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CompTIA DataX (DY0-001) Online Training

Get hands-on with AI, MLOps, and machine learning engineering in this CompTIA DataX (DY0-001) training for professional data scientists. Learn core data science skills, including statistics, data modeling, and machine learning. Deploy deep learning models, build pipelines, and apply computer vision in real-world environments. This online DataX training prepares you to support advanced data science workflows and pass the CompTIA DataX certification exam. Train for the future of IT with cutting-edge AI skills.

Updated July 2025

38Skills
309Videos
32h 27mTotal
309 videos32h 27m

Who This Course Is For

This training is for IT pros, data analysts, and developers preparing for the CompTIA DataX certification and roles in data science, machine learning, and MLOps. It builds advanced skills in modeling, statistics, and AI deployment.

Skills Your Team Will Gain

  • Designing and managing data science pipelines for machine learning
  • Applying linear algebra and calculus in deep learning models
  • Mining, preparing, and transforming data for AI applications
  • Deploying computer vision and deep learning models in production
  • Applying MLOps best practices to automate model lifecycle
  • Communicating complex AI insights to technical and non-technical teams

Course Curriculum

  • Premium skill.Explore Data Science and Resources for DataX51m
  • Premium skill.Assess your Data Science Knowledge Gaps for DataX44m
  • Premium skill.Explore Data Science Tools and Lifecycles46m
  • Premium skill.Examine Data Science Code Syntax and Workflows46m
  • Premium skill.Review Best Practices, Composition, & Requirements47m
  • Premium skill.Explore Change Using Calculus for Data Science45m
  • Premium skill.Apply Probability & Statistics for Data Science46m
  • Premium skill.Perform Statistical Testing for Data Science55m
  • Premium skill.Apply Linear Algebra to Data Science Problems49m
  • Premium skill.Examine Key Data Sources for Data Science45m
  • Premium skill.Explore Data Ingestion & Storage for Data Science45m
  • Premium skill.Explore Data Analysis & Variables for Data Science49m
  • Premium skill.Explore Multivariate Analysis and Quality in DS44m
  • Premium skill.Explore Data Transformation for Data Science53m
  • Premium skill.Augment and Feature Engineer Data for Data Science1h 4m
  • Premium skill.Explore Statistical and Machine Learning Models1h 5m
  • Premium skill.Validate Models and Communicate Data Effectively1h 4m
  • Premium skill.Analyze Model Deployment and MLOps1h 3m
  • Premium skill.Build a Supervised Learning Regression Model48m
  • Premium skill.Build a Supervised Learning Classification Model48m
  • Premium skill.Explore Quadratic and Linear Discriminant Analysis45m
  • Premium skill.Classify Data with the Naive Bayes Algorithm48m
  • Premium skill.Explore Decision Trees and Ensemble Methods1h 8m
  • Premium skill.Analyze Core Artificial Neural Network Concepts1h 1m
  • Premium skill.Explore ANN Training Techniques & Gradient Descent50m
  • Premium skill.Apply Neural Network Concepts to Deep Learning58m
  • Premium skill.Compare PyTorch and TensorFlow for Deep Learning47m
  • Premium skill.Explore Natural Language Processing (NLP) Concepts46m
  • Premium skill.Explore Tokenization, Gen AI, and LLMs in NLP52m
  • Premium skill.Prepare Text for Natural Language Processing51m
  • Premium skill.Use Advanced Text Preparation for Machine Learning49m
  • Premium skill.Apply NLP: One-Hot, BoW, TF-IDF, Word2Vec & GloVe54m
  • Premium skill.Explore Foundations of Optimization47m
  • Premium skill.Compare Linear and Nonlinear Programming Methods48m
  • Premium skill.Explore Specialized Machine Learning Optimization52m
  • Premium skill.Apply Computer Vision for Image Understanding48m
  • Premium skill.Apply Feature Extraction for Image Perception53m
  • Premium skill.Identify Knowledge Gaps with the DataX SkillScan51m

For IT leaders

What IT leaders need to know before assigning this course

For IT Leaders

If your data science team includes junior practitioners, the question isn't whether they have data skills — it's whether those skills are consistent, verifiable, and vendor-neutral. CompTIA’s DY0-001 certification gives IT leaders a standardized benchmark to evaluate those skills across the team.

This training prepares junior data scientists for the DY0-001 exam while building applied competency in statistics, data visualization, machine learning fundamentals, and data mining — skills that translate directly to supporting data-driven decision-making, regardless of the tools your organization uses. That vendor-agnostic foundation matters when you're managing a team that works across multiple platforms or standardizing onboarding for a growing data function.

Common concerns this training addresses:

Team size and role fit. This is associate-level training designed for junior data scientists — whether they're newly hired or experienced practitioners who need formal validation of their skills. It works as an onboarding resource for new hires and as a structured upskilling path for existing team members preparing to move into more advanced roles.

Time investment. Training can be assigned individually or curated into team learning plans, giving managers flexibility to align training with project cycles and business priorities rather than forcing a rigid schedule.

Skill validation and compliance risk. For organizations where data quality, analytical rigor, or reporting accuracy carries compliance weight, having certifiably trained staff is a meaningful risk control. The DY0-001 exam validates that junior data scientists understand not just tool-specific workflows, but the underlying statistical and analytical reasoning behind sound data practice.

Change management. Introducing a certification standard for junior data roles is easier when the training connects exam objectives to practical data science concepts — not just terminology review. That structure helps Team Leads and Training Managers set clear expectations for the time investment and the skills learners are expected to demonstrate.

Team Impact

How this training helps your team succeed

Real-World Impact

Junior data scientists on your team often hit a wall when moving from tool-specific work — running queries in a familiar platform, building charts in Excel — to the kind of vendor-neutral, method-agnostic analysis that business stakeholders actually need. This training closes that gap directly.

After completing this course, your data professionals can:

  • Support business decision-making with appropriate statistical methods — not just pulling numbers, but choosing the right analytical approach for the question being asked
  • Build and interpret visualizations that communicate findings to non-technical stakeholders, reducing the back-and-forth between data teams and department leads
  • Apply modeling and machine learning concepts in simulated, real-world environments — so the skills transfer to actual work rather than staying theoretical
  • Validate a vendor-neutral foundation that holds up regardless of whether your org runs on Python, R, Tableau, or a mix of tools

For teams onboarding junior data scientists, this course establishes a consistent baseline. A new hire who's completed this training arrives with a shared vocabulary and a proven ability to handle core data tasks — cleaning, modeling, visualizing — without needing hand-holding on fundamentals.

For experienced junior data scientists already contributing to your team, the CompTIA DataAI (DY0-001) certification gives their practical experience a credential internal stakeholders can recognize. It's the difference between "this analyst is good with data" and "this analyst has demonstrated competency across statistics, data manipulation, and machine learning concepts."

If your team supports data-driven reporting, forecasting, or operational analysis, this training directly develops the people doing that work.

After completion

Knowledge & ability your team will gain

Knowledge

  • Data science tools, lifecycles, code syntax, workflows, requirements, and composition best practices
  • Core mathematical foundations for data science, including calculus, probability, statistics, statistical testing, and linear algebra
  • Data sources, ingestion, storage, variables, quality, transformation, augmentation, and feature engineering
  • Statistical and machine learning model types, including regression, classification, discriminant analysis, Naive Bayes, decision trees, ensembles, and neural networks
  • NLP, generative AI and LLM concepts, tokenization, text preparation, and common text representation methods
  • Optimization foundations, linear and nonlinear programming, specialized ML optimization, and computer vision concepts

Ability

  • Assess data science knowledge gaps against CompTIA DataAI/DY0-001 topics
  • Prepare and transform data for analysis and machine learning workflows
  • Select and compare appropriate statistical, supervised learning, deep learning, NLP, and computer vision approaches
  • Validate models and communicate data findings more effectively to stakeholders
  • Recognize deployment and MLOps considerations for putting models into operational use
  • Use certification-aligned review to prepare for the CompTIA DataAI (DY0-001) exam

This course is included with every subscription

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