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

Name: CompTIA DataX Study Guide: Exam DY0-001 (2024)
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Author: Lucas Allen

Ensure you're fully prepared with CompTIA DataX Study Guide: Exam DY0-001 (2024), a certification guide that covers critical exam topics.

Lucas Allen

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Table of Contents
1. Cover
2. Table of Contents
3. Title Page
4. Copyright
5. Dedication
6. Acknowledgments
7. About the Author
8. About the Technical Editor
9. Introduction
1. About the DataX Certification
2. How This Book Is Organized
3. Interactive Online Learning Environment and Test Bank
4. How to Contact the Publisher
5. Assessment Test
6. Answers to Assessment Test
10. Chapter 1: What Is Data Science?
1. Data Science
2. Data Science Best Practices
3. Summary
4. Exam Essentials
5. Review Questions
11. Chapter 2: Mathematics and Statistical Methods

1. Calculus
2. Probability Distributions
3. Inferential Statistics
4. Linear Algebra
5. Summary
6. Exam Essentials
7. Review Questions
12. Chapter 3: Data Collection and Storage
1. Common Data Sources
2. Data Ingestion
3. Data Storage
4. Managing the Data Lifecycle
5. Summary
6. Exam Essentials
7. Review Questions
13. Chapter 4: Data Exploration and Analysis
1. Exploratory Data Analysis
2. Common Data Quality Issues
3. Summary
4. Exam Essentials
5. Review Questions
14. Chapter 5: Data Processing and Preparation
1. Data Transformation
2. Data Enrichment and Augmentation

3. Data Cleaning
4. Handling Class Imbalance
5. Summary
6. Exam Essentials
7. Review Questions
15. Chapter 6: Modeling and Evaluation
1. Types of Models
2. Model Design Concepts
3. Model Evaluation
4. Summary
5. Exam Essentials
6. Review Questions
16. Chapter 7: Model Validation and Deployment
1. Model Validation
2. Communicating Results
3. Model Deployment
4. Machine Learning Operations (MLOps)
5. Summary
6. Exam Essentials
7. Review Questions
17. Chapter 8: Unsupervised Machine Learning
1. Association Rules
2. Clustering
3. Dimensionality Reduction

4. Recommender Systems
5. Summary
6. Exam Essentials
7. Review Questions
18. Chapter 9: Supervised Machine Learning
1. Linear Regression
2. Logistic Regression
3. Discriminant Analysis
4. Naive Bayes
5. Decision Trees
6. Ensemble Methods
7. Summary
8. Exam Essentials
9. Review Questions
19. Chapter 10: Neural Networks and Deep Learning
1. Artificial Neural Networks
2. Deep Neural Networks
3. Summary
4. Exam Essentials
5. Review Questions
20. Chapter 11: Natural Language Processing
1. Natural Language Processing
2. Text Preparation
3. Text Representation

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4. Summary
5. Exam Essentials
6. Review Questions
21. Chapter 12: Specialized Applications of Data Science
1. Optimization
2. Computer Vision
3. Summary
4. Exam Essentials
5. Review Questions
22. Appendix: Answers to Review Questions
1. Chapter 1: What Is Data Science?
2. Chapter 2: Mathematics and Statistical Methods
3. Chapter 3: Data Collection and Storage
4. Chapter 4: Data Exploration and Analysis
5. Chapter 5: Data Processing and Preparation
6. Chapter 6: Modeling and Evaluation
7. Chapter 7: Model Validation and Deployment
8. Chapter 8: Unsupervised Machine Learning
9. Chapter 9: Supervised Machine Learning
10. Chapter 10: Neural Networks and Deep Learning
11. Chapter 11: Natural Language Processing
12. Chapter 12: Specialized Applications of Data Science
23. Index
24. End User License Agreement

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List of Tables
1. Chapter 2
1. TABLE 2.1 Common continuous probability distributions
2. TABLE 2.2 Common discrete probability distributions
2. Chapter 3
1. TABLE 3.1 Common licensing types
3. Chapter 4
1. TABLE 4.1 Frequency distribution of grades
2. TABLE 4.2 Summary of exploratory data analysis methods
4. Chapter 5
1. TABLE 5.1 Categorical vehicle color values
2. TABLE 5.2 One-hot encoded vehicle color values
3. TABLE 5.3 Ordinal shirt size values
4. TABLE 5.4 Label encoded shirt size values
5. TABLE 5.5 Original age values
6. TABLE 5.6 Age values min-max normalized
7. TABLE 5.7 Original test scores
8. TABLE 5.8 Test scores standardized (Z-score)
9. TABLE 5.9 Exponential population growth data for mice
10. TABLE 5.10 Log transformed population growth data
11. TABLE 5.11 Sample age data
12. TABLE 5.12 Binned sample age data
13. TABLE 5.13 Monthly sales data by product

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14. TABLE 5.14 Sales data pivoted by month and product
15. TABLE 5.15 Flattened XML address data
16. TABLE 5.16 Sample housing data
17. TABLE 5.17 Sample housing data with engineered variable
5. Chapter 8
1. TABLE 8.1 Sample market basket data
6. Chapter 11
1. TABLE 11.1 Binary representation of a DTM
2. TABLE 11.2 Frequency count representation of a DTM
3. TABLE 11.3 Float-weighted vector representation (TF-IDF) of a DTM
4. TABLE 11.4 Sample GloVe co-occurrence matrix
7. Chapter 12
1. TABLE 12.1 Common applications of computer vision
List of Illustrations
1. Chapter 1
1. FIGURE 1.1 Data science, machine learning, and artificial intelligence
2. FIGURE 1.2 Sales forecast based on historical data
3. FIGURE 1.3 Using segmentation to identify anomalous data
4. FIGURE 1.4 Biological network
5. FIGURE 1.5 Object recognition in computer vision
6. FIGURE 1.6 The CRISP-DM framework
7. FIGURE 1.7 The DMBoK framework
8. FIGURE 1.8 The Jupyter Notebook IDE

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2. Chapter 2
1. FIGURE 2.1 Curve of showing hypothetical tangent line
at
2. FIGURE 2.2 Area under the curve of for between 0 and 3
3. FIGURE 2.3 Frequency distribution of the lifespan of sample light
bulbs 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 limit
theorem
9. FIGURE 2.9 A vector in two-dimensional space
10. FIGURE 2.10 Linearly dependent vectors
11. FIGURE 2.11 Linearly independent vectors
3. Chapter 3
1. FIGURE 3.1 Example of a quantitative survey question
2. FIGURE 3.2 Relational database schema
3. FIGURE 3.3 Star schema diagram
4. FIGURE 3.4 Lottery data in the form of a CSV file
5. FIGURE 3.5 Lottery data in the form of a TSV file
6. FIGURE 3.6 Lottery data in the form of a JSON file
7. FIGURE 3.7 Lottery data in the form of an XML file
8. FIGURE 3.8 Example of a data lineage diagram

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4. Chapter 4
1. FIGURE 4.1 Histogram of student math test scores
2. FIGURE 4.2 Box plot of employee salaries
3. FIGURE 4.3 Density plot of age distribution
4. FIGURE 4.4 Quantile-quantile (Q-Q) plot of exam scores against a
theoretical...
5. FIGURE 4.5 Bar chart of the distribution of fruit types
6. FIGURE 4.6 Bar chart of the average cost per vehicle type
7. FIGURE 4.7 Scatterplot showing the relationship between salary and
years of ...
8. FIGURE 4.8 Line plot of monthly sales revenue over 12 months
9. FIGURE 4.9 Sample correlation plot
10. FIGURE 4.10 Violin plot of the relationship between vehicle type and
custome...
11. FIGURE 4.11 Sankey diagram of sales by region, category, and mode
of purchas...
12. FIGURE 4.12 Cluster visualization of items segmented by average
income, 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-month
period
17. FIGURE 4.17 Decomposed seasonal monthly sales data showing the
trend, season...
18. FIGURE 4.18 Deseasonalized monthly sales data over a 60-month
period
5. Chapter 5
1. FIGURE 5.1 Sample skewed distribution before (left) and after (right)
being ...
2. FIGURE 5.2 Union of Table A and Table B
3. FIGURE 5.3 Intersection of Table A and Table B
4. FIGURE 5.4 Inner join between Table A and Table B
5. FIGURE 5.5 Left join between Table A and Table B
6. FIGURE 5.6 Right join between Table A and Table B
7. FIGURE 5.7 Full join between Table A and Table B
8. FIGURE 5.8 Anti-join between Table A and Table B
9. FIGURE 5.9 Cross join between Table A and Table B
6. Chapter 6
1. FIGURE 6.1 Directed acyclic graph showing the relationships between
smoking,...
2. FIGURE 6.2 A sample confusion matrix showing actual versus
predicted values...
3. FIGURE 6.3 The ROC curve for a sample classifier, a perfect
classifier, and ...

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7. Chapter 7
1. FIGURE 7.1 Sample decision tree showing the decision logic for a
predictive ...
2. FIGURE 7.2 Sample feature importance chart for a predictive model
3. FIGURE 7.3 Sample residual vs. fitted values plot showing linearity
4. FIGURE 7.4 Sample residual vs. fitted values plot showing
heteroscedasticity...
5. FIGURE 7.5 Sample interactive dashboard
6. FIGURE 7.6 Sample ML pipeline illustrating Level 0 MLOps maturity
7. FIGURE 7.7 Sample ML pipeline illustrating Level 1 MLOps maturity
8. FIGURE 7.8 Sample ML pipeline illustrating Level 2 MLOps maturity
9. FIGURE 7.9 Model decay monitoring as part of an MLOps pipeline
8. Chapter 8
1. FIGURE 8.1 Sample association rule
2. FIGURE 8.2 k-means clustering result showing five clusters
3. FIGURE 8.3 The WCSS for clusters with k values from 1 to 10
4. FIGURE 8.4 The average silhouette score for clusters with k values
from 1 to...
5. FIGURE 8.5 Dendrogram showing result of hierarchical clustering
6. FIGURE 8.6 Dendrogram showing the maximum vertical distance
between the merg...
7. FIGURE 8.7 Density-based clustering with DBSCAN
8. FIGURE 8.8 The curse of dimensionality
9. FIGURE 8.9 Illustration of a user-item interactions matrix

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9. Chapter 9
1. FIGURE 9.1 Linear regression line of “best fit”
2. FIGURE 9.2 Curve of the logistic (sigmoid) function
3. FIGURE 9.3 Decision boundaries created using LDA (left) and QDA
(right) on t...
4. FIGURE 9.4 Sample decision tree
5. FIGURE 9.5 Sample decision tree
10. Chapter 10
1. FIGURE 10.1 Simple artificial neural network showing the flow of
input and o...
2. FIGURE 10.2 The multilayer perceptron (MLP) showing the input,
hidden and ou...
3. FIGURE 10.3 The threshold activation function
4. FIGURE 10.4 The sigmoid activation function
5. FIGURE 10.5 The hyperbolic tangent (tanh) activation function
6. FIGURE 10.6 The rectified linear unit (ReLU) activation function
11. Chapter 11
1. FIGURE 11.1 The continuous bag of words (CBoW) Word2Vec
method
2. FIGURE 11.2 The skip-gram Word2Vec method
12. Chapter 12
1. FIGURE 12.1 The feasible region of an optimization problem
2. FIGURE 12.2 Unconstrained optimization objective function showing
potential ...

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