CompTIA DataX (DY0-001)

CompTIA DataX (DY0-001).

The CompTIA DataAI (DY0-001) Training prepares you to combine data science and artificial intelligence techniques to solve business problems and support data-driven decision-making.

This course covers key topics including mathematics, statistics, machine learning, data analysis, predictive modeling, data governance, and AI ethics. You will learn how to collect, prepare, analyze, and visualize data while applying machine learning algorithms and AI techniques to generate valuable insights.

The training explores supervised and unsupervised learning, neural networks, deep learning, feature engineering, data pipelines, lifecycle management, and operational best practices. Advanced topics include natural language processing (NLP), computer vision, graph analytics, reinforcement learning, and signal processing.

Ideal for data analysts, data scientists, AI practitioners, business intelligence professionals, and IT specialists, this course provides the knowledge and skills required to prepare for the CompTIA DataAI certification and succeed in modern data-driven organizations.

Cursusinhoud

1. Mathematics and Statistics

Build essential mathematical and statistical foundations for data science and machine learning:

• Descriptive and inferential statistics
• Probability theory and hypothesis testing
• Model evaluation metrics
• Regression and classification analysis
• Linear algebra and calculus
• Temporal and predictive modeling techniques

CompTIA DataAI: Foundations of Descriptive Statistics and Probability

Course: 48 Minutes

• Course Overview
• Role of Descriptive and Inferential Statistics in Data Analysis
• Calculating Measures of Central Tendency
• Knowledge Check: Using Descriptive and Inferential Statistics, and Measures of Central Tendency
• Computing Measures of Variability
• Skewness, Kurtosis, Quantiles, and Percentiles
• Knowledge Check: Identifying Measures of Variability and Distribution Characteristics
• Probability Fundamentals
• Conditional Probability and Bayes' Theorem
• Knowledge Check: Reviewing Probability Concepts and Conditional Probability
• Probability Distributions
• Visualizing PDF, PMF, and CDF
• Missing Data Mechanisms
• Knowledge Check: Identifying Probability Distributions and Functions, and Missing Data Types
• Course Summary

CompTIA DataX (DY0-001): Inferential Statistics and Hypothesis Testing

Course: 46 Minutes

• Course Overview
• Inferential Statistics Foundations
• Hypothesis Testing Concepts
• Knowledge Check: Reviewing Inferential Statistics and Hypothesis Testing Concepts
• P-Values and Confidence Intervals
• Performing and Interpreting T-Tests
• Knowledge Check: Performing and Interpreting P-Values, Confidence Intervals, and T-Tests
• Performing Chi-Squared Tests
• Performing ANOVA and Interpreting the F-Statistic
• Knowledge Check: Assessing Chi-Squared Tests and ANOVA, and Interpreting F-Statistic Results
• Statistics in ML Decision-Making
• Calculating Entropy, Gini Index, and Information Gain
• Knowledge Check: Applying Statistics in ML Using Entropy, Gini Index, and Information Gain
• Course Summary

CompTIA DataX (DY0-001): Regression Metrics, Classification Metrics, and ROC/AUC

Course: 1 Hour

• Course Overview
• Understanding Regression Models
• Regression Evaluation and Error Metrics
• Computing Regression Metrics
• Knowledge Check: Analyzing Regression Metrics
• Interpreting R² and Adjusted R²
• Residual Analysis
• Knowledge Check: Assessing Residual Analysis
• Evaluating Models Using AIC/BIC for Model Comparison
• Introduction to Classification
• Knowledge Check: Reviewing Classification
• Computing Classification Metrics
• Interpreting the Confusion Matrix and Error Types
• Knowledge Check: Analyzing the Confusion Matrix
• Interpreting ROC Curves and AUC
• Metrics Under Class Imbalance
• Knowledge Check: Exploring Metrics Under Class Imbalance
• Course Summary

CompTIA DataX (DY0-001): Linear Algebra, Calculus, and Temporal Models

Course: 1 Hour, 15 Minutes

• Course Overview
• Why Linear Algebra Matters in Machine Learning
• Implementing Vectors, Matrices, and Operations in Python
• Computing Distance Metrics: Euclidean, Manhattan, Cosine
• Knowledge Check: Assessing Vectors and Matrix Operations
• Logarithms and Log-Likelihood
• Partial Derivatives in Multivariate Functions
• Knowledge Check: Assessing Logarithms and Log-Likelihood
• Chain Rule for Composite Functions
• What Are Temporal Models?
• Knowledge Check: Assessing Temporal Models
• Implementing AR, MA, and ARIMA
• Survival Analysis and Causal Inference
• Implementing ATE, Kaplan-Meier Curves, and Confounder Handling
• Knowledge Check: Assessing Survival Analysis and Causal Inference
• Course Summary

2. Modeling, Analysis, and Outcomes

Master the complete data preparation and analysis workflow:

• Exploratory Data Analysis (EDA)
• Data quality assessment and cleansing
• Feature engineering and enrichment
• Modeling readiness evaluation
• Business decision framing
• Data visualization and storytelling

CompTIA DataX (DY0-001): Exploratory Data Analysis (EDA) Foundations

Course: 52 Minutes

• Course Overview
• Why EDA Matters
• Feature Types and Visualizations
• Knowledge Check: Assessing Feature Types and Visualizations
• Conducting Univariate Analysis
• Conducting Bivariate Analysis
• Multivariate Analysis
• Knowledge Check: Reviewing Multivariate Analysis
• Pattern Detection in Data
• Statistical Methods for EDA
• Creating and Automating a Structured EDA Workflow
• Knowledge Check: Assessing Statistical Methods for EDA
• Course Summary

CompTIA DataX (DY0-001): Detecting and Handling Data Issues

Course: 1 Hour, 12 Minutes

• Course Overview
• Why Data Issues Matter
• Common Data Issues and Their Impact
• Diagnosing Data Signals and Issues
• Understanding Sparse Data
• Visualizing Sparse Data Patterns
• Knowledge Check: Assessing Common Data Issues and Data Sparsity
• Non-Linearity and Noise in Data
• Interpreting LOESS/LOWESS Plots
• Knowledge Check: Assessing Effects of Non-Linearity and Noise in Data
• Rolling Statistics and Decomposition
• Using Seasonal Decomposition Techniques
• Knowledge Check: Reviewing Rolling Statistics and Decomposition Methods
• Handling Granularity Mismatches in Datasets
• Visualizing and Managing Outliers in Data
• Multicollinearity in Data
• Knowledge Check: Assessing Data Multicollinearity and Granularity
• Calculating Variance Inflation Factor
• Develop Remediation Strategies for Data Issues
• Knowledge Check: Reviewing Data Remediation Strategies
• Course Summary

CompTIA DataX (DY0-001): CompTIA DataX: Feature Engineering and Data Enrichment

Course: 1 Hour, 16 Minutes

• Course Overview
• Feature Engineering Concepts
• Log and Power Transforms
• Knowledge Check: Assessing Feature Engineering
• Binning Techniques
• Encoding Methods for Categorical Data
• Knowledge Check: Assessing on Encoding Methods
• Implementing OneHotEncoding and OrdinalEncoding with sklearn
• Standardization and Normalization
• StandardScaler vs. MinMaxScaler
• Knowledge Check: Assessing Standardization and Normalization
• Comparing StandardScaler vs. MinMaxScaler
• Techniques for Modeling Non-Linearity
• Exploring Time-Series Patterns and Feature Engineering Alignment
• Introduction to Geospatial Features and Geocoding Concepts
• Using Simple Geocode APIs
• Efficient Preprocessing with sklearn Pipeline and ColumnTransformer
• Building Pipelines with sklearn Pipeline and ColumnTransformer
• Knowledge Check: Assessing Efficient Preprocessing
• Course Summary

CompTIA DataX (DY0-001): Modeling Readiness and Decision Framing

Course: 42 Minutes

• Course Overview
• Modeling in Analytics Lifecycle
• Defining Analytical Modeling Tasks
• Mapping Business Problems to Analytical ModelingTasks
• Assumptions and Constraints
• Baselines and Success Criteria
• Knowledge Check: Assessing Baselines and Success Criteria
• Modeling Risks
• Evaluation Goals for Models
• Validation Strategies
• Knowledge Check: Reviewing Evaluation Goals
• Defining the Decision Brief Boundary
• Course Summary

CompTIA DataX (DY0-001): Data Visualization, Communication, and Outcomes

Course: 48 Minutes

• Course Overview
• Why Data Insights Fail Without Clear Communication
• Choosing the Right Data Types and Charts
• Chart Smart: Mastering Data Visualization
• Implementing Cohesive Multi-Chart Analytical Views
• Navigate Uncertainty in Data
• Visualizations: Spotting and Avoiding Misleads
• Knowledge Check: Assessing Data Uncertainty and Visualizations
• Designing Accessible Visualizations
• Structuring Insights for Clear Communication
• Sequencing an Effective Data Story
• Designing Effective Dashboard Layouts
• Designing Insightful and Interactive Dashboards with Plotly
• Knowledge Check: Reviewing Accessible Visualization and Structuring Insights
• Course Summary

3. Machine Learning

Develop practical machine learning expertise:

• Supervised learning algorithms
• Loss functions and optimization
• Tree-based and ensemble methods
• Neural networks and deep learning fundamentals
• Unsupervised learning techniques
• Dimensionality reduction and clustering

CompTIA DataAI (DY0-001): Applied Supervised Learning - Core Algorithms and Model Selection

Course: 1 Hour, 4 Minutes

• Course Overview
• Identifying Supervised Learning Concepts
• Linear Regression Techniques
• Implementing Multiple Linear Regression Using sklearn
• Logistic Regression Principles
• Training Logistic Regression on Binary Dataset
• Knowledge Check: Reviewing Logistic Regression
• K-Nearest Neighbors (KNN)
• Performing KNN Classification with Varying K
• Knowledge Check: Reviewing K-Nearest Neighbors (KNN)
• Naive Bayes Algorithm
• Implementing GaussianNB Using sklearn
• Knowledge Check: Reviewing Naïve Bayes
• Association Rule Metrics
• Identifying Association Rule Concepts
• Support Vector Machines (SVM)
• Supervised Learning Algorithm Selection
• Knowledge Check: Reviewing Support Vector Machines (SVM)
• Course Summary

CompTIA DataAI: Machine Learning Mechanics - Loss, Generalization, and Model Optimization

Course: 1 Hour, 5 Minutes

• Course Overview
• Machine Learning Workflow
• Loss Functions in Model Training
• Calculating Loss Functions Using sklearn
• Knowledge Check: Reviewing Loss Functions
• The Bias-Variance Tradeoff
• Model Regularization Techniques
• Applying L1, L2, and Elastic Net Regularization
• Knowledge Check: Assessing Regularization Techniques
• Cross-Validation for Model Performance
• Performing K-Fold, Stratified, and Time Series CV Cross-Validation
• Knowledge Check: Reviewing Cross-Validation
• Hyperparameter Tuning for Model Performance
• Performing Hyperparameter Tuning Using sklearn
• Data Leakage and Its Impact on Models
• Knowledge Check: Reviewing Hyperparameter Tuning
• Course Summary

CompTIA DataAI (DY0-001): Tree-Based Methods and Ensemble Learning

Course: 54 Minutes

• Course Overview
• Decision Tree Essentials
• Optimization of Decision Trees
• Knowledge Check: Reviewing Tree Essentials and Optimization
• Training a DecisionTreeClassifier
• Bagging and Random Forests
• Knowledge Check: Assessing Bagging and Random Forests
• Boosting Methods
• Training a GradientBoostingClassifier
• Hyperparameter Tuning for Ensembles
• Knowledge Check: Assessing Boosting Techniques and Hyperparameter Tuning
• Interpretability for Tree Ensembles
• Comparison of Tree-Based Models
• Knowledge Check: Reviewing Interpretability and Model Comparison
• Course Summary

CompTIA DataAI (DY0-001): Deep Learning Essentials

Course: 49 Minutes

• Course Overview
• Introduction to Deep Learning Concepts
• Artificial Neuron Mechanics
• Artificial Neural Network (ANN) Architecture and Data Flow
• Knowledge Check: Reviewing Artificial Neural Network Mechanics and Architecture
• Activation Functions and Non-Linearity
• Loss Functions and Optimization Basics
• Backpropagation and Gradient Flow
• Overfitting and Underfitting in Deep Networks
• Knowledge Check: Reviewing Activation Functions, Loss Functions, Overfitting, and Underfitting
• Regularization Techniques to Reduce Overfitting
• Normalization Techniques in Deep Learning
• Implementing a Simple ANN for Classification
• Comparison of Deep Learning Frameworks
• Knowledge Check: Reviewing Regularization Techniques, Normalization, and Deep Learning
• Frameworks
• Course Summary

CompTIA DataAI (DY0-001): Unsupervised Learning and Dimensionality Reduction

Course: 1 Hour, 6 Minutes

• Course Overview
• Unsupervised Learning Concepts
• Clustering Methods Overview
• Knowledge Check: Reviewing Unsupervised Learning and Clustering Fundamentals
• K-Means Clustering
• Implementing K-Means Clustering and Cluster Selection
• Knowledge Check: Reviewing K-Means Clustering Concepts and Application
• Hierarchical Clustering (Agglomerative)
• Implementing and Interpreting Dendrograms with SciPy
• Knowledge Check: Assessing the Use of Hierarchical Clustering and Dendrograms
• Density-Based Clustering (DBSCAN)
• Selecting the Right Unsupervised Technique
• Dimensionality Reduction Overview
• Knowledge Check: Identifying DBSCAN, Technique Selection, and Dimensionality Reduction
• Principal Component Analysis (PCA) Concepts
• Implementing Principal Component Analysis (PCA) and Visualization
• Singular Value Decomposition (SVD)
• Unsupervised Evaluation Metrics and Practical Interpretation
• Knowledge Check: Reviewing Principal Component Analysis Concepts and Application
• Course Summary

4. Operations and Processes

Learn operational and engineering best practices:

• Business context and KPI frameworks
• Compliance and governance
• Data ingestion and pipeline design
• Data wrangling and labeling
• Lifecycle frameworks and version control
• Engineering and deployment best practices

CompTIA DataAI (DY0-001): Business Functions, Compliance, and KPI Foundations

Course: 53 Minutes

• Course Overview
• Identifying the Right Analytics Approach
• Understanding KPIs and Metrics
• Leading vs. Lagging Indicators
• Knowledge Check: Assessing KPIs and Leading/Lagging Indicators
• Data Governance in Data Science
• GDPR and Compliance Essentials
• Knowledge Check: Assessing Data Governance, GDPR, and Compliance Essentials
• Functional and Non-Functional Requirements
• Business vs. Analytical Problems and Risks
• Translating Business Questions into Model Design
• Business Requirements Documentation (BRD)
• Creating a Lightweight BRD for a Data Science Project
• Knowledge Check: Reviewing System Requirements, Business/Analytical Problems, and the BDR
• Course Summary

CompTIA DataAI (DY0-001): Data Types, Data Ingestion, and Pipelines

Course: 1 Hour, 2 Minutes

• Course Overview
• Understanding Data Wrangling and Cleaning
• Identifying Data Quality Issues: Spot, Classify, Act
• Techniques for Data Cleaning
• Knowledge Check: Assessing Your Understanding of Data Wrangling and Cleaning
• Merges and Joins
• Using Merge and Join Operations in Python
• Selecting the Right Imputation Strategy1
• Predicting Missing Values Using Machine Learning
• Knowledge Check: Reviewing the Use of Merges and Joins
• Handling Inconsistent Granularity
• Ground Truth Labeling for Supervised Learning
• Knowledge Check: Assessing Your Understanding of Granularity and Ground Truth Labeling
• Pipelines for Scalable Labeling
• Documenting Data Quality and Transformations
• Knowledge Check: Reviewing Scalable Labeling and Documenting Data Qualit
• Course Summary

CompTIA DataAI (DY0-001): Data Wrangling, Cleaning, and Ground Truth Labeling

Course: 1 Hour, 2 Minutes

• Course Overview
• Understanding Data Wrangling and Cleaning
• Identifying Data Quality Issues: Spot, Classify, Act
• Techniques for Data Cleaning
• Knowledge Check: Assessing Your Understanding of Data Wrangling and Cleaning
• Merges and Joins
• Using Merge and Join Operations in Python
• Selecting the Right Imputation Strategy
• Predicting Missing Values Using Machine Learning
• Knowledge Check: Reviewing the Use of Merges and Joins
• Handling Inconsistent Granularity
• Ground Truth Labeling for Supervised Learning
• Knowledge Check: Assessing Your Understanding of Granularity and Ground Truth Labeling
• Pipelines for Scalable Labeling
• Documenting Data Quality and Transformations
• Knowledge Check: Reviewing Scalable Labeling and Documenting Data Quality
• Course Summary

CompTIA DataAI (DY0-001): Lifecycle, Version Control, and Engineering Best Practices

Course: 1 Hour, 5 Minutes

• Course Overview
• Data Science Lifecycle Frameworks
• Mapping Lifecycle Phases to Deliverables
• Knowledge Check: Reviewing the Data Science Lifecycle Framework
• Lifecycle Artifacts Identification
• Version Control Concepts and Benefits
• Using Git for Data Science Projects
• Clean Coding Practices
• Knowledge Check: Understanding Version Control Concepts and Clean Coding Practices
• Spotting Code Quality Issue
• The Importance of Testing
• Writing Unit Testing in Python for Data Functions
• Experiment Tracking and Reproducibility
• Knowledge Check: Reviewing Testing, Experiment Tracking, and Reproducibility
• Dependency Management and Container Basics
• Project Structure and Integration with CI
• Knowledge Check: Assessing Dependency Management, Container Basics, and Integration with CI
• Course Summary

CompTIA DataAI (DY0-001):MLOps, CI/CD, Containerization, and Deployment Environments

Course: 57 Minutes

• Course Overview
• DevOps vs. MLOps Overview
• MLOps in Data Science Workflows
• Knowledge Check: Assessing MLOps in Data Science Workflows
• Model Validation Concepts
• CI/CD for ML Systems
• Model Formats and API Serving Patterns
• Knowledge Check: Reviewing Model Validation, CI/CD, and Model Formats
• Containerization Basics
• Dockerfile and Image Building
• Orchestration with Kubernetes
• Knowledge Check: Reviewing Containerization and Orchestration with Kubernetes
• Deployment Environments
• Matching Deployment Environment to Use Case
• Monitoring and Validation
• Building the ML Deployment Architecture
• Knowledge Check: Reviewing Deployment Environments and Metrics
• Course Summary

5. Specialized Applications of Data Science

Explore advanced AI and data science applications:

• Optimization techniques
• Natural Language Processing (NLP)
• Tokenization and embeddings
• Computer vision fundamentals
• Graph analysis
• Reinforcement learning
• Signal processing techniques

CompTIA DataAI (DY0-001): Optimization for Data Science

Course: 38 Minutes

• Course Overview
• Anatomy of an Optimization Problem
• Gradient Descent: The Optimization Engine
• Implementing Gradient Descent Algorithms
• Constraints as Geometry in Optimization
• Analyzing Optimization Tuning Challenge
• Knowledge Check: Reviewing Fundamentals and Gradient Descent
• Course Summary

CompTIA DataAI (DY0-001): Natural Language Processing (NLP) Techniques

Course: 59 Minutes

• Course Overview
• Introduction to Natural Language Processing
• Exploring NLP Challenges
• Text Preprocessing and Normalization
• Knowledge Check: Reviewing Fundamentals and Techniques of Text Preprocessing
• Feature Extraction and Text Vectorization
• Implementing Text Preprocessing and Feature Extraction
• Word Embeddings and Semantic Analysis
• Visualizing Word Embeddings
• Knowledge Check: Assessing Feature Extraction, Vectorization, and Word Embedding Concepts
• Sentiment Analysis and Text Classification
• Topic Modeling and Document Classification
• Knowledge Check: Implementing Sentiment Analysis and Topic Modelling
• Course Summary

CompTIA DataAI (DY0-001): Computer Vision Techniques & Applications

Course: 37 Minutes

• Course Overview
• Fundamentals of Computer Vision
• Optical Character Recognition (OCR)
• Knowledge Check: Identifying Computer Vision and OCR Concepts
• Applying OCR to Extract Text from Images
• Why Data Augmentation Matters in Computer Vision
• Exploring Practical Data Augmentation for Computer Vision
• Knowledge Check: Implementing Data Augmentation Techniques
• Convolutional Neural Network (CNN)
• Object Detection and Tracking Fundamentals
• Comparing Classification, Detection, and Segmentation
• Knowledge Check: Reviewing Convolutional Neural Network (CNN) and Object Tracking
• Course Summary

CompTIA DataAI (DY0-001): Graph Analytics, Reinforcement Learning, and Detection Techniques

Course: 55 Minutes

• Course Overview
• Graph Analytics Fundamentals
• Mapping Graph Concepts
• Performing Graph Analytics
• Knowledge Check: Identifying Graph Analytics Concepts
• Fundamentals of Reinforcement Learning
• Reinforcement Learning Algorithms
• Fraud Detection Techniques and Challenges
• Knowledge Check: Reviewing Reinforcement Learning and Fraud Detection
• Applying Anomaly Detection Methods
• Signal Processing
• Knowledge Check: Understanding Anomaly Detection and Signal Processing
• Integration of Graphs, RL, and Detection Techniques
• Algorithm Selection Principles
• Applying Algorithm Selection to Real‑World Scenarios
• Knowledge Check: Assessing Detection Techniques and Algorithm Selection
• Course Summary

Specificaties

Taal: Engels
Kwalificaties van de Instructeur: Gecertificeerd
Cursusformaat en Lengte: Lesvideo's met ondertiteling, interactieve elementen en opdrachten en testen
Lesduur: 21:45 uur
Voortgangsbewaking: Ja
Toegang tot Materiaal: 365 dagen
Technische Vereisten: Computer of mobiel apparaat, Stabiele internetverbindingen Webbrowserzoals Chrome, Firefox, Safari of Edge.
Support of Ondersteuning: Helpdesk en online kennisbank 24/7
Certificering: Certificaat van deelname in PDF formaat
Prijs en Kosten: Cursusprijs zonder extra kosten
Annuleringsbeleid en Geld-Terug-Garantie: Wij beoordelen dit per situatie
Award Winning E-learning: Ja

Tip! Zorg voor een rustige leeromgeving, tijd en motivatie, audioapparatuur zoals een koptelefoon of luidsprekers voor audio, accountinformatie zoals inloggegevens voor toegang tot het e-learning platform.