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CompTIA DataAI (Preparation for Exam DY0-001)
Duration
5 Days
Course
COMP-DY0001
Available Formats
Classroom Training, Online Training
Course Description
Overview
CompTIA DataAI (formerly DataX) is the premier certification for highly experienced professionals seeking to validate competency in the rapidly evolving field of data science. DataAI equips you with the skills to precisely and confidently demonstrate expertise in handling complex data sets, implementing data-driven solutions, and driving business growth through insightful data interpretation.Master data science skills and prepare for real-world challenges with DataAI CertMaster Perform. This comprehensive course combines instructional lessons, video, hands-on practice, and scenario-based exercises to help you apply your knowledge in practical environments. Aligned with DataAI exam objectives, it’s designed to reinforce your skills and prepare you for the CompTIA DataAI (V1) certification.
- Skill development: Build skills and knowledge of a data scientist through a proven learning progression design that emphasizes context and practice.
- Real-world tasks: Complete activities like modeling and forecasting using tools like Jupyter Notebooks, SQL, R, and Python.
- Interactive labs: Gain hands-on experience in both simulated and real environments that replicate real-world IT scenarios.
- Instructional lessons: Learn with narrative content, videos, and guided labs to build foundational knowledge.
- Skill tracking: Monitor progress with assessments, quizzes, and actionable feedback to ensure continuous improvement.
- Diagnostic readiness: Start with an initial diagnostic to focus your learning on areas needing improvement.
- Year-long access: Study anytime with 12 months of training, redeemable within 12 months of purchase, through CompTIA Central.
Exam details
- Exam version: V1
- Number of questions: maximum of 90 questions
- Types of questions: multiple-choice and performance-based
- Duration: 165 minutes
- Passing score: pass/fail only (no scaled score)
- Language: English and Japanese
- Recommended experience: 5+ years in data science or a similar role
Objectives
During this course, students will learn:
- Apply mathematical and statistical methods appropriately, including data processing, cleaning, statistical modeling, linear algebra, and calculus concepts.
- Utilize appropriate analysis and modeling methods to make justified model recommendations for modeling, analysis, and outcomes.
- Implement machine learning models and understand deep learning concepts to advance data science capabilities.
- Implement data science operations and processes effectively to support organizational goals.
- Demonstrate an understanding of industry trends and specialized applications of data science in various fields.
Audience
- Data Analyst
- E-commerce Analyst
- Data Scientist
- IT Manager
Prerequisites
-
Recommended experience: 5+ years in data science or a similar role
Topics
Mathematics and statistics (17%)
- Statistical methods: applying t-tests, chi-squared tests, analysis of variance (ANOVA), hypothesis testing, regression metrics, gini index, entropy, p-value, receiver operating characteristic/area under the curve (ROC/AUC), akaike information criterion/bayesian information criterion (AIC/BIC), and confusion matrix.
- Probability and modeling: explaining distributions, skewness, kurtosis, heteroskedasticity, probability density function (PDF), probability mass function (PMF), cumulative distribution function (CDF), missingness, oversampling, and stratification.
- Linear algebra and calculus: understanding rank, eigenvalues, matrix operations, distance metrics, partial derivatives, chain rule, and logarithms.
- Temporal models: comparing time series, survival analysis, and causal inference.
- EDA methods: using exploratory data analysis (EDA) techniques like univariate and multivariate analysis, charts, graphs, and feature identification.
- Data issues: analyzing sparse data, non-linearity, seasonality, granularity, and outliers.
- Data enrichment: applying feature engineering, scaling, geocoding, and data transformation.
- Model iteration: conducting design, evaluation, selection, and validation.
- Results communication: creating visualizations, selecting data, avoiding deceptive charts, and ensuring accessibility.
- Foundational concepts: applying loss functions, bias-variance tradeoff, regularization, cross-validation, ensemble models, hyperparameter tuning, and data leakage.
- Supervised learning: applying linear regression, logistic regression, k-nearest neighbors (KNN), naive bayes, and association rules.
- Tree-based learning: applying decision trees, random forest, boosting, and bootstrap aggregation (bagging).
- Deep learning: explaining artificial neural networks (ANN), dropout, batch normalization, backpropagation, and deep-learning frameworks.
- Unsupervised learning: explaining clustering, dimensionality reduction, and singular value decomposition (SVD).
- Business functions: explaining compliance, key performance indicators (KPIs), and requirements gathering.
- Data types: explaining generated, synthetic, and public data.
- Data ingestion: understanding pipelines, streaming, batching, and data lineage.
- Data wrangling: implementing cleaning, merging, imputation, and ground truth labeling.
- Data science life cycle: applying workflow models, version control, clean code, and unit tests.
- DevOps and MLOps: explaining continuous integration/continuous deployment (CI/CD), model deployment, container orchestration, and performance monitoring.
- Deployment environments: comparing containerization, cloud, hybrid, edge, and on-premises deployment.
- Optimization: comparing constrained and unconstrained optimization.
- NLP concepts: explaining natural language processing (NLP) techniques like tokenization, embeddings, term frequency-inverse document frequency (TF-IDF), topic modeling, and NLP applications.
- Computer vision: explaining optical character recognition (OCR), object detection, tracking, and data augmentation.
- Other applications: explaining graph analysis, reinforcement learning, fraud detection, anomaly detection, signal processing, and others.
Self-Paced Training Info
Learn at your own pace with anytime, anywhere training
- Same in-demand topics as instructor-led public and private classes.
- Standalone learning or supplemental reinforcement.
- e-Learning content varies by course and technology.
- View the Self-Paced version of this outline and what is included in the SPVC course.
- Learn more about e-Learning
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Please take a moment to fill out this form. We will get back to you as soon as possible.
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