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Exam Information
Official specifications published by CompTIA
Exam Format
165 min
Maximum 90
Pass/Fail only (no scaled score)
Expert
Registration
$529 USD
Pearson VUE or online proctoring
Validity
3 years
Earn 60 Continuing Education Units (CEUs) in 3 years; Retake the current version of the exam; Earn a higher-level CompTIA certification
DY0-001 Exam Topics and Domains
DY0-001 is organized into 5 weighted domains.
1
Mathematics and Statistics
17%
Statistical Methods and Analysis
Hypothesis TestingProbability and DistributionsData Distribution Properties
- Apply appropriate statistical tests for different scenarios
- Understand and interpret probability distributions
- Analyze data distribution properties and their implications
Mathematical Foundations
Linear AlgebraCalculus for Data Science
- Apply linear algebra concepts to data science problems
- Use calculus for optimization in machine learning
- Understand mathematical foundations of algorithms
2
Modeling, Analysis, and Outcomes
24%
Data Analysis and Exploration
Exploratory Data Analysis (EDA)Feature Engineering
- Conduct comprehensive exploratory data analysis
- Engineer features to improve model performance
- Communicate insights through visualization
Model Design and Evaluation
Model Selection and DesignModel Evaluation Metrics
- Design appropriate models for business problems
- Evaluate model performance comprehensively
- Make data-driven model selection decisions
Data Handling and Preprocessing
Missing Data and Imbalanced Datasets
- Handle missing data effectively
- Address imbalanced datasets
- Prepare data for modeling
3
Machine Learning
24%
Supervised Learning
Classification AlgorithmsRegression Algorithms
- Implement supervised learning algorithms
- Select appropriate algorithms for problems
- Optimize model hyperparameters
Unsupervised Learning
Clustering TechniquesDimensionality Reduction
- Apply unsupervised learning techniques
- Discover patterns in unlabeled data
- Reduce data dimensionality effectively
Deep Learning and Neural Networks
Neural Network ArchitecturesDeep Learning Applications
- Understand neural network architectures
- Implement deep learning solutions
- Apply transfer learning techniques
4
Operations and Processes
22%
Data Science Business Applications
Business Problem FormulationEthics and Governance
- Align data science with business objectives
- Implement ethical AI practices
- Ensure regulatory compliance
Data Infrastructure and Engineering
Data Acquisition and StorageData Wrangling and Processing
- Design data infrastructure
- Implement data processing pipelines
- Manage data at scale
MLOps and Deployment
Model DeploymentModel Monitoring and Maintenance
- Deploy models to production environments
- Implement MLOps best practices
- Monitor and maintain deployed models
5
Specialized Applications of Data Science
13%
Natural Language Processing
Text Processing and AnalysisAdvanced NLP
- Apply NLP techniques to text data
- Build language understanding systems
- Implement modern NLP architectures
Computer Vision
Image Processing and Analysis
- Apply computer vision techniques
- Build image processing pipelines
- Implement vision-based solutions
Specialized Analytics
Time Series and ForecastingGraph Analytics and Optimization
- Apply specialized analytics techniques
- Build domain-specific solutions
- Solve complex optimization problems
How do I earn this certification?
Passing DY0-001 earns the CompTIA DataX certification. It sits in the Data Science track.
Current Level Exams
Next Level Options
Advanced specializations Pursue vendor-specific advanced certifications
Alternative Paths
- CY0-001 - CloudNetXCloud infrastructure for data science deployments
- SY0-701 - Security+Data security and privacy fundamentals
- CS0-003 - CySA+Security analytics and threat detection
Practice with Precision
The PlanetCert Simulator mirrors the real exam environment with authentic questions and timed pressure.
How to study for this exam?
The most effective way to prepare for DY0-001 is by using the PlanetCert Simulator to practice questions and review detailed explanations.
What's changed on this exam?
Current Status
- ACTIVE
- Last content update: 2024-07-25
- Announcement date: 2024-01-15
Updates
- Large Language Models GPT-4, BERT, T5
- MLOps Tools MLflow, Kubeflow, Airflow
- Cloud ML Platforms AWS SageMaker, Azure ML, GCP Vertex AI
- AutoML Various platforms
Who should take this exam?
This exam is typically taken by Data Scientists and Machine Learning Engineers.
- 5+ years of experience in data science or related role
- Strong foundation in mathematics and statistics
- Experience with machine learning implementations
- Proficiency in programming languages (Python or R)
- Understanding of data infrastructure and pipelines