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
  • 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
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. 

Certify Your Data Science Skills 

  Learn data science, earn DataX certification, and update your LinkedIn profile. 

$279.99

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