CompTIA DataX (DY0-001) Certification Study Guide & Practice Exam Tests - GuidesDigest Training

Study Guide Content

Introduction

Overview of CompTIA DataX DY0-001 Certification

Chapter 1: Mathematics and Statistics

Given a scenario, apply the appropriate statistical method or concept

Explain probability and synthetic modeling concepts and their uses

Explain the importance of linear algebra and basic calculus concepts

Compare and contrast various types of temporal models

Chapter 2: Modeling, Analysis, and Outcomes

Given a scenario, use the appropriate exploratory data analysis (EDA) method or process

Given a scenario, analyze common issues with data

Given a scenario, apply data enrichment and augmentation techniques

Given a scenario, conduct a model design iteration process

Given a scenario, analyze results of experiments and testing to justify final model recommendations and selection

Given a scenario, translate results and communicate via appropriate methods and mediums

Chapter 3: Machine Learning

Given a scenario, apply foundational machine-learning concepts

Given a scenario, apply appropriate statistical supervised machine-learning concepts

Given a scenario, apply tree-based supervised machine-learning concepts

Explain concepts related to deep learning

Explain concepts related to unsupervised machine learning

Chapter 4: Operations and Processes

Explain the role of data science in various business functions

Explain the process of and purpose for obtaining different types of data

Explain data ingestion and storage concepts

Given a scenario, implement common data-wrangling techniques

Given a scenario, implement best practices throughout the data science life cycle

Explain the importance of DevOps and MLOps principles in data science

Compare and contrast various deployment environments

Chapter 5: Specialized Applications of Data Science

Compare and contrast optimization concepts

Explain the use and importance of natural language processing (NLP) concepts

Explain the use and importance of computer vision concepts

Explain the purpose of other specialized applications in data science