This certification course is ideal for:

• Data scientists, machine learning engineers, senior analysts, quantitative researchers
  
• Professionals with multiple years of experience in data-science, analytics, or ML who want to formalize their expertise
  
• Technical leaders or specialists who design and deploy data pipelines, ML systems, deep learning models
  
• Individuals preparing to take the CompTIA DataX (DY0-001) exam
  

Recommended experience / background:

• The certification is recommended for professionals with 5+ years of experience in data science or a related role.
  
• Strong foundation in mathematics (linear algebra, calculus), statistics, modeling, programming (e.g., Python), machine learning, data operations and processes.
  
• Prior data-science certifications or experience (e.g., Data+, DataSys+) is beneficial but not strictly stated as a formal prerequisite.

There are no formal prerequisites required to take the CompTIA DataX (DY0-001) exam.

However, CompTIA strongly recommends that candidates have:

• 5 or more years of professional experience working in data science, machine learning, or a closely related technical field.
  
• A strong foundation in mathematics and statistics, including linear algebra, probability, and calculus.
  
• Proficiency in programming with at least one data-science language such as Python, R, or SQL.
  
• Familiarity with machine learning concepts, data pipelines, and MLOps frameworks.
  
• Experience with data analysis, model evaluation, and business communication of data-driven insights.
  
• (Recommended but not required) Prior CompTIA certifications such as Data+ or DataSys+, or equivalent hands-on experience.

After completing this training (and passing the exam), participants will be able to:

• Apply mathematical and statistical techniques (linear algebra, calculus, statistical modeling) appropriately in data science contexts
  
• Process, clean, prepare data and use appropriate methods for modeling and analysis
  
• Build, evaluate, and deploy machine learning and deep learning models in production-oriented environments
  
• Understand and implement operations and processes around data science: pipelines, lifecycle, MLOps, versioning, model drift
  
• Recognize and apply specialized data-science applications (e.g., NLP, computer vision, time-series forecasting) and stay ahead of industry trends
  
• Articulate data-science strategy, justify model decisions, and interpret outcomes for stakeholders
  
• Prepare for and pass the CompTIA DataX (DY0-001) certification exam

Linear algebra, calculus concepts, statistical methods, data processing & cleaning.

Model selection, evaluation, interpretation of results, outcomes justification.

Machine learning models, deep learning concepts, model deployment considerations.

Data science operations: pipeline management, MLOps, lifecycle, productionization.

Industry-specific applications (e.g., computer vision, NLP, time-series) and emerging trends.