CompTIA DataAI

Course code: COMPDAI

CompTIA DataAI (formerly DataX) 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.

Price of the CompTIA DataAI certification exam is not included in the price of the course.

1 440 EUR

1 742 EUR including VAT

The earliest date from 16.03.2026

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Course dates
Course description
Target group
Course structure
Certification

Course dates

Starting date: 16.03.2026

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

Starting date: 25.05.2026

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

Starting date: 13.07.2026

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

Starting date: 21.09.2026

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

Starting date: 23.11.2026

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

Starting date: Upon request

Type: In-person/Virtual

Course duration: 5 days

Language: en/cz

Price without VAT: 1 440 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	16.03.2026		In-person/Virtual	5 days	en/cz	1 440 EUR	Register

	25.05.2026		In-person/Virtual	5 days	en/cz	1 440 EUR	Register

	13.07.2026		In-person/Virtual	5 days	en/cz	1 440 EUR	Register

	21.09.2026		In-person/Virtual	5 days	en/cz	1 440 EUR	Register

	23.11.2026		In-person/Virtual	5 days	en/cz	1 440 EUR	Register

	Upon request		In-person/Virtual	5 days	en/cz	1 440 EUR	Register
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Course description

Apply mathematical and statistical methods appropriately, including data processing, cleaning, statistical modeling, linear algebra, and calculus concepts.
Utilize appropriate analysis and modeling methods to make justified model recommendations for modeling, analysis, and outcomes.
Implement machine learning models and understand deep learning concepts to advance data science capabilities.
Implement data science operations and processes effectively to support organizational goals.
Demonstrate an understanding of industry trends and specialized applications of data science in various fields.

Target group

CompTIA DataAI certification is ideal for professionals seeking to validate expert-level data science and analysis skills, regardless of vendor tools. It is designed for data analysts, business intelligence professionals, and anyone involved in data-driven decision-making across industries.

Course structure

Mathematics and statistics

Statistical methods: applying t-tests, chi-squared tests, analysis of variance (ANOVA), hypothesis testing, regression metrics, gini index, entropy, p-value, receiver operating characteristic/area under the curve (ROC/AUC), akaike information criterion/bayesian information criterion (AIC/BIC), and confusion matrix.
Probability and modeling: explaining distributions, skewness, kurtosis, heteroskedasticity, probability density function (PDF), probability mass function (PMF), cumulative distribution function (CDF), missingness, oversampling, and stratification.
Linear algebra and calculus: understanding rank, eigenvalues, matrix operations, distance metrics, partial derivatives, chain rule, and logarithms.
Temporal models: comparing time series, survival analysis, and causal inference.

Modeling, analysis, and outcomes

EDA methods: using exploratory data analysis (EDA) techniques like univariate and multivariate analysis, charts, graphs, and feature identification.
Data issues: analyzing sparse data, non-linearity, seasonality, granularity, and outliers.
Data enrichment: applying feature engineering, scaling, geocoding, and data transformation.
Model iteration: conducting design, evaluation, selection, and validation.
Results communication: creating visualizations, selecting data, avoiding deceptive charts, and ensuring accessibility.

Machine learning

Foundational concepts: applying loss functions, bias-variance tradeoff, regularization, cross-validation, ensemble models, hyperparameter tuning, and data leakage.
Supervised learning: applying linear regression, logistic regression, k-nearest neighbors (KNN), naive bayes, and association rules.
Tree-based learning: applying decision trees, random forest, boosting, and bootstrap aggregation (bagging).
Deep learning: explaining artificial neural networks (ANN), dropout, batch normalization, backpropagation, and deep-learning frameworks.
Unsupervised learning: explaining clustering, dimensionality reduction, and singular value decomposition (SVD).

Operations and processes

Business functions: explaining compliance, key performance indicators (KPIs), and requirements gathering.
Data types: explaining generated, synthetic, and public data.
Data ingestion: understanding pipelines, streaming, batching, and data lineage.
Data wrangling: implementing cleaning, merging, imputation, and ground truth labeling.
Data science life cycle: applying workflow models, version control, clean code, and unit tests.
DevOps and MLOps: explaining continuous integration/continuous deployment (CI/CD), model deployment, container orchestration, and performance monitoring.
Deployment environments: comparing containerization, cloud, hybrid, edge, and on-premises deployment.

Specialized applications of data science

Optimization: comparing constrained and unconstrained optimization.
NLP concepts: explaining natural language processing (NLP) techniques like tokenization, embeddings, term frequency-inverse document frequency (TF-IDF), topic modeling, and NLP applications.
Computer vision: explaining optical character recognition (OCR), object detection, tracking, and data augmentation.
Other applications: explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.

Certification

Exam version: V1
Exam series code: DY0-001
Launch date: July 25, 2024
Number of questions: maximum of 90 questions
Types of questions: multiple-choice and performance-based
Duration: 165 minutes
Passing score: pass/fail only (no scaled score)
Language: English and Japanese
Recommended experience: 5+ years in data science or a similar role
Retirement: usually three years after launch (estimated 2027)