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The CompTIA DataAI course is designed for experienced data professionals seeking to validate and enhance their expertise in data science and Artificial Intelligent (AI). The course provides a comprehensive, hands-on approach to mastering advanced mathematics, statistics, machine learning, and deep learning concepts. Using the DataAI frameworks, learners will solidify their understanding of advanced-level data tools and concepts, and learn a wide range of topics including mathematics and statistics, machine learning, operations and processes, and specialized applications of data science. learners will gain practical experience in data processing, statistical modeling, and AI-driven analysis, ensuring they can develop reliable and scalable data solutions. They will also explore specialized industry applications in finance, healthcare, and cybersecurity, applying data science to solve real-world challenges.
Learning Outcomes:
Analyze and prepare data for AI use cases
Apply data analytics and AI concepts to solve business problems
Evaluate AI models and data outputs for accuracy, bias, and performance
Implement responsible and secure data and AI practices
Exam Details
This course is designed to build participants’ understanding of key concepts and domains covered in the CompTIA DataAI certification.
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.
Module 1: Mathematics and statistics
- 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.
Module 2: Modeling, analysis, and outcomes
- 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.
Module 3: Machine learning
- 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).
Module 4: Operations and processes
- 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, version control, clean code, and unit tests.
- Data science life cycle: Applying workflow models, version control, 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.
Module 5: Specialized applications of data science
- Optimization: Comparing constrained and unconstrained optimization.
- NLP Concepts
- Computer vision
- Explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.
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