CompTIA DataX (DY0-001)E-Learning Training

De CompTIA DataAI (DY0-001) Training bereidt professionals voor op het werken met data en kunstmatige intelligentie binnen moderne organisaties. Deze uitgebreide training combineert data science, machine learning, AI-processen en geavanceerde analysetechnieken om ruwe data om te zetten in waardevolle bedrijfsinzichten.

De CompTIA DataAI-certificering valideert de vaardigheden van kandidaten om data te verwerken en AI-technieken toe te passen voor het oplossen van zakelijke vraagstukken. De training richt zich op essentiële competenties zoals dataverzameling, voorbereiding, analyse, machine learning en AI-gestuurde besluitvorming.

Daarnaast leer je over datagovernance, datakwaliteit, ethische AI-principes en operationele processen die verantwoord gebruik van AI ondersteunen binnen organisaties. Deze LearningKit combineert theorie en praktijk om deelnemers optimaal voor te bereiden op realistische AI- en data science-projecten.

Doelgroep

• Data Analisten en Data Scientists
• AI- en Machine Learning Engineers
• Business Intelligence Professionals
• IT-professionals die starten met Data Science
• Developers en technische consultants
• Professionals die zich voorbereiden op het CompTIA DataAI (DY0-001) examen

Resultaat

Na deze training ben je in staat om data effectief te analyseren, AI- en machine learning-technieken toe te passen, data science workflows operationeel te beheren en AI-gedreven innovatie binnen organisaties te ondersteunen.

Demo CompTIA DataX (DY0-001) Training

Cursusinhoud

1. Wiskunde en Statistiek

Ontwikkel sterke wiskundige en statistische basisvaardigheden:

• Beschrijvende en inferentiële statistiek
• Kansberekening en hypothesetesten
• Model evaluatiemethoden
• Regressie- en classificatieanalyse
• Lineaire algebra en calculus
• Temporele en voorspellende modellen

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. Modellering, Analyse en Resultaten

Leer het volledige data-analyseproces beheersen:

• Exploratory Data Analysis (EDA)
• Data quality assessment en cleansing
• Feature engineering en enrichment
• Evaluatie van modelgereedheid
• Zakelijke besluitvorming ondersteunen
• Datavisualisatie en 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

Ontwikkel praktische machine learning vaardigheden:

• Supervised learning algoritmen
• Loss functions en optimalisatie
• Tree-based en ensemble learning technieken
• Neural networks en deep learning fundamentals
• Unsupervised learning technieken
• Dimensionality reduction en 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 en Processen

Leer operationele en technische best practices:

• Business context en KPI-frameworks
• Compliance en governance
• Data-ingestion en pipeline design
• Data wrangling en labeling
• Lifecycle frameworks en versiebeheer
• Engineering- en deploymentprocessen

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. Gespecialiseerde Toepassingen van Data Science

Ontdek geavanceerde AI- en data science toepassingen:

• Optimalisatietechnieken
• Natural Language Processing (NLP)
• Tokenization en embeddings
• Computer vision fundamentals
• Graph analysis
• Reinforcement learning
• Signal processing technieken

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