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

Name: CompTIA DataAI (DY0-001) | CompTIA DataAI Certification Prep
Rating: 4.5 (17 reviews)

CompTIA DataAI Certification | Master Data Science skills.Learn CompTIA DataAI DY0-001 Exam Topics and boost your career

Created byOak Academy, OAK Academy Team, Ali̇ CAVDAR

Last updated 11/2026

English

English [Auto],

What you'll learn

In this course, you will learn all the topics related to CompTIA DataAI.
After completing this course, you will be ready for the CompTIA DataAI exam.
CompTIA DataAI 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

27 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 DataAi Course Introduction7:12
In this lesson, we will introduce the CompTIA DataAI 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

Welcome to the “CompTIA DataAI (DY0-001) | CompTIA DataAI Certification Prep” course

CompTIA DataAI Certification | Master Data Science skills. Learn CompTIA DataAI DY0-001 Exam Topics and boost your career

Plenty of people say they work with data. CompTIA DataAI (DY0-001) is how you show it. It is CompTIA’s advanced data science certification, and this course takes you through its exam topics one by one: the math and statistics underneath, how models are built and judged, how machine learning really works, and how data science runs inside an organization. Every topic is explained in depth and step by step, so you understand why a method works and when to use it, not just its name.

This is where data skills get serious. You work through probability, statistics, and linear algebra, then move into modeling and machine learning, data processing and governance, and the specialized applications that put it all to use. You also learn to communicate what the data says, because a model nobody can explain is a model nobody trusts.

Then you check yourself. Sample questions at the end of the chapters follow the style of DataAI questions, so you see right away which topics you have mastered and which ones deserve another pass.

Whether you’re a data analyst ready to move up, a business intelligence professional heading toward data science, an IT or engineering professional adding machine learning to your toolkit, or a data scientist who wants the skills formally recognized, you can start here. The course walks you through the full DataAI curriculum at your own pace.

Why Learn CompTIA DataAI?

CompTIA DataAI is a vendor-neutral, advanced-level certification for professionals who work with data science. It validates that you can analyze large datasets, apply mathematical and statistical models, use machine learning, and turn the results into decisions a business can act on. It sits in CompTIA’s Xpert series, which is aimed at professionals building expert knowledge in business-critical technologies.

It points toward roles such as data scientist, machine learning engineer, quantitative analyst, predictive analyst, and AI engineer. Statistics, modeling, and machine learning are the core of those jobs, so what you learn here is useful well beyond the exam.

What Skills Will You Learn in This Course?

Mathematics and Statistics: Probability, statistics, and linear algebra, the foundations behind every data science method.
Modeling, Analysis and Outcomes: Building and evaluating models, interpreting results, and turning analysis into decisions.
Machine Learning: How machine learning models are built, trained, optimized, and evaluated for predictive analytics.
Operations and Processes: Data processing, data governance, security best practices, and workflow optimization.
Specialized Applications of Data Science: Applying data science concepts to real-world scenarios.
Communicating Insights: Data visualization and reporting techniques that explain findings clearly.
Exam Preparation: Sample questions at the end of the chapters, so the style of DataAI questions feels familiar on exam day.

Who Is This Course For?

Data Analysts Ready to Level Up: Analysts who want to go beyond reporting into modeling and machine learning.
Aspiring Data Scientists: Learners aiming at data scientist, predictive analyst, or quantitative analyst roles.
Business Intelligence Professionals: People who want deeper statistical and machine learning skills behind their dashboards.
IT, Software and Engineering Professionals: Technical professionals moving toward machine learning and AI engineering.
CompTIA DataAI Candidates: Anyone preparing for DY0-001 who wants every exam topic in one structured course.
Data+ Holders: Professionals who earned Data+ and want the next step in CompTIA’s data path.

Some familiarity with data and basic math makes the early chapters smoother. The course explains each concept step by step as it comes up.

Why Choose This Course?

It goes where the exam goes. The course covers the math and statistics, modeling, machine learning, operations, and specialized applications that DataAI is built around, and it takes the time to explain each one properly.

You learn the reasoning, not only the vocabulary. Every topic is explained step by step, so you understand why a model fits a problem and what its results mean. That reasoning is what scenario-based exam questions test.

Math becomes usable. Probability, statistics, and linear algebra are taught as tools you apply to data, not as theory you memorize.

You learn to explain your results. Visualization and reporting sit next to modeling, because data science only creates value when people understand it.

You test yourself as you go. Sample questions at the end of the chapters show you what you have understood while it is still fresh.

You study at your own pace and can revisit any lesson as often as you like.

Frequently Asked Questions

What is CompTIA DataAI (DY0-001)?
It is an advanced, vendor-neutral CompTIA certification for professionals who work in data science. It validates your ability to analyze large datasets, apply statistical and machine learning models, and turn results into business decisions.

Is CompTIA DataAI for beginners?
No, DataAI is designed for experienced data professionals. This course explains each topic step by step, but it works best if you already have some familiarity with data and basic math. If you’re brand new to data, CompTIA Data+ is the better first step, and DataAI is the natural next one.

What is the difference between Data+ and DataAI?
Data+ is an early-career certification focused on analyzing and presenting data. DataAI is advanced and goes into the mathematics, modeling, and machine learning behind data science. Start with Data+ if you’re building your foundation, and choose DataAI if you already work with data and want to specialize.

Which exam does this course cover?
CompTIA DataAI DY0-001. Download the official exam objectives and use them as a checklist while you study, so you can tick off each topic as you finish it.

Do I need strong math skills?
DataAI includes probability, statistics, and linear algebra, so math is part of the journey. The course explains these topics in context, applied to data, so you learn them as tools rather than abstract theory.

What jobs can DataAI lead to?
DataAI points toward roles such as data scientist, machine learning engineer, quantitative analyst, predictive analyst, and AI engineer. Requirements depend on the employer and the position, so compare a few job listings with the course topics.

What is CompTIA Xpert?
CompTIA Xpert is CompTIA’s series of advanced certifications for professionals building expert knowledge in business-critical technologies. DataAI is part of this series.

Will this course help me pass the DataAI exam?
It is built to help you understand the exam topics and prepare with confidence. Your result depends on your study effort, your understanding, and your performance on exam day.

What if I get stuck on a topic?
Post your question in the course Q&A section. Our instructors answer questions within 48 hours at the latest, and you can rewatch any lesson with lifetime access.

Why Choose OAK Academy?

Our answer is simple: the quality of teaching.

Based in London, OAK Academy is an online education company offering courses on Udemy in IT, software, design, and development, in many languages. We keep adding new courses and update existing ones so students stay current.

Clear Video and Audio: See clearly, hear clearly, and learn without distractions.
Lifetime Access: Come back to any lesson whenever you want to review a topic.
Fast, Friendly Q&A Support: Ask your questions in the course Q&A section.
Certificate of Completion: Download your Udemy certificate when you finish the course.

Start Your CompTIA DataAI DY0-001 Journey Today

Master the math, build the models, explain the results, and walk into your DY0-001 preparation knowing what to expect.

Enroll today in the CompTIA DataAI (DY0-001) | CompTIA DataAI Certification Prep and take your next step in data science.

Who this course is for:

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

CompTIA DataAI (DY0-001) | CompTIA DataAI 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: