CompTIA DataAI Certification Training Course
Length
5 days / 5 weeks
Price
$1699
Days
Mon - Fri
Why Choose This Course
CompTIA DataAI is an instructor-led, exam‑aligned programme for experienced data professionals who want to validate end‑to‑end data science capability across mathematics and statistics, modelling and communication, machine learning, and production operations. The certification, introduced as DataAI V1 with exam code DY0‑001, confirms you can select appropriate statistical and mathematical methods, build and evaluate models, and deploy solutions within workflows that meet organisational and compliance needs. It reflects current industry practice, where data science, ML and MLOps are integrated to deliver measurable outcomes.
The course covers rigorous statistical testing and probability concepts, linear algebra and calculus ideas used in modelling, and temporal analysis for time‑dependent problems. You will learn to plan and run exploratory data analysis, address data quality and granularity issues, engineer features, and iterate models with defensible selection and validation. Communication is emphasised through clear visualisation choices and accessibility considerations so stakeholders can act on findings.
Training is suitable for senior analysts, data scientists, and engineers who need a structured pathway to exam readiness while focusing on practical skills that transfer to enterprise environments. The exam is pass/fail and is recommended for professionals with five or more years of experience in data science or similar roles.
Prerequisites
- There are no formal prerequisites for this course. CompTIA recommends 5+ years in data science or a similar role.
Exam
Candidates can achieve this certification by passing the following exam(s).
- CompTIA DataAI (V1), exam code DY0‑001
Books
- CompTIA DataAI course material included.
Delivery
- Live virtual online training attend in real-time from anywhere
Skills Gained
- Choose and apply statistical tests (t‑tests, chi‑square, ANOVA) and interpret p‑values and confidence intervals appropriately.
- Use regression and classifier metrics (e.g., ROC/AUC, confusion‑matrix measures) to evaluate model performance.
- Characterise distributions, skewness, kurtosis, and types of missingness; apply stratification and oversampling when valid.
- Apply linear algebra operations and calculus concepts (eigenvalues, matrix ops, partial derivatives, chain rule) in modelling.
- Compare temporal and causal approaches, including time series and survival analysis scenarios.
- Conduct EDA, address data issues (sparsity, non‑linearity, seasonality, granularity, outliers), and engineer features.
- Build, tune and validate models using cross‑validation, regularisation, and ensemble strategies.
- Implement supervised, unsupervised and tree‑based methods; explain deep learning components and training practices.
- Design pipelines for streaming and batch ingestion with data lineage and documentation.
- Perform data cleaning, merging, imputation, and ground‑truth labelling.
- Apply workflow models, version control, clean code and unit tests across the data science lifecycle.
- Operationalise models using CI/CD, container orchestration, and monitoring across cloud, hybrid, edge and on‑premises.
- Communicate results with accessible, non‑deceptive visualisations tailored to stakeholders.
- Understand specialised applications: NLP, computer vision, optimisation, graph and anomaly detection.
Audience
- Senior data analysts, data scientists, ML engineers and data engineers working on analytics and AI‑enabled initiatives.
- Professionals responsible for designing, evaluating, deploying and monitoring data‑driven solutions in production.
Course Schedule & Pricing
Choose the schedule that fits your life — all options include full course materials & certification support
Full-time immersion for rapid certification readiness.
Balance your career while you upgrade your skills.
Maximum flexibility for busy working professionals.
Outline
Mathematics and statistics
- Hypothesis testing and experimental design concepts
- t‑tests, chi‑square, ANOVA use cases and pitfalls
- Regression metrics: RMSE, R²/Adj‑R², F statistic applications
- ROC/AUC and confusion‑matrix measures (precision, recall, F1, etc.)
- Probability: distributions, PDFs/PMFs/CDFs, skewness and kurtosis
- Missingness types, oversampling and stratification
- Linear algebra: matrix operations, eigenvalues, distance metrics
- Calculus: partial derivatives, chain rule, logs/exponentials in ML
- Temporal analysis: time series comparison and survival analysis basics
Modelling, analysis and outcomes
- EDA workflows: univariate and multivariate analysis, profiling
- Handling sparsity, non‑linearity, seasonality, granularity, outliers
- Feature engineering: scaling, transformation, geocoding
- Iteration: design, evaluation, selection, validation strategies
- Communicating results: chart choice, avoiding deception, accessibility
- Machine learning
- Loss functions, bias–variance, regularisation and data leakage controls
- Supervised learning: linear/logistic regression, KNN, naive Bayes, association rules
- Tree‑based learning: decision trees, random forests, boosting, bagging
- Deep learning foundations: ANN concepts, backprop, batch norm, dropout
- Unsupervised learning: clustering, dimensionality reduction, SVD
Operations and processes
- Business context: KPIs, compliance, requirements gathering
- Data sources and types: generated, synthetic, public
- Ingestion and lineage: pipelines, streaming vs. batching
- Data wrangling: cleaning, merging, imputation, labelling
- Lifecycle practices: workflow models, version control, clean code, unit tests
- DevOps/MLOps: CI/CD, container orchestration, performance monitoring
- Environments: cloud, hybrid, edge, on‑premises deployment trade‑offs
Specialised applications
- Optimisation: constrained vs. unconstrained scenarios
- NLP: tokenisation, embeddings, TF‑IDF, topic modelling, applications
- Computer vision: OCR, detection, tracking, augmentation basics
- Graph analysis, reinforcement learning, fraud/anomaly detection, signal processing use cases
Terms & Conditions
Frequently Asked Questions (FAQ's)
What experience level is DataAI intended for?
How is DataAI different from CompTIA Data+?
Does the DataX to DataAI name change affect current candidates?
Our Partnership
Reliable certification testing is vital for validating professional skills in today’s tech-driven world. As a Pearson VUE Authorised Centre, we provide a secure environment for globally recognised IT exams. This partnership ensures convenient access to certifications with the highest standards of integrity and accuracy.
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Average annual salary for IT support specialists worldwide; entry-level starts around US$40K–$50K.
78%
Employers globally prefer candidates with recognised IT certifications like A+.
11%
Projected global growth in IT support roles by 2032.
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Estimated global IT market size in 2024, continuing strong growth.
100,000+
Unfilled IT support roles worldwide, creating high demand for A+ certified professionals.
95%
Student satisfaction rate reported by leading global IT training programs.
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