- CompTIA DataX pathway and how it differs from Data+.
- Map of DY0-001 exam domains and weights.
- Exam logistics: maximum of 90 questions (MC and performance-based), 165 minutes, pass/fail.
- Study approach for advanced practitioner topics.
CompTIA DataX (DY0-001)
CompTIA DataX Certification Training Course
Apply mathematics and statistics, modeling, machine learning, MLOps processes, and specialized data science techniques — CompTIA DataX (DY0-001).
Course focus: Probability and linear algebra for data science, exploratory analysis and model evaluation, supervised and unsupervised machine learning, data wrangling and MLOps, and specialized applications such as NLP and computer vision awareness aligned to CompTIA DataX exam objectives.
Course Overview
Advanced data science skills — not entry-level data literacy alone.
Accumentum’s CompTIA DataX Certification Training Course prepares experienced data science practitioners for the CompTIA DataX exam (DY0-001). The credential validates end-to-end skills from mathematics and statistics through modeling, machine learning, operations, and specialized applications.
This course is advanced. CompTIA recommends five or more years in data science or a similar role. It is not CompTIA Data+; Data+ is a separate earlier data analytics credential.
Course Objectives
What you will be able to apply as a data science practitioner.
01
Mathematics and statistics
Probability, distributions, hypothesis testing concepts, linear algebra, calculus basics, and temporal models (17%).
02
Modeling and outcomes
EDA, data issues, feature engineering, model design/evaluation, and results communication (24%).
03
Machine learning
Supervised, unsupervised, tree-based, and deep learning foundations with loss and leakage awareness (24%).
04
Operations and specialized apps
Data wrangling, MLOps processes, and specialized applications of data science (22% + 13%).
Who Should Attend
- Data scientists, ML engineers, and quantitative analysts preparing for CompTIA DataX.
- Experienced practitioners with several years in data science or a similar role.
- Analytics professionals moving from descriptive work into modeling and ML operations.
- Learners who already hold foundational data credentials and need an advanced practitioner exam map.
Prerequisites
- CompTIA recommends five or more years of experience in data science or a similar role.
- Comfort with statistics, programming for data work, and common ML workflows is expected.
- This training is not CompTIA Data+; Data+ is a separate analytics foundation credential.
- Hands-on practice with modeling, evaluation, and MLOps supports DY0-001 exam readiness.
Pathway Map
Eight modules mapped to CompTIA DataX DY0-001 domains.
Exam domains: Mathematics and Statistics 17%; Modeling, Analysis, and Outcomes 24%; Machine Learning 24%; Operations and Processes 22%; Specialized Applications of Data Science 13%.
Course Content
- Statistical methods and hypothesis-testing concepts used in modeling.
- Probability, distributions, skewness, kurtosis, PDF/PMF/CDF, sampling, and stratification.
- Linear algebra and calculus concepts: matrices, eigenvalues, distance metrics, derivatives, logs.
- Temporal models: time series, survival analysis, and causal inference awareness (domain 1.0 — 17%).
- Exploratory data analysis methods and feature identification.
- Sparse data, missingness, and outliers.
- Feature engineering, scaling, geocoding, and transformations.
- Modeling, analysis, and outcomes skill area begins (domain 2.0 — 24%).
- Model design, evaluation, selection, and tuning concepts.
- Metrics such as confusion-matrix measures and related evaluation ideas at objective level.
- Visualizations and data storytelling without deceptive charts.
- Translate model outcomes for stakeholders.
- Loss functions, overfitting/underfitting awareness, and data leakage.
- Statistical supervised learning concepts.
- Tree-based learning: decision trees, bagging, and related ensemble ideas.
- Domain 3.0 — 24% of the exam.
- Neural network and deep-learning framework awareness.
- Unsupervised learning: clustering and association concepts.
- When to choose supervised vs unsupervised approaches.
- Connect ML choices to business outcomes.
- Data wrangling for production-ready pipelines.
- Data science workflow and MLOps practices.
- Reproducibility, monitoring, and process controls at objective level.
- Domain 4.0 — 22% of the exam.
- Specialized applications outlined in CompTIA DataX objectives (domain 5.0 — 13%).
- Awareness of NLP, computer vision, and generative techniques as specialized applications.
- Selecting techniques for domain-specific problems.
- Tie specialized methods back to math, modeling, ML, and operations.
Course Features
Designed for CompTIA DataX practitioner work.
Exam-domain map
Eight modules aligned to CompTIA DataX DY0-001 domains and published weights.
Advanced scope
Math, modeling, ML, MLOps, and specialized applications — not Data+ analytics foundations alone.
Public exam facts
Maximum of 90 questions; multiple-choice and performance-based; 165 minutes; pass/fail. No invented scaled score.
Experience alignment
Built for practitioners with multi-year data science experience.
Certification Exam
Prepare for the official CompTIA DataX (DY0-001) exam.
DataX Exam Readiness
Accumentum DataX training prepares experienced practitioners for the CompTIA DataX certification exam (DY0-001).
The CompTIA DataX exam series code is DY0-001. Public facts: maximum of 90 questions; multiple-choice and performance-based; length of test 165 minutes; scoring is pass/fail.
Exam domains: Mathematics and Statistics (17%); Modeling, Analysis, and Outcomes (24%); Machine Learning (24%); Operations and Processes (22%); Specialized Applications of Data Science (13%).
Frequently Asked Questions
CompTIA DataX training with Accumentum: scope, audience, domains, and public exam facts.
No. This is CompTIA DataX (DY0-001), an advanced data science certification. Data+ is a separate analytics foundation credential.
Data scientists, ML engineers, quantitative analysts, and experienced practitioners (CompTIA recommends 5+ years) preparing for DY0-001.
Apply math/stats for data science, run EDA and model evaluation, use core ML approaches, operate wrangling/MLOps processes, and map specialized applications to problems.
Eight modules: toolkit; mathematics and statistics; EDA and enrichment; model design and outcomes; ML foundations; deep learning and unsupervised methods; operations and MLOps; specialized applications.
Math and statistics 17%, modeling/analysis/outcomes 24%, machine learning 24%, operations and processes 22%, specialized applications 13%.
Exam DY0-001. Maximum of 90 questions. Multiple-choice and performance-based. 165 minutes. Pass/fail scoring.
No. Module topics follow CompTIA’s published DataX exam objectives and public test details.
Use Accumentum registration for CompTIA DataX training dates and enrollment support.
Enrollment
Enroll in CompTIA DataX Certification Training with Accumentum.
Enroll in Accumentum’s CompTIA DataX Certification Training Course to prepare for exam DY0-001. For course dates and enrollment support, visit Accumentum’s registration page linked below.
Prepare for CompTIA DataX practitioner work.
Apply advanced data science across math, modeling, ML, and MLOps. Not Data+.