CompTIA DataX (DY0-001) | CompTIA DataX Certification Prep
CompTIA DataX Certification | Master Data Science skills, learn CompTIA DataX DY0-001 Exam Topics and boost your career
176
students
28 hours
content
May 2025
last update
$44.99
regular price
What you will 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 Gallery
6469565
udemy ID
17/02/2025
course created date
30/04/2025
course indexed date
adedayo0001
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