CompTIA DataX Study Guide : Exam DY0-001.

Format:

Book

Author/Creator:

Nwanganga, Fred.

Series:

Sybex Study Guide Series

Language:

English

Subjects (All):

Information technology--Examinations--Study guides.

Information technology.

Physical Description:

1 online resource (419 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

Demonstrate your Data Science skills by earning the brand-new CompTIA DataX credential In CompTIA DataX Study Guide: Exam DY0-001, data scientist and analytics professor, Fred Nwanganga, delivers a practical, hands-on guide to establishing your credentials as a data science practitioner and succeeding on the CompTIA DataX certification exam. In this book, you'll explore all the domains covered by the new credential, which include key concepts in mathematics and statistics; techniques for modeling, analysis and evaluating outcomes; foundations of machine learning; data science operations and processes; and specialized applications of data science. This up-to-date Study Guide walks you through the new, advanced-level data science certification offered by CompTIA and includes hundreds of practice questions and electronic flashcards that help you to retain and remember the knowledge you need to succeed on the exam and at your next (or current) professional data science role. You'll find: Chapter review questions that validate and measure your readiness for the challenging certification exam Complimentary access to the intuitive Sybex online learning environment, complete with practice questions and a glossary of frequently used industry terminology Material you need to learn and shore up job-critical skills, like data processing and cleaning, machine learning model-selection, and foundational math and modeling concepts Perfect for aspiring and current data science professionals, CompTIA DataX Study Guide is a must-have resource for anyone preparing for the DataX certification exam (DY0-001) and seeking a better, more reliable, and faster way to succeed on the test.

Contents:

Cover

Title Page

Copyright Page

Acknowledgments

About the Author

About the Technical Editor

Contents at a Glance

Contents

Introduction

About the DataX Certification

How This Book Is Organized

Interactive Online Learning Environment and Test Bank

How to Contact the Publisher

Assessment Test

Answers to Assessment Test

Chapter 1 What Is Data Science?

Data Science

Data Science, Machine Learning, and Artificial Intelligence

Common Applications of Data Science

Data Science Best Practices

Data Science Workflow Models

Common Tools and Techniques

Summary

Exam Essentials

Review Questions

Chapter 2 Mathematics and Statistical Methods

Calculus

Derivatives

Integrals

Probability Distributions

Continuous Probability Distributions

Discrete Probability Distributions

Inferential Statistics

Estimating Population Parameters

Hypothesis Testing

Linear Algebra

Vectors

Matrices

Chapter 3 Data Collection and Storage

Common Data Sources

Generated Data

Synthetic Data

Commercial or Public Data

Data Ingestion

Data Ingestion Methods

Infrastructure Requirements

Data Ingestion Pipeline

Data Storage

Structured Storage

Unstructured Storage

Semi-Structured Storage

Compressed Formats

Managing the Data Lifecycle

Data Lineage

Refresh Cycles

Archiving

Chapter 4 Data Exploration and Analysis

Exploratory Data Analysis

Quantitative Variables

Qualitative Variables

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

Choosing an Exploratory Data Analysis Method

Common Data Quality Issues

Structural Issues

Temporal Issues

Completeness Issues

Exam Essentials.

Review Questions

Chapter 5 Data Processing and Preparation

Data Transformation

Encoding

Scaling and Normalization

Transformation Functions

Structural Transformation

Feature Extraction

Data Enrichment and Augmentation

Ground Truth Labeling

Feature Engineering

Merging and Combining Data

Data Cleaning

Identifying Data Errors

Handling Inconsistent Data

Addressing Duplicate Data

Resolving Missing Data

Dealing with Outliers

Handling Class Imbalance

Undersampling

Oversampling

Chapter 6 Modeling and Evaluation

Types of Models

Regressors

Classifiers

Temporal Models

Model Design Concepts

The Holdout Method

The Bias-Variance Trade-off

Feature Selection

Cross-Validation

Bootstrapping

Hyperparameter Tuning

Model Evaluation

Regressor Performance Metrics

Classifier Performance Metrics

Chapter 7 Model Validation and Deployment

Model Validation

Performance Metrics

Inference Performance

Design Constraints

Business Requirements Alignment

Benchmarking

Communicating Results

Data

Visuals

Stakeholders

Ethics

Accessibility

Documentation

Model Deployment

Containerization

Virtualization

Cluster Deployment

Cloud Deployment

On-Premises Deployment

Hybrid Deployment

Edge Deployment

Machine Learning Operations (MLOps)

Automation

Versioning

Testing

Monitoring

Chapter 8 Unsupervised Machine Learning

Association Rules

Identifying Strong Rules

Clustering

Centroid-Based Clustering

Connectivity-Based Clustering

Density-Based Clustering

Dimensionality Reduction

Recommender Systems

Collaborative Filtering.

Content-Based Filtering

Hybrid Filtering

Chapter 9 Supervised Machine Learning

Linear Regression

Regularization

Logistic Regression

Discriminant Analysis

Linear Discriminant Analysis

Quadratic Discriminant Analysis

Naive Bayes

Decision Trees

Ensemble Methods

Bagging

Boosting

Stacking

Chapter 10 Neural Networks and Deep Learning

Artificial Neural Networks

Network Topology

Activation Function

Training Algorithm

Deep Neural Networks

Common Deep Learning Architectures

Common Deep Learning Frameworks

Chapter 11 Natural Language Processing

Natural Language Processing

Text Analysis

Language Understanding

Language Generation

Text Preparation

Tokenization

Stemming

Lemmatization

Removing Stop Words

Part-of-Speech (POS) Tagging

Spelling Normalization

Data Augmentation (Augmenters)

Text Representation

Vectorization

Vector Space Model

Word Embeddings

Chapter 12 Specialized Applications of Data Science

Optimization

Decision Variables

Objective Function

Constraints

Constrained Optimization

Unconstrained Optimization

Computer Vision

Image Acquisition

Image Preprocessing

Appendix Answers to Review Questions

Chapter 1: What Is Data Science?

Chapter 2: Mathematics and Statistical Methods

Chapter 3: Data Collection and Storage

Chapter 4: Data Exploration and Analysis

Chapter 5: Data Processing and Preparation

Chapter 6: Modeling and Evaluation

Chapter 7: Model Validation and Deployment

Chapter 8: Unsupervised Machine Learning.

Chapter 9: Supervised Machine Learning

Chapter 10: Neural Networks and Deep Learning

Chapter 11: Natural Language Processing

Chapter 12: Specialized Applications of Data Science

Index

EULA.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394238996

1394238991

OCLC:

1449624098