DY0-001.AE1
CompTIA DataAI (DY0-001)
Master CompTIA DataX DY0-001 certification with practical labs, covering data science fundamentals to advanced machine learning deployment.
- Practice in 34 Laboratorios prácticos — nothing to install
- 14 Lecciones interactivas y 67 topics mapped to the official exam objectives
- 551 Preguntas del examen de práctica y 2 Pruebas completas
Intermediate A tu propio ritmo · 1 año de acceso
34 LiveLabs prácticos
Practice real IT tasks in guided environments.
- Entornos reales
- Calificación automática
- Sin instalación
14Lecciones interactivas
67Topics
34Laboratorio en vivo
551Preguntas del examen de práctica
1Vídeos
201Tarjetas didácticas
80Glosario de términos
01 / Habilidades que obtendrás
What you will be able to do
Try Free →
No se requiere tarjeta de crédito
This CompTIA DataX DY0-001 certification training isn't just theory; it's your operational guide to becoming a competent data professional. We cut through the noise, focusing on the practical application of data science principles from collection to model deployment. You'll tackle 34 hands-on labs, reinforce concepts with 240 quizzes, and solidify knowledge through 312 exercises. Expect to grapple with real-world data challenges, understanding that perfect data is a myth. The trade-off for comprehensive coverage is the sheer volume of material, demanding consistent effort. This course prepares you to pass the CompTIA DataX DY0-001 exam and apply these skills immediately.
- Data Science Fundamentals: Master core concepts, best practices, and real-world applications of data science.
- Statistical & Mathematical Mastery: Understand probability, inferential stats, linear algebra, and calculus for data analysis.
- Data Wrangling & Cleaning: Learn to collect, store, explore, and fix messy data.
- Machine Learning Modeling: Build, evaluate, and deploy supervised & unsupervised models (regression, clustering, neural networks, NLP).
- MLOps & Model Deployment: Validate models, communicate insights, and manage ML workflows in production.
- Data Lifecycle Management: Master the entire data pipeline, from identifying common data sources and ingestion strategies to robust storage solutions and managing data through its lifecycle. Understand the inherent trade-offs between data freshness and storage costs.
- Machine Learning Model Development & Deployment: Design, build, and rigorously evaluate various machine learning models, including supervised, unsupervised, and deep learning. Learn to validate models effectively and deploy them using MLOps principles, recognizing that deployment complexity scales with model interdependence.
- Statistical and Mathematical Foundations: Apply essential calculus, probability distributions, inferential statistics, and linear algebra to interpret data and inform model selection. Grasp the limitations of statistical inference when dealing with biased or incomplete datasets.
- Data Preparation and Feature Engineering: Develop proficiency in data transformation, enrichment, augmentation, and cleaning techniques. Address common data quality issues and class imbalance, understanding that over-processing can sometimes obscure valuable signals.
Course Highlights
-
14 Lecciones estructuradas Cobertura completa de los objetivos principales del curso
-
34 LiveLabs prácticos Escenarios interactivos guiados con evaluación instantánea
-
551 Preguntas de práctica Pruebas de evaluación con justificaciones de respuesta detalladas
-
1 año de acceso completo Aprendizaje a tu propio ritmo, accesible en cualquier momento y en todos los dispositivos
02 / Lecciones y laboratorios
See exactly what you will learn and practice
Plan de estudios
14 Lecciones interactivas · 67 topics01 Introduction 2 topics +
- About the DataX Certification
- How This Course Is Organized
02 What Is Data Science? 4 topics +
- Data Science
- Data Science Best Practices
- Summary
- Exam Essentials
03 Mathematics and Statistical Methods 6 topics · 8 Laboratorio en vivo +
- Calculus
- Probability Distributions
- Inferential Statistics
- Linear Algebra
- Summary
- Exam Essentials
8 Laboratorio en vivo in this lesson — see the labs panel →
04 Data Collection and Storage 6 topics · 1 Laboratorio en vivo +
- Common Data Sources
- Data Ingestion
- Data Storage
- Managing the Data Lifecycle
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
05 Data Exploration and Analysis 4 topics · 3 Laboratorio en vivo +
- Exploratory Data Analysis
- Common Data Quality Issues
- Summary
- Exam Essentials
3 Laboratorio en vivo in this lesson — see the labs panel →
06 Data Processing and Preparation 6 topics · 4 Laboratorio en vivo +
- Data Transformation
- Data Enrichment and Augmentation
- Data Cleaning
- Handling Class Imbalance
- Summary
- Exam Essentials
4 Laboratorio en vivo in this lesson — see the labs panel →
07 Modeling and Evaluation 5 topics · 8 Laboratorio en vivo +
- Types of Models
- Model Design Concepts
- Model Evaluation
- Summary
- Exam Essentials
8 Laboratorio en vivo in this lesson — see the labs panel →
08 Model Validation and Deployment 6 topics · 1 Laboratorio en vivo +
- Model Validation
- Communicating Results
- Model Deployment
- Machine Learning Operations (MLOps)
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
09 Unsupervised Machine Learning 6 topics · 1 Laboratorio en vivo +
- Association Rules
- Clustering
- Dimensionality Reduction
- Recommender Systems
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
10 Supervised Machine Learning 8 topics · 3 Laboratorio en vivo +
- Linear Regression
- Logistic Regression
- Discriminant Analysis
- Naive Bayes
- Decision Trees
- Ensemble Methods
- Summary
- Exam Essentials
3 Laboratorio en vivo in this lesson — see the labs panel →
11 Neural Networks and Deep Learning 4 topics · 2 Laboratorio en vivo +
- Artificial Neural Networks
- Deep Neural Networks
- Summary
- Exam Essentials
2 Laboratorio en vivo in this lesson — see the labs panel →
12 Natural Language Processing 5 topics · 1 Laboratorio en vivo +
- Natural Language Processing
- Text Preparation
- Text Representation
- Summary
- Exam Essentials
1 Laboratorio en vivo in this lesson — see the labs panel →
13 Specialized Applications of Data Science 4 topics · 2 Laboratorio en vivo +
- Optimization
- Computer Vision
- Summary
- Exam Essentials
2 Laboratorio en vivo in this lesson — see the labs panel →
14 Appendix: Key Terms 1 topics +
- Glossary
Laboratorios prácticos Our edge
34 Laboratorio en vivos- Calculating Probabilities Using a PDF
- Exploring Discrete Probability Distributions
- Calculating Skewness and Kurtosis in Data
- Simulating the CLT
- Performing a Chi-Squared Test of Independence
- Performing a t-test for Two Samples
- Performing a Two-Way ANOVA
- Working With Matrices
- Generating Synthetic Data
- Creating a Bar Chart and a Scatterplot
- Creating a Box and Whisker Plot
- Creating a Sankey Diagram
- Transforming the Data
- Flattening Hierarchical Data in Python
- Dealing with Missing Values
- Performing Oversampling and Undersampling
- Performing Survival Analysis Using the Kaplan-Meier Estimator
- Creating a Time-Series Model to Predict Future Values
- Performing the Holdout Method for Model Training and Evaluation
- Evaluating Model Performance Using K-Fold Cross-Validation
- Evaluating Model Performance Using Bootstrapping
- Evaluating a Regression Model
- Evaluating a Classifier Model
- Plotting the ROC Curve
- Selecting the Optimal Model
- Performing K-Means Clustering
- Using Linear Regression for Cost Prediction
- Using Logistic Regression for Patient Classification
- Classifying Species Using Decision Trees
- Creating an ANN Model
- Using CNN for Image Classification
- Using NLP for Analyzing Tweets
- Optimizing an ML Model
- Implementing Computer Vision Techniques
Los laboratorios se ejecutan en tu navegador; no hay nada que instalar.
03 / Preguntas frecuentes
Preguntas antes de empezar
What is the CompTIA DataX DY0-001 Certification?+
Issued by CompTIA, DataX is an expert-level credential validating expertise in data science, covering mathematics, machine learning, data operations, and specialized applications like NLP and computer vision. It’s designed for professionals with 5+ years of experience in data science or related fields.
What topics does the DataX certification exam cover?+
The DataX DY0-001 exam focuses on:
- Mathematics & Statistics (calculus, probability, linear algebra)
- Data Processing (cleaning, exploration, lifecycle management)
- Machine Learning (supervised/unsupervised models, neural networks)
- Model Deployment & MLOps
- Specialized Applications (NLP, optimization, computer vision)
How do I prepare for the DY0-001 exam?+
- Enroll in uCertify’s CompTIA DataX online course.
- Take our gamified practice tests.
- Challenge your knowledge with 50+ interactive question formats.
- Gain hands-on experience with real-world datasets and labs.
Are there prerequisites for the course/exam?+
While not mandatory, 5+ years of data science experience is strongly recommended to take the CompTIA DataX certification exam.
Certify Your Data Science Skills
Learn data science, earn DataX certification, and update your LinkedIn profile.
- 1 año de acceso completo
- 34 LiveLab incluido
- Certificado de finalización
No se requiere tarjeta de crédito