice

▣ Chapter 11: Data Preprocessing and Derived Variable Creation

11.1 Handling missing values
___11.1.1 Practice Handling Missing Values
11.2 Outlier Handling
___11.2.1 Outlier Handling Practice
11.3 Variable Binning
___11.3.1 Variable Binning Practice
11.4 Data Standardization and Normalization Scaling
___11.4.1 Data Standardization and Normalization Scaling Practice
11.5 Creating Derived Variables to Improve Model Performance
___11.5.1 Practice creating derived variables
11.6 Sliding Window Data Processing
___11.6.1 Sliding Window Practice
11.7 Handling dummy variables of categorical variables
___11.7.1 Practice handling dummy variables of categorical variables
11.8 Undersampling and Oversampling to Address Class Imbalance Problems
___11.8.1 Undersampling and Oversampling Practice
11.9 How to measure data distance
___11.9.1 Representative distance measurement methods
___11.9.2 Data Distance Measurement Practice

[Part 3] Analyzing Data

▣ Chapter 12: Statistical Analysis Methodology

12.1 Analysis Model Overview
12.2 Principal Component Analysis (PCA)
___12.2.1 Principal Component Analysis Practice
12.3 Common Factor Analysis (CFA)
___12.3.1 Common Factor Analysis Practice
12.4 Addressing Multicollinearity and Shapley Value Analysis
12.5 Data Massage and Blind Analysis
___12.5.1 Data Massage
___12.5.2 Blind Analysis
12.6 Z-test and T-test
___12.6.1 Z-test and T-test Practice
12.7 Analysis of Variance (ANOVA)
___12.7.1 ANOVA Practice
12.8 Chi-square test (cross-tabulation)
___12.8.1 Chi-square test practice

▣ Chapter 13: Machine Learning Analysis Methodology

13.1 Linear Regression Analysis and Elastic Net (Predictive Model)
___13.1.1 Origin and Principles of Regression Analysis
___13.1.2 Polynomial regression
___13.1.3 Ridge, Lasso, and Elastic Net
___13.1.4 Linear Regression Analysis and Elastic Net Practice
13.2 Logistic Regression Analysis (Classification Model)
___13.2.1 Logistic Regression Analysis Practice
13.3 Decision Trees and Random Forests (Prediction/Classification Models)
___13.3.1 Classification trees and regression trees
___13.3.2 Advantages and Disadvantages of Decision Tree Models
___13.3.3 Methods for preventing overfitting of decision tree models
___13.3.4 Random Forest
___13.3.5 Decision Tree and Random Forest Practice
13.4 Linear Discriminant Analysis and Quadratic Discriminant Analysis (Classification Models)
___13.4.1 Linear Discriminant Analysis
___13.4.2 Second-order discriminant analysis
___13.4.3 Practice of Linear Discriminant Analysis and Quadratic Discriminant Analysis
13.5 Support Vector Machine (Classification Model)
___13.5.1 Support Vector Machine Practice
13.6 KNN (Classification, Prediction Model)
___13.6.1 KNN Practice
13.7 Time Series Analysis (Forecasting Model)
___13.7.1 Regression-based time series analysis
___13.7.2 ARIMA Model
___13.7.3 Time Series Analysis Practice
13.8 k-means clustering (clustering model)
___13.8.1 k-means clustering practice
13.9 Association Rules and Collaborative Filtering (Recommendation Models)
___13.9.1 Association Rules
___13.9.2 Content-Based Filtering and Collaborative Filtering
___13.9.3 Association Rules and Collaborative Filtering Practice
13.10 Artificial Neural Networks (CNN, RNN, LSTM)
___13.10.1 CNN
___13.10.2 RNN and LSTM
___13.10.3 Artificial Neural Network Practice

▣ Chapter 14: Model Evaluation

14.1 Training set, validation set, test set, and overfitting
14.2 Major cross-validation methods
___14.2.1 k-Fold Cross Validation
___14.2.2 LOOCV(Leave-one-out Cross-validation)
___14.2.3 Stratified K-fold Cross Validation
___14.2.4 Nested Cross Validation
___14.2.5 Grid Search Cross Validation
___14.2.6 Practicing the Major Cross-Validation Methods
14.3 Regression Performance Evaluation Index
___14.3.1 R-Square and Adjusted R-Square
___14.3.2 RMSE (Root Mean Square Error)
___14.3.3 MAE(Mean Absolute Error)
___14.3.4 MAPE(Mean Absolute Percentage Error)
___14.3.5 RMSLE(Root Mean Square Logarithmic Error)
___14.3.6 AIC and BIC
___14.3.7 Regression Performance Evaluation Index Practice
14.4 Classification and Recommendation Performance Evaluation Indicators
___14.4.1 Confusion Matrix
___14.4.2 Accuracy, misclassification rate, precision, sensitivity, specificity, and f-score
___14.4.3 Improvement Table, Improvement Chart, and Improvement Curve
___14.4.4 ROC Curve and AUC
___14.4.5 Profit Curve
___14.4.6 Precision at k, Recall at K, and MAP
___14.4.7 Classification, Recommendation Performance Evaluation Indicator Practice
14.5 A/B Testing and MAB
___14.5.1 A/B Testing
___14.5.2 MAB
14.6 Pitfalls of Significance Probability
14.7 Analyst's Subjective Judgment and Storytelling

★ What this book covers ★

◎ Basic statistical concepts essential for data analysis
◎ Data bias and cognitive bias
◎ How to define business problems and derive the purpose of data analysis
◎ Configuring the data analysis environment
◎ Data exploration and visualization
◎ Data preprocessing and derived variable creation
◎ Major machine learning algorithms and model performance evaluation techniques
◎ A/B testing and MAB