CompTIA DataX Exam Syllabus

DataX PDF, DY0-001 Dumps, DY0-001 PDF, DataX VCE, DY0-001 Questions PDF, CompTIA DY0-001 VCE, CompTIA DataX Dumps, CompTIA DataX PDF Use this quick start guide to collect all the information about CompTIA DataX (DY0-001) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the DY0-001 CompTIA DataX exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual CompTIA DataX certification exam.

The CompTIA DataX certification is mainly targeted to those candidates who want to build their career in Data and Analytics domain. The CompTIA DataX exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of CompTIA DataX.

CompTIA DataX Exam Summary:

Exam Name	CompTIA DataX
Exam Code	DY0-001
Exam Price	$509 (USD)
Duration	165 mins
Number of Questions	90
Passing Score	Pass/Fail
Schedule Exam	Pearson VUE
Sample Questions	CompTIA DataX Sample Questions
Practice Exam	CompTIA DY0-001 Certification Practice Exam

CompTIA DY0-001 Exam Syllabus Topics:

Topic	Details
Mathematics and Statistics - 17%
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 R2 Adjusted R2 Root mean square error (RMSE) F statistic - Gini index - Entropy - Information gain - p value - Type I and Type II errors - Receiver operating characteristic/area under the curve (ROC/AUC) - Akaike information criterion/Bayesian information criterion (AIC/BIC) - Correlation coefficients Pearson correlation Spearman correlation - Confusion matrix Classifier performance metrics 1. Accuracy 2. Recall 3. Precision 4. F1 score 5. Matthews Correlation Coefficient (MCC) Central limit theorem Law of large numbers
Explain probability and synthetic modeling concepts and their uses.	- Distributions Normal Uniform Poisson t Binomial Power law - Skewness - Kurtosis - Heteroskedasticity vs. homoskedasticity - Probability density function (PDF) - Probability mass function (PMF) - Cumulative distribution function (CDF) - Probability Monte Carlo simulation Bootstrapping Bayes’ rule Expected value - Types of missingness Missing at random Missing completely at random Not missing at random - Oversampling - Stratification
Explain the importance of linear algebra and basic calculus concepts.	- Linear algebra Rank Span Trace Eigenvalues/eigenvectors Basis vector Identity matrix Matrix and vector operations 1. Matrix multiplication 2. Matrix transposition 3. Matrix inversion 4. Matrix decomposition Distance metrics 1. Euclidean 2. Radial 3. Manhattan 4. Cosine - Calculus Partial derivatives Chain rule Exponentials Logarithms
Compare and contrast various types of temporal models.	- Time series Autoregressive (AR) Moving average (MA) Autoregressive integrated moving average (ARIMA) - Longitudinal studies - Survival analysis Parametric Non-parametric - Causal inference Directed acyclic graphs (DAGs) Difference-in-differences A/B testing of treatment effects Randomized controlled trials
Modeling, Analysis, and Outcomes - 24%
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 Bar plot Scatter plot Box and whisker plot Line plot Violin plot Heat map Correlation plot Histogram Sankey diagram Quartile-Quartile (Q-Q) plot Density plot Scatter plot matrix - Feature type identification Categorical variables Discrete variables Continuous variables Ordinal variables Nominal variables Binary variables
Given a scenario, analyze common issues with data.	- Common issues Sparse data 1. Sparse matrix 2. Sparse vectors Non-linearity Non-stationarity Lagged observations Difference observations Multicollinearity Seasonality Granularity misalignment Insufficient features Multivariate outliers
Given a scenario, apply data enrichment and augmentation techniques.	- Feature engineering - Data transformation One-hot encoding Label encoding Cross-terms Linearization 1. Logarithmic 2. Exponential Box-Cox transformation Normalization Binning Ratios Pivoting - Geocoding - Scaling - Standardization - Additional data sources Data augmentation Data sets Synthetic data
Given a scenario, conduct a model design iteration process.	- Design and specifications Constraints 1. Time 2. Resource 3. Physical hardware 4. Cost - Performance evaluation Statistical metrics Training time and cost Inference performance over time Model diagnostic plots 1. Residual vs. fitted values - Model selection Literature review Hyperparameter tuning Experiment tracking Model architecture iteration - Requirements validation
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 Differentiate between business needs vs. business wants vs. reality
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 Types of business executive stakeholders Types of business domain stakeholders Types of peers/professional stakeholders - Consider data types, dimensions, and levels of aggregation to produce appropriate visualizations/reports - Avoid unintentionally deceptive charting and reporting - Chart accessibility Font choice and size Color choice Content tagging Effectiveness for accessibility Government regulatory implications - Data and model documentation Code documentation Data dictionary Metadata Change descriptions
Machine Learning - 24%
Given a scenario, apply foundational machine-learning concepts.	- Loss function Variance minimization - Bias-variance tradeoff Overfitting Underfitting - Variable/feature selection Feature importance Multicollinearity Correlation matrix Variance inflation factor (VIF) - Class imbalance and mitigations Oversampling the minority class Undersampling the majority class Synthetic minority oversampling technique (SMOTE) - Regularization - Cross-validation k-fold - The curse of dimensionality - Occam’s razor/law of parsimony - In sample vs. out of sample - Interpolation vs. extrapolation - Ensemble models - Hyperparameter tuning Grid search Random search - Classifiers Binary classifiers Multiclass (multinomial) classifiers - Recommender systems Collaborative filtering Alternating least squares (ALS) Similarity-based - Regressors - Embeddings - Post hoc model explainability Global explanations Local explanations - Interpretable models - Model drift causes Data drift Concept drift - Data leakage Transfer learning Cold start problem
Given a scenario, apply appropriate statistical supervised machine-learning concepts.	- Linear regression models Ordinary least squares (OLS) Assumptions Weighted least squares Ridge Least Absolute Shrinkage and Selection Operator (LASSO) Elastic net - Logistic regression models Probit Logit - Linear discriminant analysis - Quadratic discriminant analysis (QDA) - Association rules Confidence Lift Reinforcement Support - Naive Bayes
Given a scenario, apply tree-based supervised machine-learning concepts.	- Decision trees - Random forest - Boosting Gradient boosting XGBoost - Bootstrap aggregation (bagging)
Explain concepts related to deep learning.	- Artificial neural network architecture Perceptron Artificial neuron Multilayer perceptron Activation functions 1. Rectified linear unit (ReLU) 2. Sigmoid 3. Tanh 4. Softmax Layer types 1. Input 2. Hidden 3. Pooling 4. Output - Dropout - Batch normalization - Early stopping - Schedulers - Back propagation - One-shot learning - Zero-shot learning - Few-shot learning - Deep-learning frameworks PyTorch TensorFlow/Keras AutoML - Optimizers Adam optimizer Momentum Root Mean Square Propagation (RMSprop) Stochastic gradient descent Mini-batch - Model types Convolutional neural network (CNN) Recurrent neural network (RNN) Long short-term memory (LSTM) Generative adversarial networks (GANs) Autoencoders Transformers
Explain concepts related to unsupervised machine learning.	- Clustering k-means 1. Silhouette score/elbow method Hierarchical Density-based spatial clustering analysis with noise (DBSCAN) - Dimensionality reduction Principal component analysis (PCA) t-distributed stochastic neighbor embedding (t-SNE) Uniform manifold approximation and projection (UMAP) - k-nearest neighbors (KNN) - Singular value decomposition (SVD)
Operations and Processes - 22%
Explain the role of data science in various business functions.	- Compliance, security, and privacy Personally identifiable information (PII) Proprietary Anonymizing sensitive data Data obfuscation Data use regulations - Measures, metrics, and key performance indicators (KPIs) - Requirements gathering Make recommendations based on cost-benefit analyses Translate business need to the most appropriate solution Relevant range of application
Explain the process of and purpose for obtaining different types of data.	- Generated data Survey Administrative Sensor Transactional Experimental Data-generating process - Synthetic data Costs and benefits Creation process Limitations Sampling Rationale - Commercial/public data Costs and benefits Availability Licensing Restrictions
Explain data ingestion and storage concepts.	- Infrastructure requirements Resource sizing Graphics processing unit (GPU)/Tensor Processing Unit (TPU) - Data formats Common formats 1. Comma-separated values (CSV) 2. JavaScript Object Notation (JSON) 3. Parquet Compressed format Structured storage Semi-structured storage Unstructured storage - Streaming - Batching - Pipeline implementation - Orchestration/automation - Persistence - Refresh cycles - Archiving - Data lineage
Given a scenario, implement common data-wrangling techniques.	- Merging/combining Defining keys Data matching 1. Match rates 2. Fuzzy join Observation tracking Union Intersection Types of joins - Cleaning Date/time standardization Regular expressions Deduplication Unit conversion/standardization Missing codes - Data errors Idiosyncratic Systematic - Outliers Identification Winsorization/cut points Error vs. valid data point - Data flattening Extensible Markup Language (XML) JSON - Imputation types - Ground truth labeling
Given a scenario, implement best practices throughout the data science life cycle.	- Data science workflow models Cross-Industry Standard Protocol for Data Mining (CRISP-DM) Data Management Association (DAMA) - Version control Code Data Hyperparameters Models - Integrated development environment (IDE) - Dependency licensing - Access via application programming interface (API) Data access and retrieval Model endpoint/model services - Process documentation Markdown Docstring Appropriate code commenting Reference data and documentation - Clean code methods - Unit test writing
Explain the importance of DevOps and MLOps principles in data science.	- Data replication - Continuous integration/continuous deployment (CI/CD) pipelines - Model deployment - Container orchestration - Virtualization - Code isolation - Model performance monitoring - Model validation Online Offline Model A/B testing
Compare and contrast various deployment environments.	- Containerization - Cloud deployment - Cluster deployment - Hybrid deployment - Edge deployment - On-premises deployment
Specialized Applications of Data Science - 13%
Compare and contrast optimization concepts.	- Constrained optimization Network topology 1. Traveling salesman Scheduling Linear solvers 1. Simplex method Non-linear solvers Pricing Resource allocation Bundling Boundary cases - Unconstrained optimization One-armed bandit Multi-armed bandit Finding local maxima or minima
Explain the use and importance of natural language processing (NLP) concepts.	- Tokenization/bag of words - Word embeddings n-grams - Term frequency-inverse document frequency (TF-IDF) - Document term matrix - Edit distance - Large language models Word2vec GloVe - Text preparation Lemmatization Stop words Augmenters String indexing Stemming Part-of-speech (POS) tagging - Topic modeling Latent Dirichlet Allocation - Disambiguation - NLP applications Sentiment analysis Question-and-answer/dialogue Named-entity recognition (NER) 1. Auto-tagging Text generation Matching models Speech recognition and generation Text summarization Natural language understanding (NLU) Natural language generation (NLG)
Explain the use and importance of computer vision concepts.	- Optical character recognition - Object/semantic segmentation - Object detection - Tracking - Sensor fusion - Data augmentation Filter application Rotation Occlusion Spurious noise Flipping Scaling Holes Masking Cropping
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

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CompTIA DataX Exam Syllabus

CompTIA DataX Exam Summary:

CompTIA DY0-001 Exam Syllabus Topics:

Mathematics and Statistics - 17%

Modeling, Analysis, and Outcomes - 24%

Machine Learning - 24%

Operations and Processes - 22%

Specialized Applications of Data Science - 13%

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