COMP-210
CompTIA DataAI Bootcamp
Description
CompTIA DataAI (formerly DataX) is the premier certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science. DataAI 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.
Prerequisite
We recommend 18–24 months of experience with exposure to databases and analytical tools, a basic understanding of statistics, and data visualization experience.
Objectives
By the end of this course, participants will:
- Design and evaluate advanced data and AI solutions.
- Apply advanced analytics and machine learning concepts.
- Work across the full AI and data science lifecycle.
- Assess data quality, bias, and risk in AI systems.
- Align AI initiatives with business value and governance requirements.
- Operationalize AI and analytics solutions.
Key Takeaways
- A strategic mindset for data and AI solutions
- Confidence evaluating and selecting ML approaches
- Practical understanding of AI risks and ethics
- Skills to bridge technical teams and business stakeholders
- Operational awareness of AI in production environments
- Exam-ready expertise aligned to senior-level roles
Certificate of Completion
- Certificate of Completion issued after successful completion of all chapters, hands-on exercises, and course evaluation.
- Certificate is downloadable from the Ghost Team Academy Education Portal.
Training Outline
Day 1 – Mathematics & Statistics
- Topics:
- Welcome & course overview
- Role of mathematical reasoning in data science/AI
- Probability basics, distributions, and descriptive statistics
- Hypothesis testing (t-tests, chi-square) and p-values
- Regression concepts and metrics (MSE, RMSE, R-squared)
- ROC/AUC, confusion matrices, and classification metrics
- Linear algebra essentials (matrix operations, eigenvalues)
- Labs/Exercises: Interpreting stats outputs & preparing data for modeling
Day 2 – Modeling, Analysis, & Outcomes
- Topics:
- Exploratory Data Analysis (EDA) fundamentals
- Feature identification & transformation
- Handling seasonality, outliers, and sparse data
- Model selection best practices
- Evaluation metrics and validation strategies
- Communicating results and avoiding misleading visuals
- Lab/Exercises: Build models and compare performance outcomes
Day 3 – Machine Learning
- Topics:
- Supervised learning: regression, classification
- Cross-validation and bias-variance tradeoff
- Regularization & hyperparameter tuning
- Tree-based models: decision trees, random forest, boosting
- Introduction to deep learning concepts & neural network architecture
- Unsupervised learning: clustering, dimensionality reduction
- Lab/Exercises: Train and evaluate ML models with real data
Day 4 – Operations & Processes
- Topics:
- Data ingestion workflows: pipelines, streaming vs. batch
- Clean code, version control, and reproducibility
- Introduction to DevOps and MLOps principles
- Deployment environments: cloud, hybrid, containers
- Monitoring models in production & handling drift
- Data lineage, quality, and governance controls
- Labs/Exercises: Design & document an operational AI workflow
Day 5 – Specialized Applications & Exam Readiness
- Topics:
- Natural Language Processing (NLP) concepts (tokenization, embeddings)
- Computer Vision basics (OCR, object detection)
- Reinforcement learning and anomaly detection
- Capstone: designing an AI solution end-to-end
- Practice exam questions aligned to all domains
- Exam strategies and domain weighting review
- Course summary, key takeaways, and Q&A
