CompTIA DataX Study Guide: Exam DY0-001 (2024)

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Table of Contents1. Cover2. Table of Contents3. Title Page4. Copyright5. Dedication6. Acknowledgments7. About the Author8. About the Technical Editor9. Introduction1. About the DataX Certification2. How This Book Is Organized3. Interactive Online Learning Environment and Test Bank4. How to Contact the Publisher5. Assessment Test6. Answers to Assessment Test10. Chapter 1: What Is Data Science?1. Data Science2. Data Science Best Practices3. Summary4. Exam Essentials5. Review Questions11. Chapter 2: Mathematics and Statistical Methods

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1. Calculus2. Probability Distributions3. Inferential Statistics4. Linear Algebra5. Summary6. Exam Essentials7. Review Questions12. Chapter 3: Data Collection and Storage1. Common Data Sources2. Data Ingestion3. Data Storage4. Managing the Data Lifecycle5. Summary6. Exam Essentials7. Review Questions13. Chapter 4: Data Exploration and Analysis1. Exploratory Data Analysis2. Common Data Quality Issues3. Summary4. Exam Essentials5. Review Questions14. Chapter 5: Data Processing and Preparation1. Data Transformation2. Data Enrichment and Augmentation

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3. Data Cleaning4. Handling Class Imbalance5. Summary6. Exam Essentials7. Review Questions15. Chapter 6: Modeling and Evaluation1. Types of Models2. Model Design Concepts3. Model Evaluation4. Summary5. Exam Essentials6. Review Questions16. Chapter 7: Model Validation and Deployment1. Model Validation2. Communicating Results3. Model Deployment4. Machine Learning Operations (MLOps)5. Summary6. Exam Essentials7. Review Questions17. Chapter 8: Unsupervised Machine Learning1. Association Rules2. Clustering3. Dimensionality Reduction

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4. Recommender Systems5. Summary6. Exam Essentials7. Review Questions18. Chapter 9: Supervised Machine Learning1. Linear Regression2. Logistic Regression3. Discriminant Analysis4. Naive Bayes5. Decision Trees6. Ensemble Methods7. Summary8. Exam Essentials9. Review Questions19. Chapter 10: Neural Networks and Deep Learning1. Artificial Neural Networks2. Deep Neural Networks3. Summary4. Exam Essentials5. Review Questions20. Chapter 11: Natural Language Processing1. Natural Language Processing2. Text Preparation3. Text Representation

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4. Summary5. Exam Essentials6. Review Questions21. Chapter 12: Specialized Applications of Data Science1. Optimization2. Computer Vision3. Summary4. Exam Essentials5. Review Questions22. Appendix: Answers to Review Questions1. Chapter 1: What Is Data Science?2. Chapter 2: Mathematics and Statistical Methods3. Chapter 3: Data Collection and Storage4. Chapter 4: Data Exploration and Analysis5. Chapter 5: Data Processing and Preparation6. Chapter 6: Modeling and Evaluation7. Chapter 7: Model Validation and Deployment8. Chapter 8: Unsupervised Machine Learning9. Chapter 9: Supervised Machine Learning10. Chapter 10: Neural Networks and Deep Learning11. Chapter 11: Natural Language Processing12. Chapter 12: Specialized Applications of Data Science23. Index24. End User License Agreement

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List of Tables1. Chapter 21. TABLE 2.1 Common continuous probability distributions2. TABLE 2.2 Common discrete probability distributions2. Chapter 31. TABLE 3.1 Common licensing types3. Chapter 41. TABLE 4.1 Frequency distribution of grades2. TABLE 4.2 Summary of exploratory data analysis methods4. Chapter 51. TABLE 5.1 Categorical vehicle color values2. TABLE 5.2 One-hot encoded vehicle color values3. TABLE 5.3 Ordinal shirt size values4. TABLE 5.4 Label encoded shirt size values5. TABLE 5.5 Original age values6. TABLE 5.6 Age values min-max normalized7. TABLE 5.7 Original test scores8. TABLE 5.8 Test scores standardized (Z-score)9. TABLE 5.9 Exponential population growth data for mice10. TABLE 5.10 Log transformed population growth data11. TABLE 5.11 Sample age data12. TABLE 5.12 Binned sample age data13. TABLE 5.13 Monthly sales data by product

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14. TABLE 5.14 Sales data pivoted by month and product15. TABLE 5.15 Flattened XML address data16. TABLE 5.16 Sample housing data17. TABLE 5.17 Sample housing data with engineered variable5. Chapter 81. TABLE 8.1 Sample market basket data6. Chapter 111. TABLE 11.1 Binary representation of a DTM2. TABLE 11.2 Frequency count representation of a DTM3. TABLE 11.3 Float-weighted vector representation (TF-IDF) of a DTM4. TABLE 11.4 Sample GloVe co-occurrence matrix7. Chapter 121. TABLE 12.1 Common applications of computer visionList of Illustrations1. Chapter 11. FIGURE 1.1 Data science, machine learning, and artificial intelligence2. FIGURE 1.2 Sales forecast based on historical data3. FIGURE 1.3 Using segmentation to identify anomalous data4. FIGURE 1.4 Biological network5. FIGURE 1.5 Object recognition in computer vision6. FIGURE 1.6 The CRISP-DM framework7. FIGURE 1.7 The DMBoK framework8. FIGURE 1.8 The Jupyter Notebook IDE

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2. Chapter 21. FIGURE 2.1 Curve ofshowing hypothetical tangent lineat2. FIGURE 2.2 Area under the curve offorbetween 0 and 33. FIGURE 2.3 Frequency distribution of the lifespan of sample lightbulbs test...4. FIGURE 2.4 Probability density function (PDF)5. FIGURE 2.5 PDF showing interval of interest (shaded area)6. FIGURE 2.6 Cumulative distribution function (CDF)7. FIGURE 2.7 Probability mass function (PMF)8. FIGURE 2.8 Sampling distributions illustrating the central limittheorem9. FIGURE 2.9 A vector in two-dimensional space10. FIGURE 2.10 Linearly dependent vectors11. FIGURE 2.11 Linearly independent vectors3. Chapter 31. FIGURE 3.1 Example of a quantitative survey question2. FIGURE 3.2 Relational database schema3. FIGURE 3.3 Star schema diagram4. FIGURE 3.4 Lottery data in the form of a CSV file5. FIGURE 3.5 Lottery data in the form of a TSV file6. FIGURE 3.6 Lottery data in the form of a JSON file7. FIGURE 3.7 Lottery data in the form of an XML file8. FIGURE 3.8 Example of a data lineage diagram

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4. Chapter 41. FIGURE 4.1 Histogram of student math test scores2. FIGURE 4.2 Box plot of employee salaries3. FIGURE 4.3 Density plot of age distribution4. FIGURE 4.4 Quantile-quantile (Q-Q) plot of exam scores against atheoretical...5. FIGURE 4.5 Bar chart of the distribution of fruit types6. FIGURE 4.6 Bar chart of the average cost per vehicle type7. FIGURE 4.7 Scatterplot showing the relationship between salary andyears of ...8. FIGURE 4.8 Line plot of monthly sales revenue over 12 months9. FIGURE 4.9 Sample correlation plot10. FIGURE 4.10 Violin plot of the relationship between vehicle type andcustome...11. FIGURE 4.11 Sankey diagram of sales by region, category, and modeof purchas...12. FIGURE 4.12 Cluster visualization of items segmented by averageincome, popu...13. FIGURE 4.13 Sample visualization using principal component analysis(PCA)14. FIGURE 4.14 Sample nonstationary monthly sales revenue over a 60-month perio...15. FIGURE 4.15 Sample stationary monthly sales revenue over a 60-month period a...

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16. FIGURE 4.16 Sample seasonal monthly sales data over a 60-monthperiod17. FIGURE 4.17 Decomposed seasonal monthly sales data showing thetrend, season...18. FIGURE 4.18 Deseasonalized monthly sales data over a 60-monthperiod5. Chapter 51. FIGURE 5.1 Sample skewed distribution before (left) and after (right)being ...2. FIGURE 5.2 Union of Table A and Table B3. FIGURE 5.3 Intersection of Table A and Table B4. FIGURE 5.4 Inner join between Table A and Table B5. FIGURE 5.5 Left join between Table A and Table B6. FIGURE 5.6 Right join between Table A and Table B7. FIGURE 5.7 Full join between Table A and Table B8. FIGURE 5.8 Anti-join between Table A and Table B9. FIGURE 5.9 Cross join between Table A and Table B6. Chapter 61. FIGURE 6.1 Directed acyclic graph showing the relationships betweensmoking,...2. FIGURE 6.2 A sample confusion matrix showing actual versuspredicted values...3. FIGURE 6.3 The ROC curve for a sample classifier, a perfectclassifier, and ...

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7. Chapter 71. FIGURE 7.1 Sample decision tree showing the decision logic for apredictive ...2. FIGURE 7.2 Sample feature importance chart for a predictive model3. FIGURE 7.3 Sample residual vs. fitted values plot showing linearity4. FIGURE 7.4 Sample residual vs. fitted values plot showingheteroscedasticity...5. FIGURE 7.5 Sample interactive dashboard6. FIGURE 7.6 Sample ML pipeline illustrating Level 0 MLOps maturity7. FIGURE 7.7 Sample ML pipeline illustrating Level 1 MLOps maturity8. FIGURE 7.8 Sample ML pipeline illustrating Level 2 MLOps maturity9. FIGURE 7.9 Model decay monitoring as part of an MLOps pipeline8. Chapter 81. FIGURE 8.1 Sample association rule2. FIGURE 8.2 k-means clustering result showing five clusters3. FIGURE 8.3 The WCSS for clusters withkvalues from 1 to 104. FIGURE 8.4 The average silhouette score for clusters withkvaluesfrom 1 to...5. FIGURE 8.5 Dendrogram showing result of hierarchical clustering6. FIGURE 8.6 Dendrogram showing the maximum vertical distancebetween the merg...7. FIGURE 8.7 Density-based clustering with DBSCAN8. FIGURE 8.8 The curse of dimensionality9. FIGURE 8.9 Illustration of a user-item interactions matrix

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9. Chapter 91. FIGURE 9.1 Linear regression line of “best fit”2. FIGURE 9.2 Curve of the logistic (sigmoid) function3. FIGURE 9.3 Decision boundaries created using LDA (left) and QDA(right) on t...4. FIGURE 9.4 Sample decision tree5. FIGURE 9.5 Sample decision tree10. Chapter 101. FIGURE 10.1 Simple artificial neural network showing the flow ofinput and o...2. FIGURE 10.2 The multilayer perceptron (MLP) showing the input,hidden and ou...3. FIGURE 10.3 The threshold activation function4. FIGURE 10.4 The sigmoid activation function5. FIGURE 10.5 The hyperbolic tangent (tanh) activation function6. FIGURE 10.6 The rectified linear unit (ReLU) activation function11. Chapter 111. FIGURE 11.1 The continuous bag of words (CBoW) Word2Vecmethod2. FIGURE 11.2 The skip-gram Word2Vec method12. Chapter 121. FIGURE 12.1 The feasible region of an optimization problem2. FIGURE 12.2 Unconstrained optimization objective function showingpotential ...