CompTIA DataX (Plus) Certification Training

• Module 1: Mathematics and statistics (17%)
  • 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 (24%)
  • 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 (24%)
  • 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 (22%)
  • 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 (13%)
  • Optimization: Comparing constrained and unconstrained optimization.
  • NLP Concepts: Explaining natural language processing (NLP) techniques like tokenization, embeddings, term frequency-inverse document frequency (TF-IDF), topic modeling, and NLP applications.
  • Computer vision: Explaining optical character recognition (OCR), object detection, tracking, and data augmentation.
  • Other applications: Explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.

This training is ideal for:

• Senior Data Scientists
• Lead Data Scientists
• Principal Data Scientists
• Data Science Managers
• AI Engineers
• Machine Learning Engineers
• Senior Data Analysts
• Data Engineers
• MLOps Engineers
• Data Science Consultants
• Quantitative Analysts 
• Research Scientists – AI/Data Science
• Applied Scientists
• Big Data Engineers 
• Analytics Managers
• Decision Scientists

• 5+ years of recommended experience in data science or a similar role.
• Strong command of statistical concepts, mathematical reasoning, and machine learning methodologies

By the end of this training, participants will be able to:

• Apply mathematical and statistical methods appropriately, including data processing, cleaning, statistical modeling, linear algebra, and calculus concepts.
• Utilize appropriate analysis and modeling methods to make justified model recommendations for modeling, analysis, and outcomes.
• Implement machine learning models and understand deep learning concepts to advance data science capabilities.
• Implement data science operations and processes effectively to support organizational goals.
• Demonstrate an understanding of industry trends and specialized applications of data science in various fields.