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Comptia Datax (dy0-001) | Comptia Datax Certification Prep
Published 2/2025
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 5.50 GB | Duration: 28h 9m
CompTIA DataX Certification | Master Data Science skills, learn CompTIA DataX DY0-001 Exam Topics and boost your career
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.
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 careerCompTIA 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 DataXCompTIA 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 CourseThis 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
Students preparing for the CompTIA DataX Certification,Data Scientists,Data Analysts,Data Manager,Data Specialist,Database Engineers,Information Technology Professionals
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