CompTIA DataX Study Guide: Exam DY0-001 (Sybex Study Guide) 1st Edition 2024 with complete solution

CompTIA DataX Study Guide: Exam DY0-001 (Sybex Study Guide) 1st Edition 2024 with complete solution 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. Table of Contents Cover Table of Contents Title Page Copyright Dedication Acknowledgments About the Author About the Technical Editor 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 Best Practices Summary Exam Essentials Review Questions Chapter 2: Mathematics and Statistical Methods Calculus Probability Distributions Inferential Statistics Linear Algebra Summary Exam Essentials Review Questions Chapter 3: Data Collection and Storage Common Data Sources Data Ingestion Data Storage Managing the Data Lifecycle Summary Exam Essentials Review Questions Chapter 4: Data Exploration and Analysis Exploratory Data Analysis Common Data Quality Issues Summary Exam Essentials Review Questions Chapter 5: Data Processing and Preparation Data Transformation Data Enrichment and Augmentation Data Cleaning Handling Class Imbalance Summary Exam Essentials Review Questions Chapter 6: Modeling and Evaluation Types of Models Model Design Concepts Model Evaluation Summary Exam Essentials Review Questions Chapter 7: Model Validation and Deployment Model Validation Communicating Results Model Deployment Machine Learning Operations (MLOps) Summary Exam Essentials Review Questions Chapter 8: Unsupervised Machine Learning Association Rules Clustering Dimensionality Reduction Recommender Systems Summary Exam Essentials Review Questions Chapter 9: Supervised Machine Learning Linear Regression Logistic Regression Discriminant Analysis Naive Bayes Decision Trees Ensemble Methods Summary Exam Essentials Review Questions Chapter 10: Neural Networks and Deep Learning Artificial Neural Networks Deep Neural Networks Summary Exam Essentials Review Questions Chapter 11: Natural Language Processing Natural Language Processing Text Preparation Text Representation Summary Exam Essentials Review Questions Chapter 12: Specialized Applications of Data Science Optimization Computer Vision Summary Exam Essentials Review Questions 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 End User License Agreement List of Tables Chapter 2 TABLE 2.1 Common continuous probability distributions TABLE 2.2 Common discrete probability distributions Chapter 3 TABLE 3.1 Common licensing types Chapter 4 TABLE 4.1 Frequency distribution of grades TABLE 4.2 Summary of exploratory data analysis methods Chapter 5 TABLE 5.1 Categorical vehicle color values TABLE 5.2 One-hot encoded vehicle color values TABLE 5.3 Ordinal shirt size values TABLE 5.4 Label encoded shirt size values TABLE 5.5 Original age values TABLE 5.6 Age values min-max normalized TABLE 5.7 Original test scores TABLE 5.8 Test scores standardized (Z-score) TABLE 5.9 Exponential population growth data for mice TABLE 5.10 Log transformed population growth data TABLE 5.11 Sample age data TABLE 5.12 Binned sample age data TABLE 5.13 Monthly sales data by product TABLE 5.14 Sales data pivoted by month and product TABLE 5.15 Flattened XML address data TABLE 5.16 Sample housing data TABLE 5.17 Sample housing data with engineered variable Chapter 8 TABLE 8.1 Sample market basket data Chapter 11 TABLE 11.1 Binary representation of a DTM TABLE 11.2 Frequency count representation of a DTM TABLE 11.3 Float-weighted vector representation (TF-IDF) of a DTM TABLE 11.4 Sample GloVe co-occurrence matrix Chapter 12 TABLE 12.1 Common applications of computer vision List of Illustrations Chapter 1 FIGURE 1.1 Data science, machine learning, and artificial intelligence FIGURE 1.2 Sales forecast based on historical data FIGURE 1.3 Using segmentation to identify anomalous data FIGURE 1.4 Biological network FIGURE 1.5 Object recognition in computer vision FIGURE 1.6 The CRISP-DM framework FIGURE 1.7 The DMBoK framework FIGURE 1.8 The Jupyter Notebook IDE Chapter 2 FIGURE 2.1 Curve of showing hypothetical tangent line at FIGURE 2.2 Area under the curve of for between 0 and 3 FIGURE 2.3 Frequency distribution of the lifespan of sample light bulbs test... FIGURE 2.4 Probability density function (PDF) FIGURE 2.5 PDF showing interval of interest (shaded area) FIGURE 2.6 Cumulative distribution function (CDF) FIGURE 2.7 Probability mass function (PMF) FIGURE 2.8 Sampling distributions illustrating the central limit theorem FIGURE 2.9 A vector in two-dimensional space FIGURE 2.10 Linearly dependent vectors FIGURE 2.11 Linearly independent vectors Chapter 3 FIGURE 3.1 Example of a quantitative survey question FIGURE 3.2 Relational database schema FIGURE 3.3 Star schema diagram FIGURE 3.4 Lottery data in the form of a CSV file FIGURE 3.5 Lottery data in the form of a TSV file FIGURE 3.6 Lottery data in the form of a JSON file FIGURE 3.7 Lottery data in the form of an XML file FIGURE 3.8 Example of a data lineage diagram Chapter 4 FIGURE 4.1 Histogram of student math test scores FIGURE 4.2 Box plot of employee salaries FIGURE 4.3 Density plot of age distribution FIGURE 4.4 Quantile-quantile (Q-Q) plot of exam scores against a theoretical... FIGURE 4.5 Bar chart of the distribution of fruit types FIGURE 4.6 Bar chart of the average cost per vehicle type FIGURE 4.7 Scatterplot showing the relationship between salary and years of ... FIGURE 4.8 Line plot of monthly sales revenue over 12 months FIGURE 4.9 Sample correlation plot FIGURE 4.10 Violin plot of the relationship between vehicle type and custome... FIGURE 4.11 Sankey diagram of sales by region, category, and mode of purchas... FIGURE 4.12 Cluster visualization of items segmented by average income, popu... FIGURE 4.13 Sample visualization using principal component analysis (PCA) FIGURE 4.14 Sample nonstationary monthly sales revenue over a 60-month perio... FIGURE 4.15 Sample stationary monthly sales revenue over a 60-month period a... FIGURE 4.16 Sample seasonal monthly sales data over a 60- month period FIGURE 4.17 Decomposed seasonal monthly sales data showing the trend, season... FIGURE 4.18 Deseasonalized monthly sales data over a 60- month period Chapter 5 FIGURE 5.1 Sample skewed distribution before (left) and after (right) being ... FIGURE 5.2 Union of Table A and Table B FIGURE 5.3 Intersection of Table A and Table B FIGURE 5.4 Inner join between Table A and Table B FIGURE 5.5 Left join between Table A and Table B FIGURE 5.6 Right join between Table A and Table B FIGURE 5.7 Full join between Table A and Table B FIGURE 5.8 Anti-join between Table A and Table B FIGURE 5.9 Cross join between Table A and Table B Chapter 6 FIGURE 6.1 Directed acyclic graph showing the relationships between smoking,... FIGURE 6.2 A sample confusion matrix showing actual versus predicted values... FIGURE 6.3 The ROC curve for a sample classifier, a perfect classifier, and ... Chapter 7 FIGURE 7.1 Sample decision tree showing the decision logic for a predictive ... FIGURE 7.2 Sample feature importance chart for a predictive model FIGURE 7.3 Sample residual vs. fitted values plot showing linearity FIGURE 7.4 Sample residual vs. fitted values plot showing heteroscedasticity... FIGURE 7.5 Sample interactive dashboard FIGURE 7.6 Sample ML pipeline illustrating Level 0 MLOps maturity FIGURE 7.7 Sample ML pipeline illustrating Level 1 MLOps maturity FIGURE 7.8 Sample ML pipeline illustrating Level 2 MLOps maturity FIGURE 7.9 Model decay monitoring as part of an MLOps pipeline Chapter 8 FIGURE 8.1 Sample association rule FIGURE 8.2 k-means clustering result showing five clusters FIGURE 8.3 The WCSS for clusters with k values from 1 to 10 FIGURE 8.4 The average silhouette score for clusters with k values from 1 to... FIGURE 8.5 Dendrogram showing result of hierarchical clustering FIGURE 8.6 Dendrogram showing the maximum vertical distance between the merg... FIGURE 8.7 Density-based clustering with DBSCAN FIGURE 8.8 The curse of dimensionality FIGURE 8.9 Illustration of a user-item interactions matrix Chapter 9 FIGURE 9.1 Linear regression line of “best fit” FIGURE 9.2 Curve of the logistic (sigmoid) function FIGURE 9.3 Decision boundaries created using LDA (left) and QDA (right) on t... FIGURE 9.4 Sample decision tree FIGURE 9.5 Sample decision tree Chapter 10 FIGURE 10.1 Simple artificial neural network showing the flow of input and o... FIGURE 10.2 The multilayer perceptron (MLP) showing the input, hidden and ou... FIGURE 10.3 The threshold activation function FIGURE 10.4 The sigmoid activation function FIGURE 10.5 The hyperbolic tangent (tanh) activation function FIGURE 10.6 The rectified linear unit (ReLU) activation function Chapter 11 FIGURE 11.1 The continuous bag of words (CBoW) Word2Vec method FIGURE 11.2 The skip-gram Word2Vec method Chapter 12 FIGURE 12.1 The feasible region of an optimization problem FIGURE 12.2 Unconstrained optimization objective function showing potential ... FIGURE 12.3 Binary image with holes (A) and with the holes filled (B) FIGURE 12.4 Feature extraction