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DataAI

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DA-002 Live Instructor-Led Training
CompTIA DY0-001 5 Days 40 Hours
$2,199

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CompTIA DataAI (DY0-001) develops advanced skills across the data science and artificial intelligence lifecycle. It is intended for experienced professionals who design, build, deploy, and manage AI-driven data solutions in enterprise environments, as well as organizations developing senior-level data and machine learning capabilities.

The curriculum brings together mathematics and statistics, modeling and analysis, machine learning, operational processes, and specialized applications of data science. Participants work with statistical modeling, hypothesis testing, exploratory data analysis, supervised and unsupervised learning, deep learning concepts, MLOps pipelines, production deployment, and the governance and ethical considerations involved in operating AI systems at scale.

Data scientists, machine learning engineers, applied statisticians, quantitative analysts, and related professionals with five or more years of hands-on experience are the primary audience. For organizations, the training supports the development of senior staff who can manage the full lifecycle of data and AI solutions, from data acquisition and model development through deployment, monitoring, governance, and ongoing performance management.

Course Objectives

What you will learn

Domain 1: Mathematics and Statistics

  • Given a scenario, apply the appropriate statistical method or concept.
  • Explain probability and synthetic modeling concepts and their uses.
  • Explain the importance of linear algebra and basic calculus concepts.
  • Compare and contrast various types of temporal models.

Domain 2: Modeling, Analysis, and Outcomes

  • Given a scenario, use the appropriate exploratory data analysis (EDA) method or process.
  • Given a scenario, analyze common issues with data.
  • Given a scenario, apply data enrichment and augmentation techniques.
  • Given a scenario, conduct a model design iteration process.
  • Given a scenario, analyze results of experiments and testing to justify final model recommendations and selection.
  • Given a scenario, translate results and communicate via appropriate methods and mediums.

Domain 3: Machine Learning

  • Given a scenario, apply foundational machine-learning concepts.
  • Given a scenario, apply appropriate statistical supervised machine-learning concepts.
  • Given a scenario, apply tree-based supervised machine-learning concepts.
  • Explain concepts related to deep learning.
  • Explain concepts related to unsupervised machine learning.

Domain 4: Operations and Processes

  • Explain the role of data science in various business functions.
  • Explain the process of and purpose for obtaining different types of data.
  • Explain data ingestion and storage concepts.
  • Given a scenario, implement common data-wrangling techniques.
  • Given a scenario, implement best practices throughout the data science life cycle.
  • Explain the importance of DevOps and MLOps principles in data science.
  • Compare and contrast various deployment environments.

Domain 5: Specialized Applications of Data Science

  • Compare and contrast optimization concepts.
  • Explain the use and importance of natural language processing (NLP) concepts.
  • Explain the use and importance of computer vision concepts.
  • Explain the purpose of other specialized applications in data science.