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CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep

Name: CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep
Rating: 4.5 (14 reviews)

CompTIA DataX Certification | Master Data Science skills, learn CompTIA DataX DY0-001 Exam Topics and boost your career

Created byOak Academy, OAK Academy Team, Ali̇ CAVDAR

Last updated 6/2026

English

What you'll learn

In this course, you will learn all the topics related to CompTIA DataX.
After completing this course, you will be ready for the CompTIA DataX exam.
CompTIA DataX Course Introduction
Mathematics and Statistics, T-Tests, P-value, Hypothesis Testing, Chi-squared, Analysis of Variance (ANOVA), Confidence Intervals, Classification vs Regression
Regression Error Metrics, Classification Error Metrics, Gini Index, Entropy & Information Gain,ROC AUC,AIC BIC , Correlation Coefficients, Central Limit Theorem
Law of Large Numbers ,Distributions ,Skewness,Kurtosis ,Heteroskedasticity vs. Homoskedasticity ,Probability Density Function (PDF)
Probability Mass Function (PMF), Cumulative Distribution Function (CDF), Probability , Types of Missingness , Oversampling , Stratification
Linear Algebra , Calculus , Time Series , Longitudinal Studies , Survival Analysis , Causal Inference, Exploratory Data Analysis (EDA) Method or Process
Univariate Analysis,Multivariate Analysis, Identification of Object Behaviors and Attributes ,Visualization Type (Charts & Graphs), Box and Whisker Plot
Scatter Plot & Bar Chart , Violin Plot , Line Chart , Histogram – Waterfall , Heatmap & Correlation Plot ,Sankey Diagram & Quartile-Quartile (Q-Q) Plot
Density Plot & Scatter Plot Matrix ,Feature Type Identification, Common Issues Lesson ,Feature Engineering , Data Transformation Lessons
Geocoding , Scaling, Standardization, Additional Data Sources , Design and Specification , Model Selection, Requirements Validation
Performance Evaluation, Performance Benchmarking , Specification Testing Results , Final Performance Measures, Satisfy Business Requirements
Effective Data Visualization and Reporting Techniques, Data Visualization Best Practices and Pitfalls , Chart Accessibility, Data & Model Documentation
Loss Function , Bias-Variance Trade-Off , Variable Feature Selection, Class Imbalance ,Regularization , K-Fold Cross Validation
The Curse of Dimensionality , Occam's Razor (Law of Parsimony) , In-Sample vs. Out-of-Sample , Interpolation vs. Extrapolation , Ensemble Models
Hyperparameter Tuning , Classifiers ,Recommender Systems , Regressors ,Embeddings , Post Hoc Model Explainability , Interpretable Model , Model Drift Causes
Data Leakage , Linear Regression Theory , Logistic Regression Algorithm Theory , Linear Discriminant Analysis (LDA) , Quadratic Discriminant Analysis (QDA)
Association Rules , Naive Bayes, Decision Tree Algorithm Theory,Random Forest Algorithm Theory,Boosting, Bootstrap Aggregation (Bagging)
Artificial Neural Network Architecture , Dropout ,Batch Normalization,Early Stopping, Schedulers
Back Propagation, Shot-based Learning Techniques ,Deep Learning Frameworks, Optimizers, Model Types
K-Means Clustering,Hierarchical Clustering Algorithm Theory,Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Principal Component Analysis (PCA) Theory, t-Distributed Stochastic Neighbor Embedding (t-SNE), K-Nearest Neighbors (KNN)
Singular Value Decomposition (SVD), Compliance, Security, and Privacy Measures, Metrics, and Key Performance Indicators (KPIs)
Requirements Gathering, Generated Data, Synthetic Data, Commercial Public Data, Infrastructure Requirements
Data Format, Streaming, Batching, Pipeline Implementation, Orchestration Automation,Persistence, Refresh Cycles, Archiving, Data Lineage
Merging - Combining, Cleaning, Data Errors, Outliers, Graphs Analysis - Graph Theory, Heuristics, Greedy Algorithms, Reinforcement Learning, Event Detection
Fraud Detection, Anomaly Detection, Multimodal Machine Learning, Optimization for Edge Computing, Signal Processing, Data Replication, , Data Augmentation,
Continuous Integration - Continuous Deployment (CI - CD), Model Deployment,Container Orchestration, Virtualization, Code Isolation, Model Performance Monitoring
Model Validation, Compare and contrast various deployment environments, Containerization,Cloud Deployment, Cluster Deployment, Hybrid Deployment,Edge Deployment
On-Premises Deployment, Constrained Optimization, Unconstrained Optimization , Natural language processing (NLP) concepts, Tokenization - Bag of Words
Word Embeddings, Term Frequency-Inverse Document Frequency (TF-IDF), Document Term Matrix, Edit Distance, Large Language Model, Text Preparation, Sensor Fusion
Topic Modeling, Disambiguation, NLP Applications Lesson 1, Computer vision concepts, Optical Character Recognition, Object - Semantic Segmentation, Tracking
Once you learn these topics in this course, you will pass the exam.

Course content

25 sections • 267 lectures • 28h 8m total length

Installing Anaconda Distribution for Windows10:35
In this lesson we will learn how to install anaconda distributor on windows operating system.
Python, python programming, python examples, python example, python hands-on, pycharm python, python pycharm, python with examples, python: learn python with real python hands-on examples, learn python, real python
Installing Anaconda Distribution for MacOs6:17
In this lesson we will learn how to install anaconda distributor on MacOs operating system.
Whether you work in machine learning or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed upon the premise that there should be only one way (and preferably one obvious way) to do things, a philosophy that has resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing a variety of different tools for programmers suited for many different tasks.
Installing Anaconda Distribution for Linux14:43
In this lesson we will learn how to install anaconda distributor on Linux operating system.
How do I learn Python on my own?
Python has a simple syntax that makes it an excellent programming language for a beginner to learn. To learn Python on your own, you first must become familiar with the syntax. But you only need to know a little bit about Python syntax to get started writing real code; you will pick up the rest as you go. Depending on the purpose of using it, you can then find a good Python tutorial, book, or course that will teach you the programming language by building a complete application that fits your goals. If you want to develop games, then learn Python game development. If you're going to build web applications, you can find many courses that can teach you that, too. Udemy’s online courses are a great place to start if you want to learn Python on your own.
Project Files0:05
Reviewing The Jupyter Notebook12:54
In this tutorial, we will examine the jupyter notebook interface in detail.
What is python?
Python is a general-purpose, object-oriented, high-level programming language. Whether you work in artificial intelligence or finance or are pursuing a career in web development or data science, Python is one of the most important skills you can learn. Python's simple syntax is especially suited for desktop, web, and business applications. Python's design philosophy emphasizes readability and usability. Python was developed on the premise that there should be only one way (and preferably, one obvious way) to do things, a philosophy that resulted in a strict level of code standardization. The core programming language is quite small and the standard library is also large. In fact, Python's large library is one of its greatest benefits, providing different tools for programmers suited for a variety of tasks.
Reviewing The Jupyter Lab11:36
In this tutorial, we will examine the jupyter lab interface in detail.
Python vs. R: What is the Difference?
Python and R are two of today's most popular programming tools. When deciding between Python and R, you need to think about your specific needs. On one hand, Python is relatively easy for beginners to learn, is applicable across many disciplines, has a strict syntax that will help you become a better coder, and is fast to process large datasets. On the other hand, R has over 10,000 packages for data manipulation, is capable of easily making publication-quality graphics, boasts superior capability for statistical modeling, and is more widely used in academia, healthcare, and finance.
Overview of Jupyter Notebook and Google Colab5:31
In this video we try to learn how to use Jupyter Notebook and Google COLAB.
Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

CompTIA DataX Course Introduction7:12
In this lesson, we will introduce the CompTIA DataX course, its objectives, and key takeaways.
Mathematics and Statistics5:22
In this lesson, we will cover fundamental mathematical and statistical concepts essential for data analysis.
T-Tests4:52
In this lesson, we will explore T-tests, a statistical method used to compare means between two groups and determine significance.
P-value6:53
In this lesson, we will explain the concept of the P-value and how it is used in hypothesis testing.
Let's Practice- Paired Samples t-Test in Python6:56
In this lesson, we will perform a paired samples t-test in Python to reinforce our understanding.
Hypothesis Testing5:16
In this lesson, we will examine hypothesis testing, its steps, and its importance in statistical analysis.
Chi squared2:49
In this lesson, we will cover the chi-square test, used to analyze relationships between categorical variables.
Analysis of Variance (ANOVA)7:24
In this lesson, we will introduce ANOVA, a technique for comparing means across multiple groups.
Let's Practice- ANOVA Test in Python5:01
In this lesson, we will conduct an ANOVA test in Python to gain hands-on experience.
Let's Practice- Chi-Square Test in Python4:58
In this lesson, we will apply the chi-square test in Python and interpret the results.
Confidence Intervals2:54
In this lesson, we will explain confidence intervals, their calculation, and their role in decision-making.
Classification vs Regression3:23
In this lesson, we will compare classification and regression, highlighting their differences and applications.
Regression Error Metrics9:48
In this lesson, we will discuss error metrics used to evaluate regression models' performance.
Let's Practice Regression Perfomance Metrics12:03
In this lesson, we will implement regression performance metrics in Python.
Classification Error Metrics18:27
In this lesson, we will explore error metrics for evaluating classification models.
Let's Practice Regression Perfomance Metrics4:36
In this lesson, we will practice classification performance metrics using Python.
Gini Index4:46
In this lesson, we will introduce the Gini Index, a measure of impurity in decision trees.
Entropy & Information Gain4:10
In this lesson, we will explain entropy and information gain, key concepts in decision tree learning.
Let's Practice- Gini Index, Entropy, Information Gain5:08
In this lesson, we will compute Gini Index, entropy, and information gain using Python.
ROC AUC8:52
In this lesson, we will explore ROC curves and AUC as performance measures for classification models.
AIC BIC5:54
In this lesson, we will discuss AIC and BIC, criteria used for model selection in statistics.
Correlation Coefficients6:28
In this lesson, we will examine correlation coefficients, which measure relationships between variables.
Let’s Practice- Correlation Coefficients17:53
In this lesson, we will compute and analyze correlation coefficients using Python.
Central Limit Theorem5:10
In this lesson, we will explain the Central Limit Theorem and its importance in statistical inference.
Law of Large Number2:52
In this lesson, we will discuss the Law of Large Numbers and its implications for probability theory.

Distributions7:54
In this lesson, we will explore different types of distributions and their significance in statistical analysis.
Skewness5:31
In this lesson, we will discuss skewness, a measure of asymmetry in a probability distribution.
Kurtosis5:02
In this lesson, we will examine kurtosis, which describes the tails and peakedness of a distribution.
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 119:07
In this lesson, we will practice working with distributions, skewness, and kurtosis through hands-on exercises.
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 23:46
In this lesson, we will continue practicing distributions, skewness, and kurtosis using real-world data.
Let’s Practice- Distributions, Skewness, Kurtosis Lesson 33:16
In this lesson, we will further solidify our understanding of distributions, skewness, and kurtosis with practical applications.
Heteroskedasticity vs. Homoskedasticity5:53
In this lesson, we will compare heteroskedasticity and homoskedasticity and their impact on regression models.
Probability Density Function(PDF)4:12
In this lesson, we will introduce the Probability Density Function (PDF), which describes the likelihood of continuous random variables.
Let’s Practice- Probability Density Function(PDF)4:17
In this lesson, we will apply and visualize the Probability Density Function (PDF) using Python.
Probability Mass Function(PMF)3:47
In this lesson, we will explain the Probability Mass Function (PMF) and its role in modeling discrete random variables.
Cumulative Distribution Function(CDF)4:03
In this lesson, we will explore the Cumulative Distribution Function (CDF) and its use in probability calculations.
Let’s Practice- Cumulative Distribution Function(CMF)6:30
In this lesson, we will practice working with the Cumulative Distribution Function (CDF) through hands-on examples.
Probability6:40
In this lesson, we will introduce probability concepts and how they are used in data analysis and decision-making.
Types of Missingness6:53
In this lesson, we will examine different types of missing data and their implications for statistical analysis.
Let’s Practice- Types of Missingness18:45
In this lesson, we will practice handling different types of missing data through real-world examples.
Oversampling4:58
In this lesson, we will explore oversampling techniques used to address class imbalances in datasets.
Stratification3:29
In this lesson, we will discuss stratification and its importance in ensuring representative samples in statistical studies.

Linear Algebra Lesson 18:19
In this lesson, we will introduce fundamental concepts of linear algebra and their applications in data science.
Linear Algebra Lesson 24:31
In this lesson, we will explore key operations in linear algebra, including matrix manipulation and vector spaces.
Linear Algebra Lesson 35:11
In this lesson, we will apply linear algebra techniques to real-world data problems through hands-on exercises.
Calculus5:55
In this lesson, we will cover essential calculus concepts, including differentiation and integration, and their relevance in machine learning and optimization.

Time Series Lesson 15:01
In this lesson, we will introduce time series analysis, covering fundamental concepts such as trends, seasonality, and stationarity.
Time Series Lesson 24:35
In this lesson, we will explore advanced time series techniques, including forecasting methods and model evaluation.
Longitudinal Studies3:41
In this lesson, we will discuss longitudinal studies, which involve repeated observations of the same subjects over time.
Survival Analysis6:02
In this lesson, we will introduce survival analysis, a statistical approach for analyzing time-to-event data.
Causal Inference7:56
In this lesson, we will explore causal inference methods used to determine cause-and-effect relationships in data.

Exploratory Data Analysis(EDA) Method or Process5:48
In this lesson, we will explore the EDA process and its role in understanding data.
Univariate Analysis9:16
In this lesson, we will analyze a single variable to explore its distribution.
Let’s Practice- Univariate Analysis17:23
In this lesson, we will practice univariate analysis with hands-on exercises.
Multivariate Analysis7:49
In this lesson, we will analyze multiple variables and their relationships.
Let’s Practice- Multivariate Analysis Lesson 18:26
In this lesson, we will practice multivariate analysis with sample data.
Let’s Practice- Multivariate Analysis Lesson 213:05
In this lesson, we will apply advanced multivariate analysis techniques.
Let’s Practice- Multivariate Analysis Lesson 37:00
In this lesson, we will interpret multivariate analysis results using real data.
Let’s Practice- Multivariate Analysis Lesson 411:23
In this lesson, we will refine our multivariate analysis skills with practical exercises.
Let’s Practice- Multivariate Analysis Lesson 58:02
In this lesson, we will enhance our multivariate analysis abilities with various data.
Let’s Practice- Multivariate Analysis Lesson 612:37
In this lesson, we will solve complex multivariate problems to master the concepts.
Identification of Object Behaviors and Attributes7:09
In this lesson, we will identify object behaviors and attributes in a dataset.
Visualization Type(Charts & Graphs)2:59
In this lesson, we will explore different types of charts and graphs for visualization.
Box and Whisker Plot7:47
In this lesson, we will discuss Box and Whisker Plots for visualizing data spread.
Violin Plot4:56
In this lesson, we will learn about Violin Plots for displaying data distribution.
Scatter Plot & Bar Chart3:41
In this lesson, we will explore Scatter Plots and Bar Charts for analyzing relationships.
Line Chart2:30
In this lesson, we will focus on Line Charts for visualizing trends over time.
Histogram – Waterfall3:27
In this lesson, we will learn about Histograms and Waterfall charts for data distribution.
Heatmap & Correlation Plot4:54
In this lesson, we will use Heatmaps and Correlation Plots to visualize variable relationships.
Sankey Diagram & Quartile-Quartile(Q-Q) Plot5:44
In this lesson, we will introduce Sankey Diagrams and Q-Q Plots for flow and distribution comparisons.
Density Plot & Scatter Plot Matrix5:31
In this lesson, we will explore Density Plots and Scatter Plot Matrices for multivariate data visualization.
Feature Type Identification9:06
In this lesson, we will identify different feature types in a dataset.
Let’s Practice- Feature Type Identification3:35
In this lesson, we will practice identifying feature types in various datasets.

Feature Engineering5:21
In this lesson, we will explore feature engineering techniques to create new features that improve model performance.
Data Transformation Lesson 15:11
In this lesson, we will practice data transformation techniques to prepare data for analysis.
Let’s Practice- One-Hot Encoding5:10
In this lesson, we will apply one-hot encoding to transform categorical variables into numerical data.
Data Transformation Lesson 25:27
In this lesson, we will continue exploring data transformation techniques with pivoting.
Let’s Practice- Pivoting10:46
In this lesson, we will practice pivoting techniques to reshape and summarize data effectively.
Geocoding5:47
In this lesson, we will introduce geocoding and its use in converting addresses into geographic coordinates.
Scaling7:24
In this lesson, we will focus on scaling techniques for normalizing numerical data.
Let’s Practice- Scaling(Robust Scaler)2:40
In this lesson, we will practice scaling using the Robust Scaler method.
Standardization6:40
In this lesson, we will explain standardization and its role in data transformation.
Let’s Practice- Scaling(Standard Scaler)7:09
In this lesson, we will practice scaling using the Standard Scaler method.
Additional Data Sources8:03
In this lesson, we will explore additional data sources to enhance the analysis.
Let’s Practice- Data Sets16:02
In this lesson, we will practice working with various datasets to apply the learned techniques.

Design and Spesification9:22
In this lesson, we will explore the process of design and specification in building effective models and systems.
Performance Evaluation8:15
In this lesson, we will focus on performance evaluation methods to assess model accuracy and efficiency.
Model Selection5:56
In this lesson, we will discuss model selection techniques and criteria for choosing the best model for a given problem.
Let’s Practice- Hyperparameter Tuning13:03
In this lesson, we will practice hyperparameter tuning to optimize model performance through fine-tuning its parameters.
Requirements Validation6:13
In this lesson, we will explore requirements validation techniques to ensure that project goals and specifications are met.

Performance Benchmarking5:13
In this lesson, we will explore performance benchmarking methods to compare model performance against established standards.
Specification Testing Results4:49
In this lesson, we will discuss specification testing results and how they help verify if the model meets predefined criteria.
Final Performance Measures3:12
In this lesson, we will focus on final performance measures and how to evaluate the overall effectiveness of a model.
Satisfy Business Requirements3:49
In this lesson, we will explore how to satisfy business requirements by aligning model outcomes with organizational goals.

Requirements

Basic Knowledge of Mathematics
Watch the course videos completely and in order.
Internet Connection
Any device where you can watch the lesson, such as a mobile phone, computer or tablet.
Learning determination and patience.
Understand how to read graphs

Description

Hello there,
Welcome to the "CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep" course!

CompTIA DataX Certification | Master Data Science skills, learn CompTIA DataX DY0-001 Exam Topics and boost your career

CompTIA DataX is a professional certification that validates your expertise in data analysis, machine learning, statistics, and data science. It ensures that you can analyze large datasets, apply mathematical models, and generate actionable insights to support data-driven decision-making. DataX is designed to help professionals demonstrate their ability to work with data efficiently and communicate findings effectively.

This course provides everything you need to pass the CompTIA DataX exam and excel in data analytics. You'll gain hands-on experience in data analysis, statistics, and machine learning, applying real-world techniques to extract insights and support business decisions. Whether you're a beginner or an experienced professional, this course will equip you with the skills to succeed in data-driven roles.

CompTIA Datax solidifies your comprehensive understanding of critical data tools & concepts. Differentiate yourself with DataX.

Features of CompTIA DataX

CompTIA DataX provides a solid foundation for data science and analytics, making it a valuable certification for professionals looking to advance in data-driven fields. Below are some of the key focus areas:

Mathematical and Statistical Foundations: Learn the core principles of probability, statistics, and linear algebra, which are essential for data analysis.
Machine Learning & Modeling: Understand how to build and evaluate machine learning models to extract insights from data.
Operations and Processes: Gain expertise in handling data processing, data governance, and workflow optimization.
Data Science Applications: Apply your knowledge to real-world case studies and projects, making you job-ready.

What You Will Learn in This Course

This course will provide you with the skills and knowledge required to pass the CompTIA DataX exam and apply data analysis techniques in real-world scenarios. Whether you are a beginner or an experienced professional, this course is designed to take you through the full curriculum step-by-step.

By enrolling in this course, you will:

Develop a strong foundation in mathematics, statistics, and probability for data science.
Learn to build and optimize machine learning models for predictive analytics.
Master data visualization and reporting techniques to communicate insights effectively.
Understand data processing, governance, and security best practices.
Work on real-world projects that will enhance your analytical skills.

Career Opportunities With CompTIA DataX

By completing this course, you will gain the confidence to pursue roles in data science, business intelligence, and analytics. Potential career paths include:

Data Scientist
Data Analyst
Business Intelligence Analyst

CompTIA DataX ensures that you have the knowledge and practical skills to translate business needs into data-driven solutions, apply machine learning techniques, and manage data workflows efficiently.

Are you ready to master data analysis and machine learning? This course is the perfect starting point!

What is CompTIA DataX ?

CompTIA DataX (DY0-001) is an advanced level CompTIA certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science.

The CompTIA DataX (DY0-001) certification expands your understanding of various career paths, opening doors to growth and specialization in the fast-changing field of data science. With this certification, you can pursue roles such as data scientist, quantitative analyst, machine learning engineer, predictive analyst, or artificial intelligence (AI) engineer.

Why choose DataX?

Professionals in the data field looking to upskill and close existing knowledge gaps will benefit from CompTIA DataX. This new certification falls under CompTIA Xpert — a new series of certifications and trainings offered through CompTIA that are geared towards professionals who are looking to gain expert knowledge in business-critical technologies.

What’s the difference between Data+ and DataX?

DataX and Data+ are similar in name but are two distinct certifications. Data+ is geared toward new-to-the-field data science learners, while DataX was designed for highly experienced professionals who want to gain more specialized knowledge of data science.

Why would you want to take this course?

Our answer is simple: The quality of teaching

OAK Academy based in London is an online education company OAK Academy gives education in the field of IT, Software, Design, development in Turkish, English, Portuguese, and a lot of different language on Udemy platform where it has over 2000 hours of video education lessons.

When you enroll, you will feel the OAK Academy`s seasoned developers' expertise

Video and Audio Production Quality

All our content is created/produced as high-quality video/audio to provide you the best learning experience

You will be,

Seeing clearly
Hearing clearly
Moving through the course without distractions

You'll also get:

Lifetime Access to The Course
Fast & Friendly Support in the Q&A section
Udemy Certificate of Completion Ready for Download

We offer full support, answering any questions

Dive in now into the "CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep" course!

CompTIA DataX Certification | Master Data Science skills, learn CompTIA DataX DY0-001 Exam Topics and boost your career

Who this course is for:

Students preparing for the CompTIA DataX Certification
Data Scientists
Data Analysts
Data Manager
Data Specialist
Database Engineers
Information Technology Professionals

CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep

What you'll learn

Explore related topics

Course content

Installations7 lectures • 1hr 2min

The appropriate statistical method or concept25 lectures • 2hr 49min

Probability and synthetic modeling concepts17 lectures • 1hr 54min

The importance of linear algebra and basic calculus concepts.4 lectures • 24min

Compare and contrast various types of temporal models5 lectures • 27min

(EDA) method or process22 lectures • 2hr 42min

Analyze common issues with data3 lectures • 24min

Data enrichment and augmentation techniques.12 lectures • 1hr 26min

Conduct a model design iteration process.5 lectures • 43min

Analyze results of experiments4 lectures • 17min

Requirements

Description

Who this course is for: