CompTIA

CompTIA DataX (DY0-001)

Data skills refined. Certification earned. Career upgraded. All thanks to our CompTIA DataX training. 

(DY0-001.AE1) / ISBN : 978-1-64459-749-1
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About This Course

This CompTIA DataX training is your all-access pass to the data analytics skills that get you hired. 

Dive into data collection, exploration, and machine learning. Then, level up with neural networks, NLP, and real-world applications. From probability distributions to model deployment, get ready to pass the CompTIA DY0-001 exam. 

Skills You’ll Get

  • 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.
Download Course Outline

1

Introduction

  • About the DataX Certification
  • How This Course Is Organized
2

What Is Data Science?

  • Data Science
  • Data Science Best Practices
  • Summary
  • Exam Essentials
3

Mathematics and Statistical Methods

  • Calculus
  • Probability Distributions
  • Inferential Statistics
  • Linear Algebra
  • Summary
  • Exam Essentials
4

Data Collection and Storage

  • Common Data Sources
  • Data Ingestion
  • Data Storage
  • Managing the Data Lifecycle
  • Summary
  • Exam Essentials
5

Data Exploration and Analysis

  • Exploratory Data Analysis
  • Common Data Quality Issues
  • Summary
  • Exam Essentials
6

Data Processing and Preparation

  • Data Transformation
  • Data Enrichment and Augmentation
  • Data Cleaning
  • Handling Class Imbalance
  • Summary
  • Exam Essentials
7

Modeling and Evaluation

  • Types of Models
  • Model Design Concepts
  • Model Evaluation
  • Summary
  • Exam Essentials
8

Model Validation and Deployment

  • Model Validation
  • Communicating Results
  • Model Deployment
  • Machine Learning Operations (MLOps)
  • Summary
  • Exam Essentials
9

Unsupervised Machine Learning

  • Association Rules
  • Clustering
  • Dimensionality Reduction
  • Recommender Systems
  • Summary
  • Exam Essentials
10

Supervised Machine Learning

  • Linear Regression
  • Logistic Regression
  • Discriminant Analysis
  • Naive Bayes
  • Decision Trees
  • Ensemble Methods
  • Summary
  • Exam Essentials
11

Neural Networks and Deep Learning

  • Artificial Neural Networks
  • Deep Neural Networks
  • Summary
  • Exam Essentials
12

Natural Language Processing

  • Natural Language Processing
  • Text Preparation
  • Text Representation
  • Summary
  • Exam Essentials
13

Specialized Applications of Data Science

  • Optimization
  • Computer Vision
  • Summary
  • Exam Essentials

1

Mathematics and Statistical Methods

  • Calculating Probabilities Using a PDF
  • Calculating Skewness and Kurtosis in Data
  • Exploring Discrete Probability Distributions
  • Simulating the CLT
  • Performing a Chi-Squared Test of Independence
  • Performing a Two-Way ANOVA
  • Performing a t-test for Two Samples
  • Working With Matrices
2

Data Collection and Storage

  • Generating Synthetic Data
3

Data Exploration and Analysis

  • Creating a Bar Chart and a Scatterplot
  • Creating a Box and Whisker Plot
  • Creating a Sankey Diagram
4

Data Processing and Preparation

  • Transforming the Data
  • Flattening Hierarchical Data in Python
  • Dealing with Missing Values
  • Performing Oversampling and Undersampling
5

Modeling and Evaluation

  • 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
6

Model Validation and Deployment

  • Selecting the Optimal Model
7

Unsupervised Machine Learning

  • Performing K-Means Clustering
8

Supervised Machine Learning

  • Using Linear Regression for Cost Prediction
  • Using Logistic Regression for Patient Classification
  • Classifying Species using Decision Trees
9

Neural Networks and Deep Learning

  • Creating an ANN Model
  • Using CNN for Image Classification
10

Natural Language Processing

  • Using NLP for Analyzing Tweets
11

Specialized Applications of Data Science

  • Optimizing an ML Model
  • Implementing Computer Vision Techniques

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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. 

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)

  • 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.

While not mandatory, 5+ years of data science experience is strongly recommended to take the CompTIA DataX certification exam. 

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