COMPTIACompTIA DataX
CompTIA DataX certification validates expertise in data science, equipping professionals with skills to handle complex data sets and implement data-driven solutions. Ideal for experienced data scientists aiming to drive business growth through insightful data interpretation.
654 practice questions · Updated 2023-10-01
5Domains
25Objectives
169Concepts
654Questions
DY0-001 Curriculum
Every domain, objective, and concept the DY0-001 exam measures.
- T-Test Application
- Chi-Squared Test Application
- ANOVA Application
- Hypothesis Testing
- Regression Metrics
- Gini Index
- Entropy Calculation
- P-Value Interpretation
- ROC/AUC Analysis
- AIC/BIC Evaluation
- Confusion Matrix Analysis
- Distributions
- Skewness
- Kurtosis
- Heteroskedasticity
- Probability Density Function (PDF)
- Probability Mass Function (PMF)
- Cumulative Distribution Function (CDF)
- Missingness
- Oversampling
- Stratification
- Matrix Rank
- Eigenvalues
- Matrix Operations
- Distance Metrics
- Partial Derivatives
- Chain Rule
- Logarithms
- Time Series Definition
- Time Series Analysis Techniques
- Survival Analysis Definition
- Survival Analysis Techniques
- Causal Inference Definition
- Causal Inference Methods
- Comparing Time Series and Survival Analysis
- Comparing Time Series and Causal Inference
- Comparing Survival Analysis and Causal Inference
- Univariate Analysis
- Multivariate Analysis
- Data Visualization with Charts
- Graphical Data Representation
- Feature Identification
- Sparse Data Analysis
- Non-linearity Detection
- Seasonality Identification
- Granularity Assessment
- Outlier Detection
- Feature Engineering
- Feature Scaling
- Geocoding
- Data Transformation
- Model Design Principles
- Model Evaluation Techniques
- Model Selection Criteria
- Model Validation Methods
- VisualizationCreation
- DataSelectionForVisualization
- AvoidingDeceptiveCharts
- EnsuringChartAccessibility
- Loss Functions
- Bias-Variance Tradeoff
- Regularization Techniques
- Cross-Validation
- Ensemble Models
- Hyperparameter Tuning
- Data Leakage
- Linear Regression Fundamentals
- Linear Regression Application
- Logistic Regression Fundamentals
- Logistic Regression Application
- K-Nearest Neighbors Fundamentals
- K-Nearest Neighbors Application
- Naive Bayes Fundamentals
- Naive Bayes Application
- Association Rules Fundamentals
- Association Rules Application
- Decision Tree Structure
- Decision Tree Splitting Criteria
- Decision Tree Pruning
- Random Forest Ensemble
- Random Forest Feature Importance
- Boosting Algorithms
- Bagging Technique
- Overfitting in Tree-Based Models
- Hyperparameter Tuning in Tree-Based Models
- Artificial Neural Networks (ANN)
- Dropout
- Batch Normalization
- Backpropagation
- Deep-Learning Frameworks
- Clustering
- K-Means Clustering
- Hierarchical Clustering
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Applications of SVD
- Compliance Definition
- Regulatory Compliance
- Compliance Monitoring
- Key Performance Indicators (KPIs)
- KPI Selection
- KPI Analysis
- Requirements Gathering Process
- Stakeholder Identification
- Requirements Documentation
- Generated Data Definition
- Synthetic Data Definition
- Public Data Definition
- Generated Data Use Cases
- Synthetic Data Generation Techniques
- Public Data Sources
- Data Privacy Considerations
- Comparing Data Types
- Data Pipelines
- Streaming Data
- Batch Processing
- Data Lineage
- Data Cleaning Techniques
- Data Merging Methods
- Data Imputation Strategies
- Ground Truth Labeling
- Workflow Models
- Version Control Systems
- Clean Code Practices
- Unit Testing
- Continuous Integration
- Continuous Deployment
- Model Deployment
- Container Orchestration
- Performance Monitoring
- Containerization Basics
- Cloud Deployment Models
- Hybrid Deployment Characteristics
- Edge Computing Deployment
- On-Premises Deployment
- Comparing Deployment Environments
- Definition of Optimization
- Constrained Optimization
- Unconstrained Optimization
- Constraints in Optimization
- Comparing Optimization Types
- Formulating Optimization Problems
- Optimization Techniques
- NLP Overview
- Tokenization
- Embeddings
- TF-IDF
- Topic Modeling
- NLP Applications
- Optical Character Recognition (OCR) Basics
- OCR Algorithms
- Object Detection Fundamentals
- Object Detection Algorithms
- Tracking in Computer Vision
- Tracking Algorithms
- Data Augmentation Techniques
- Impact of Data Augmentation
- Graph Analysis Basics
- Graph Theory Concepts
- Reinforcement Learning Principles
- Fraud Detection Techniques
- Anomaly Detection Methods
- Signal Processing Fundamentals
- Applications of Graph Analysis
- Reinforcement Learning Algorithms
- Fraud Detection Algorithms
- Anomaly Detection Algorithms
- Signal Processing Techniques
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Percentages reflect share of the current practice bank, not official exam weightings — no structured per-skill weight is published for DY0-001, so none is invented.