+971 54 326 9878
+971 4 385 4500
Type and hit enter to Search
×
Featured Image

DataX

  • 4.8(11,203 Rating)

Duration

5 days

Language

English / Arabic

Batch Options

4 hours & 8 hours

Upcoming Batch

10 November 2025
Overview Schedule Dates Course Content FAQs

Course Overview

CompTIA DataX is the premier certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science. DataX 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.

Skills Learned

  • 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.

Exam Details

  • Exam version: V1
  • Exam series code: DY0-001
  • Launch date: July 25, 2024
  • Number of questions: a 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)

Career Path

Flexible Training Options to
Meet Your Needs

We understand that flexibility is key to effective learning and development, especially in today’s dynamic work environment. That’s why we offer multiple delivery formats for our trainings in UAE. Whether you prefer the interaction of in-person classes, the convenience of live virtual training, or the independence of self-paced online learning, we have a solution tailored to your schedule. Our goal is to make professional growth accessible to everyone, allowing you to upskill without compromising your other commitments.

Online Instructor Led

Learn from the comforts of your home/office with our expert trainers via online platforms.

Classroom Training

Face-to-face physical classroom training with max interactions at our 5* training venues.

Onsite Training

Learn in your own environment from our expert trainers for maximum impact.

Overseas Training

Choose your desired country for learning with our expert trainers.

Target Audiance

  • Data Analyst
  • E-commerce Analyst
  • Data Scientist
  • IT Manager
This course includes:
  • Soft copy of Courseware
  • Certificate of Course Complition
  • Experienced and Professional Trainer
  • Lab and Exercises During Training
  • Sample Questions for Exam Prepration
  • Mock Test on the Last Day of Training
  • Exam Registration Guidance
  • 5-Star Training Environmment
Request More Information
Please enable JavaScript in your browser to complete this form.
Name

Schedule Dates

10 November 2025 - 14 November 2025
DataX
16 February 2026 - 20 February 2026
DataX
18 May 2026 - 22 May 2026
DataX
24 August 2026 - 28 August 2026
DataX

Course Content

  • 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.

  • 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.

  • 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).

  • 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.

  • 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.

FAQs

CompTIA DataX is designed to validate advanced, cross-functional data skills, including analytics, governance, integration, and strategy, bridging technical expertise with business intelligence.

  • Data+ focuses on analytics and interpretation.
  • DataSys+ addresses database administration and system management.
  • DataX takes a holistic approach, covering enterprise-wide data management, governance, strategy, and advanced analytics.

While no mandatory prerequisites exist, learners should ideally have 3–5 years of experience in data analytics, business intelligence, or IT systems, along with familiarity with SQL, BI tools, and data governance principles.

Yes. DataX maintains a vendor-neutral approach but references widely used platforms like AWS, Azure, Google Cloud, Power BI, and Tableau for applied examples.

DataX emphasises global regulatory standards such as GDPR, HIPAA, ISO 27001, and CCPA, ensuring professionals can manage compliant data environments.

Yes. While not a deep ML/AI certification, DataX introduces AI-assisted analytics, predictive modelling, and automation for data-driven decision-making.

Yes. It provides a strong foundation for certifications such as Certified Analytics Professional (CAP), DAMA CDMP, AWS Big Data Specialty, and Microsoft Certified Data Engineer.

Related Courses

Looking to expand your knowledge further? Explore our carefully selected related courses that complement this program. Whether you're interested in delving deeper into cybersecurity, networking, data management, or quality assurance, these courses are designed to enhance your skills and broaden your expertise. Choose the next step in your professional development and continue building a competitive edge in the IT industry.

View All

Digital Literacy Pro

DataSys+

Windows Client Pro

Business Essentials