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CompTIA DataAI (DY0-001)

Category:

Upon completion of this course, candidate will be able to:

• Understand and implement data science operations and processes.
• Apply mathematical and statistical methods appropriately and understand the importance of
data processing and cleaning, statistical modeling, linear algebra, and calculus concepts.
• Apply machine-learning models and understand deep-learning concepts.
• Utilize appropriate analysis and modeling methods and make justified model
recommendations.
• Demonstrate understanding of industry trends and specialized data science applications.

5+ years in data science or a similar role is recommended.

• Data Analyst
• E-commerce Analyst
• Data Scientist
• IT Manager

1.1 Given a scenario, apply the appropriate statistical method or concept
  • t-tests
  • Chi-squared test
  • Analysis of variance (ANOVA)
  • Hypothesis testing
  • Confidence intervals
  • Regression performance metrics
  • Gini index
  • Entropy
  • Information gain
  • P value
  • Type I and Type II errors
  • Receiver operating characteristics/area under the curve (ROC/AUC)
  • Akaike Information criterion/Bayesian information criterion (AIC/BIC)
  • Correlation coefficients
  • Confusion matrix
1.2 Explain probability and synthetic modeling concepts and their uses
  • Distributions
  • Skewness
  • Kurtosis
  • Heteroskedasticity vs. homoscedasticity
  • Probability density function (PDF)
  • Probability mass function (PMF)
  • Cumulative distribution function (CDF)
  • Probability
  • Types of missingness
  • Oversampling
  • Stratification
1.3 Explain the importance of linear algebra and basic calculus concepts
  • Linear algebra
  • Calculus
1.4 Compare and contrast various types of temporal models
  • Time series
  • Longitudinal studies
  • Survival analysis
  • Causal inference
2.1 Given a scenario, use the appropriate exploratory data analysis (EDA) method or process
  • Univariate analysis
  • Multivariate analysis
  • Identification of object behaviors and attributes
  • Charts and graphs
  • Feature type identification
2.2 Given a scenario, analyze common issues with data
  • Common issues with data
2.3 Given a scenario, apply data enrichment and augmentation techniques
  • Feature engineering
  • Data transformation
  • Geocoding
  • Scaling
  • Standardization
  • Additional data sources
2.4 Given a scenario, conduct a model design iteration process
  • Design and specifications
  • Performance evaluation
  • Model selection
  • Requirements validation
2.5 Given a scenario, analyze results of experiments and testing to justify final model recommendations and selection
  • Benchmark against the baseline
  • Benchmark against the conventional processes
  • Specification testing results
  • Final performance measures
  • Satisfy business requirements
2.6 Given a scenario, translate results and communicate via appropriate methods and mediums
  • Types of visualizations and reports
  • Data selection for reports
  • Effective communication and report considerations for peers and stakeholders
  • Consider data types, dimensions, and levels of aggregation to produce appropriate visualizations/reports
  • Avoid unintentionally deceptive charting and reporting
  • Chart accessibility
  • Data and model documentation
3.1 Given a scenario, apply foundational machine-learning concepts
  • Loss function
  • Bias-variance tradeoff
  • Variable/feature selection
  • Class imbalance and mitigations
  • Regularization
  • Cross-validation
  • The curse of dimensionality
  • Occam's razor/law of parsimony
  • In sample vs. out of sample
  • Interpolation vs. extrapolation
  • Ensemble models
  • Hyperparameter tuning
  • Classifiers
  • Recommender systems
  • Regressors
  • Embeddings
  • Post hoc model explainability
  • Interpretable models
  • Model drift causes
  • Data leakage
3.2 Given a scenario, apply appropriate statistical supervised machine-learning concepts
  • Linear regression models
  • Logistic regression models
  • Linear discriminant analysis
  • Quadratic discriminant analysis (QDA)
  • Association rules
  • Naive Bayes
3.3 Given a scenario, apply tree-based supervised machine-learning concepts
  • Decision trees
  • Random forest
  • Boosting
  • Bootstrap aggregation (bagging)
3.4 Explain concepts related to deep learning
  • Artificial neural network architecture
  • Dropout
  • Batch normalization
  • Early stopping
  • Schedulers
  • Back propagation
  • One-shot learning
  • Zero-shot learning
  • Few-shot learning
  • Deep-learning frameworks
  • Optimizers
  • Model types
3.5 Explain concepts related to unsupervised machine learning
  • Clustering
  • Dimensionality reduction
  • k-nearest neighbors (KNN)
  • Singular value decomposition (SVD)
4.1 Explain the role of data science in various business functions
  • Compliance, security, and privacy
  • Measures, metrics, and key performance indicators (KPIs)
  • Requirements gathering
4.2 Explain the process of and purpose for obtaining different types of data
  • Generated data
  • Synthetic data
  • Commercial/public data
4.3 Explain data ingestion and storage concepts
  • Infrastructure requirements
  • Data formats
  • Streaming
  • Batching
  • Pipeline implementation
  • Orchestration/automation
  • Persistence
  • Refresh cycles
  • Archiving
  • Data lineage
4.4 Given a scenario, implement common data-wrangling techniques
  • Merging/combining
  • Cleaning
  • Data errors
  • Outliers
  • Data flattening
  • Imputation types
  • Ground truth labeling
4.5 Given a scenario, implement best practices throughout the data science life cycle
  • Data science workflow models
  • Version control
  • Integrated development environment (IDE)
  • Dependency licensing
  • Access via application programming interface (API)
  • Process documentation
  • Clean code methods
  • Unit test writing
4.6 Explain the importance of DevOps and MLOps principles in data science
  • Data replication
  • Continuous Integration/continuous deployment (CI/CD)
  • Model deployment
  • Container orchestration
  • Virtualization
  • Code isolation
  • Model performance monitoring
  • Model validation
4.7 Compare and contrast various deployment environments
  • Containerization
  • Cloud deployment
  • Cluster deployment
  • Hybrid deployment
  • Edge deployment
  • On-premises deployment
5.1 Compare and contrast optimization concepts
  • Constrained optimization
  • Unconstrained optimization
5.2 Explain the use and importance of natural language processing (NLP) concepts
  • Tokenization/bag of words
  • Word embeddings
  • Term frequency-inverse document frequency (TF-IDF)
  • Document term matrix
  • Edit distance
  • Large language models
  • Text preparation
  • Topic modeling
  • Disambiguation
  • NLP applications
5.3 Explain the use and importance of computer vision concepts
  • Optical character recognition
  • Object/semantic segmentation
  • Object detection
  • Tracking
  • Sensor fusion
  • Data augmentation
5.4 Explain the purpose of other specialized applications in data science
  • Graph analysis/graph theory
  • Heuristics
  • Greedy algorithms
  • Reinforcement learning
  • Event detection
  • Fraud detection
  • Anomaly detection
  • Multimodal machine learning
  • Optimization for edge computing
  • Signal processing
Length of exam 165 minutes
Number of questions 90 questions
Question format Multiple-Choice questions and Performance based
Passing grade Pass/Fail only; no scaled score
Languages English
Testing center Authorized Pearson VUE Test Centre or remote proctoring

Description

CompTIA DataAI is the premier certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science. DataAI equips you with the skills to precisely and confidently demonstrate expertise in handling complex data sets, implementing data-driven solutions, and driving business growth through insightful data interpretation.

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