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Data Preprocessing
Data preprocessing is a crucial step in data analysis that ensures raw data is cleaned, formatted, and structured for better modeling and insights. It involves multiple techniques aimed at improving data quality and consistency.
Data Cleaning
- Identifying and correcting errors
- Removing duplicates
- Filtering out irrelevant data
- Addressing inconsistencies
- Validating data accuracy
Handling Missing Values
- Imputation with mean, median, or mode
- Predictive modeling for missing data
- Deletion of records with missing values
- Using algorithms that support missing values
- Flagging missing data for further analysis
Outlier Detection and Removal
- Statistical methods (e.g., Z-scores, IQR)
- Visual methods (e.g., box plots)
- Domain-specific thresholds
- Transformation techniques
- Capping or flooring values
Data Transformation
- Normalization and standardization
- Logarithmic transformations
- Encoding categorical variables
- Aggregating data
- Discretization
Data Integration
- Merging datasets from different sources
- Resolving schema conflicts
- Ensuring data consistency
- Handling duplicate records
- Establishing relationships between integrated data
Data Reduction
- Dimensionality reduction (e.g., PCA)
- Numerosity reduction
- Data compression techniques
- Sampling methods
- Aggregation of data
Normalization and Standardization
- Min-max scaling
- Z-score standardization
- Robust scaling
- Unit vector transformation
- Log transformation
Data Encoding
- One-hot encoding
- Label encoding
- Binary encoding
- Frequency encoding
- Target encoding
Data Sampling
- Random sampling
- Stratified sampling
- Systematic sampling
- Cluster sampling
- Reservoir sampling
Data Validation
- Consistency checks
- Range checks
- Uniqueness checks
- Format validation
- Cross-field validation
Descriptive Analysis
It involves summarizing and organizing data to understand its main characteristics, often through numerical calculations, graphs, and tables.
Frequency Distribution
- Simple Frequency Distribution
- Grouped Frequency Distribution
- Cumulative Frequency Distribution
- Relative Frequency Distribution
- Percentage Frequency Distribution
Measures of Central Tendency
- Mean
- Median
- Mode
- Geometric Mean
- Harmonic Mean
Measures of Dispersion
- Range
- Variance
- Standard Deviation
- Interquartile Range
- Mean Absolute Deviation
Percentile Analysis
- Quartiles
- Deciles
- Percentiles
- Z-Scores
- T-Scores
Cross-Tabulation
- Two-Way Tables
- Multi-Way Tables
- Contingency Tables
- Chi-Square Test
- Fisher's Exact Test
Data Summarization
- Descriptive Statistics
- Data Tabulation
- Aggregation
- Data Integration
- Data Cleaning
Trend Analysis
- Time Series Analysis
- Moving Averages
- Exponential Smoothing
- Seasonal Decomposition
- Regression Analysis
Data Profiling
- Structure Discovery
- Content Discovery
- Relationship Discovery
- Anomaly Detection
- Data Quality Assessment
Visualization of Summaries
- Bar Charts
- Histograms
- Pie Charts
- Box Plots
- Scatter Plots
Report Generation
- Executive Summaries
- Detailed Analysis Reports
- Dashboards
- Infographics
- Presentations
Inferential Statistics
It involves analyzing sample data to make generalizations about a larger population, enabling predictions and decisions under uncertainty..
Hypothesis Testing
- Z-Test
- T-Test
- ANOVA (Analysis of Variance)
- Chi-Square Test
- F-Test
Confidence Interval Estimation
- Confidence Interval for Mean
- Confidence Interval for Proportion
- Confidence Interval for Difference of Means
- Confidence Interval for Difference of Proportions
- Prediction Interval
Significance Testing (p-values)
- One-Tailed Test
- Two-Tailed Test
- Type I Error
- Type II Error
- Multiple Comparisons Adjustment
Nonparametric Tests
- Mann-Whitney U Test
- Wilcoxon Signed-Rank Test
- Kruskal-Wallis Test
- Spearman's Rank Correlation
- Friedman Test
Parametric Tests
- Paired T-Test
- Independent T-Test
- One-Way ANOVA
- Two-Way ANOVA
- Linear Regression
Chi-Square Tests
- Chi-Square Test for Independence
- Chi-Square Test for Goodness of Fit
- Yates' Correction for Continuity
- McNemar's Test
- Fisher's Exact Test
Correlation Analysis
- Pearson Correlation
- Spearman Correlation
- Kendall's Tau
- Partial Correlation
- Point-Biserial Correlation
Variance Analysis
- One-Way ANOVA
- Two-Way ANOVA
- MANOVA (Multivariate Analysis of Variance)
- ANCOVA (Analysis of Covariance)
- Repeated Measures ANOVA
Sample Size Determination
- Cohen's D
- Effect Size
- Power Analysis
- Margin of Error
- Confidence Level
Power Analysis
- Prospective Power Analysis
- Retrospective Power Analysis
- Post-Hoc Power Analysis
- Sensitivity Analysis
- Specificity Analysis
Regression Analysis
It is a statistical technique to model relationships between a dependent variable and one or more independent variables, enabling predictions and insights into data trends.
Simple Linear Regression
- Ordinary Least Squares (OLS)
- Best-Fit Line Calculation
- Slope and Intercept Estimation
- Correlation vs. Causation
- Assumption of Linearity
Multiple Linear Regression
- Multicollinearity Considerations
- Adjusted R-Squared
- Feature Selection Techniques
- Interaction Terms Inclusion
- Homoscedasticity
Logistic Regression
- Binary Logistic Regression
- Multinomial Logistic Regression
- Odds Ratios Interpretation
- Maximum Likelihood Estimation
- Link Functions
Polynomial Regression
- Quadratic Regression
- Cubic Regression
- Overfitting Risk
- Basis Function Transformation
- Feature Scaling Importance
Stepwise Regression
- Forward Selection
- Backward Elimination
- Hybrid Selection Methods
- AIC/BIC Model Criteria
- Automated Variable Selection
Ridge and Lasso Regression
- L1 Regularization (Lasso)
- L2 Regularization (Ridge)
- Elastic Net Regression
- Shrinkage Methods
- Hyperparameter Tuning
Interaction Effects Modeling
- Interaction Terms in Regression
- Moderation Effects
- Centering Variables
- Interpretation of Interaction Coefficients
- Statistical Significance Testing
Residual Analysis
- Normality of Residuals
- Homoscedasticity Tests
- Residual Plots Interpretation
- Outlier Detection
- Influence Measures (Cook’s Distance)
Model Diagnostics
- Variance Inflation Factor (VIF)
- Durbin-Watson Test
- Leverage Points Analysis
- Autocorrelation Checks
- Goodness-of-Fit Evaluation
Regression Validation
- Cross-Validation Techniques
- Train-Test Splitting
- Bootstrapping Methods
- Bias-Variance Tradeoff
- Model Generalizability
Time Series Analysis
Statistical methods for examining data points collected or recorded at successive time intervals. It helps to identify underlying patterns and build forecasting models while assessing model performance through error measurement.
Trend Analysis
- Linear Trend
- Exponential Trend
- Polynomial Trend
- Moving Average Trend
- Logarithmic Trend
Seasonal Decomposition
- Additive Decomposition
- Multiplicative Decomposition
- Classical Decomposition
- STL Decomposition
- Seasonal Subseries Plot
Stationarity Testing
- Augmented Dickey-Fuller Test
- KPSS Test
- Phillips-Perron Test
- Variance Ratio Test
- ADF-GLS Test
Autocorrelation Analysis
- Autocorrelation Function (ACF)
- Partial Autocorrelation Function (PACF)
- Cross-Correlation Function (CCF)
- Ljung-Box Test
- Durbin-Watson Statistic
Smoothing Techniques
- Simple Moving Average
- Exponential Moving Average
- Weighted Moving Average
- Holt’s Linear Trend Method
- Double Exponential Smoothing
Forecasting Models
- Naive Forecasting
- Seasonal Naive Forecasting
- Drift Method
- Ensemble Forecasting
- Benchmark Models
ARIMA Modeling
- AR Model (AutoRegressive)
- MA Model (Moving Average)
- ARMA Model
- ARIMA with Differencing
- SARIMA (Seasonal ARIMA)
Exponential Smoothing
- Simple Exponential Smoothing
- Holt’s Linear Trend
- Holt-Winters Additive
- Holt-Winters Multiplicative
- Damped Trend Exponential Smoothing
Time Series Regression
- Lagged Variables Regression
- Distributed Lag Models
- Dynamic Regression Models
- Cointegration Regression
- Error Correction Models
Error Measurement
- Mean Absolute Error (MAE)
- Mean Squared Error (MSE)
- Root Mean Squared Error (RMSE)
- Mean Absolute Percentage Error (MAPE)
- Symmetric Mean Absolute Percentage Error (sMAPE)
Multivariate Analysis
It is a statistical technique that helps uncover relationships, patterns, and underlying structures in high-dimensional datasets.
Principal Component Analysis (PCA)
- Standard PCA
- Kernel PCA
- Sparse PCA
- Robust PCA
- Incremental PCA
Factor Analysis
- Exploratory Factor Analysis (EFA)
- Confirmatory Factor Analysis (CFA)
- Principal Factor Analysis
- Maximum Likelihood Factor Analysis
- Bayesian Factor Analysis
Cluster Analysis
- Hierarchical Clustering
- K-means Clustering
- Density-Based Clustering (DBSCAN)
- Model-Based Clustering
- Fuzzy C-Means Clustering
Discriminant Analysis
- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
- Regularized Discriminant Analysis (RDA)
- Stepwise Discriminant Analysis
- Kernel Discriminant Analysis
MANOVA
- One-way MANOVA
- Two-way MANOVA
- Repeated Measures MANOVA
- Nested MANOVA
- Multivariate Analysis of Covariance (MANCOVA)
Canonical Correlation Analysis
- Standard Canonical Correlation
- Partial Canonical Correlation
- Redundancy Analysis
- Regularized Canonical Correlation
- Sparse Canonical Correlation
Multidimensional Scaling
- Classical MDS
- Metric MDS
- Non-metric MDS
- Torgerson Scaling
- Sammon Mapping
Correspondence Analysis
- Simple Correspondence Analysis
- Multiple Correspondence Analysis
- Canonical Correspondence Analysis
- Symmetric Correspondence Analysis
- Detrended Correspondence Analysis
Structural Equation Modeling
- Covariance-Based SEM
- Partial Least Squares SEM (PLS-SEM)
- Path Analysis
- Integrated Confirmatory Factor Analysis
- Latent Growth Modeling
Multivariate Regression
- Multiple Linear Regression
- Multinomial Logistic Regression
- Ridge Regression
- Lasso Regression
Predictive Modeling
It involves using historical data and statistical algorithms to forecast future outcomes, supporting data-driven decision-making across various industries.
Classification Algorithms
- Logistic Regression
- Naive Bayes
- K-Nearest Neighbors (KNN)
- Decision Trees
- Support Vector Machines
Decision Trees
- CART (Classification and Regression Trees)
- C4.5
- C5.0
- CHAID
- ID3
Ensemble Methods
- Bagging
- Boosting
- Stacking
- Voting Classifier
- Blending
Random Forests
- Bootstrapped Aggregation
- Feature Bagging
- Out-of-Bag Estimation
- Variable Importance
- Proximity Measures
Support Vector Machines
- Linear SVM
- Nonlinear SVM
- Kernel SVM
- Soft Margin SVM
- Hard Margin SVM
Neural Networks
- Feedforward Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Deep Neural Networks
- Autoencoders
Model Training and Testing
- Train/Test Split
- Holdout Method
- Bootstrapping
- Grid Search
- Hyperparameter Tuning
Cross-Validation Techniques
- K-Fold Cross-Validation
- Stratified K-Fold Cross-Validation
- Leave-One-Out Cross-Validation (LOOCV)
- Repeated K-Fold
- Time Series Cross-Validation
Feature Selection
- Filter Methods
- Wrapper Methods
- Embedded Methods
- Recursive Feature Elimination (RFE)
- Principal Component Analysis (PCA)
Quality Control
It involve systematic techniques and statistical methods to monitor, control, and enhance operational processes. These practices aim to identify and eliminate process variations and defects, ensuring higher efficiency and consistent quality through continuous monitoring and improvement.
Control Charts
- X-Bar Chart
- R-Chart
- S-Chart
- p-Chart
- c-Chart
Process Capability Analysis
- Cp Index
- Cpk Index
- Pp Index
- Ppk Index
- Process Performance Index
Six Sigma Methodologies
- DMAIC
- DMADV
- DFSS (Design for Six Sigma)
- Lean Six Sigma
- Black Belt Methodologies
DMAIC Framework
- Define
- Measure
- Analyze
- Improve
- Control
Pareto Analysis
- Pareto Chart
- 80/20 Rule Analysis
- Cumulative Impact
- Defect Contribution Analysis
- Pareto Principle Application
Root Cause Analysis
- 5 Whys
- Fault Tree Analysis
- Failure Mode and Effects Analysis (FMEA)
- Cause and Effect Diagram
- Brainstorming Sessions
Process Mapping
- Flowcharting
- Value Stream Mapping
- SIPOC Diagram
- Process Flow Diagrams
- Swim Lane Diagrams
Fishbone Diagram
- Ishikawa Diagram
- Cause and Effect Diagram
- Brainstorming Diagram
- Root Cause Fishbone
- Process Cause Diagram
Statistical Process Control (SPC)
- Control Charts
- Process Monitoring
- Sampling Plans
- Process Behavior Charts
- Real-Time SPC
Continuous Improvement Metrics
- Cost of Quality (COQ)
- Defect Rates
- Cycle Time Reduction
- Overall Equipment Effectiveness (OEE)
- Customer Satisfaction Index
Data Visualization
It is graphical representation of information and data, enabling stakeholders to identify trends, patterns, and insights effectively
Bar and Column Charts
- Grouped Bar Chart
- Stacked Bar Chart
- Diverging Bar Chart
- Bullet Graph
Line Graphs
- Multiple Line Graph
- Step Line Graph
- Smoothed Line Graph
- Area Line Graph
- Sparkline
Scatter Plots
- Bubble Chart
- Dot Plot
- Scatter Plot Matrix
- 3D Scatter Plot
- Connected Scatter Plot
Histograms
- Equal Interval Histogram
- Variable Bin Width Histogram
- Cumulative Histogram
- Density Plot
- Stacked Histogram
Box Plots
- Notched Box Plot
- Variable Width Box Plot
- Violin Plot
- Scatter Box Plot
- Grouped Box Plot
Heat Maps
- Correlation Heat Map
- Geographical Heat Map
- Clustered Heat Map
- Calendar Heat Map
- Table Heat Map
Geographic Maps
- Choropleth Map
- Proportional Symbol Map
- Dot Distribution Map
- Cartogram
- Heat Map Overlay
Network Diagrams
- Force-Directed Graph
- Hierarchical Network Diagram
- Circular Network Diagram
- Matrix-Based Network Diagram
- Arc Diagram
Interactive Dashboards
- Real-Time Dashboard
- Analytical Dashboard
- Operational Dashboard
- Strategic Dashboard
- Tactical Dashboard
Infographics
- Statistical Infographic
- Informational Infographic
- Timeline Infographic
- Process Infographic
- Comparison Infographic
Report Writing
It is a structured process of presenting information clearly and concisely to a specific audience and purpose. A well-organized report typically includes several key sections, each serving a distinct function.
Executive Summary
A brief overview encapsulating the main points of the report, including its purpose, methods, findings, and conclusions. It allows readers to quickly grasp the essence of the report without delving into the full content.
Introduction and Background
This section sets the context by outlining the purpose of the report, the issues to be discussed, and their significance. It may also include the scope, methods, and organization of the report.
Methodology Description
Details the methods and procedures employed in the study or investigation, providing enough information for the reader to understand how data was collected and analyzed.
Data Analysis Results
Presents the findings of the study in a clear and objective manner, often using tables, graphs, and charts to enhance understanding. This section focuses on factual data without interpretation.
Discussion and Interpretation
Analyzes and interprets the results, explaining their implications, significance, and how they relate to the original objectives or hypotheses. This section may also compare findings with existing literature.
Conclusions
Summarizes the main findings and their broader implications, providing a clear and concise statement of what has been learned from the study.
Recommendations
Offers actionable suggestions based on the conclusions, advising on potential steps, solutions, or areas for further research.
Limitations
Acknowledges any constraints or limitations encountered during the study, such as methodological weaknesses or data constraints, which may affect the interpretation of the results.
References and Citations
Lists all the sources cited in the report, providing full bibliographic details to allow readers to locate the original materials. This section ensures academic integrity and gives credit to previous work.
Appendices and Supplementary Materials
Includes additional material that supports the report but is too detailed or voluminous to be included in the main body, such as raw data, detailed calculations, or technical diagrams.
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