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| Section | Weight | Objectives |
|---|
| Data Acquisition and Preprocessing | 30% | - Data preparation with Pandas and NumPy
- 1. Indexing, filtering, sorting and grouping
- 2. Data structures: Series, DataFrame, ndarray
- 3. Data reshaping and aggregation
- Data collection, integration and storage
- 1. Data formats and storage systems
- 2. Data collection methods and sources
- 3. Data integration and merging
- Data cleaning and validation
- 1. Quality assurance and validation techniques
- 2. Handling missing, duplicate and invalid values
- 3. Data standardization and transformation
|
| Data Modeling and Machine Learning Basics | 20% | - Supervised learning fundamentals
- 1. Model training, testing and evaluation
- 2. Classification: logistic regression, k-NN, decision trees
- 3. Regression: linear, multiple, polynomial
- Model performance and optimization
- 1. Overfitting, underfitting and generalization
- 2. Hyperparameter tuning basics
- 3. Accuracy, precision, recall, F1-score
|
| SQL and Database Integration | 10% | - Relational database concepts
- 1. Tables, keys, relationships and normalization
- SQL querying
- 1. Subqueries and filtering
- 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
- 1. Error handling and best practices
- 2. Connecting to SQLite, MySQL or PostgreSQL
- 3. Executing queries and retrieving results
|
| Data Visualization and Communication | 15% | - Visualization principles and best practices
- 1. Audience-focused presentation
- 2. Choosing appropriate chart types
- 3. Color, layout and clarity
- Data storytelling and reporting
- 1. Written and verbal presentation techniques
- 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
- 1. Customization and styling
- 2. Heatmaps, pair plots and correlation matrices
- 3. Line, bar, scatter, histogram, box plots
|
| Data Exploration and Statistical Analysis | 25% | - Inferential statistics
- 1. Hypothesis testing and confidence intervals
- 2. Probability concepts and distributions
- 3. Statistical significance and interpretation
- Exploratory data analysis
- 1. Feature selection and dimensionality reduction basics
- 2. Identifying patterns, trends and outliers
- Descriptive statistics
- 1. Correlation and covariance analysis
- 2. Measures of central tendency and dispersion
- 3. Frequency distributions and percentiles
|
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Exam Questions for the Python Institute PCAD-31-02 - Improve Your Career Prospects
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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q68-Q73):
NEW QUESTION # 68
What is the primary reason for using parameterized queries instead of directly formatting SQL strings in Python?
- A. To simplify data encryption
- B. To reduce the number of SQL statements
- C. To allow dynamic table creation
- D. To prevent SQL injection vulnerabilities
Answer: D
NEW QUESTION # 69
What is a major challenge in aggregating data from multiple sources?
Response:
- A. Duplicate records and format inconsistencies
- B. Excessive disk space usage
- C. Overfitting in predictive models
- D. Increased algorithmic complexity
Answer: A
NEW QUESTION # 70
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?
- A. GROUP BY
- B. WHERE
- C. HAVING
- D. ORDER BY
Answer: B
NEW QUESTION # 71
What is a key assumption of linear regression that distinguishes it from logistic regression?
- A. The relationship between predictors and the outcome is linear
- B. Predictors must be independent
- C. The target variable is unordered categorical
- D. Output values must be binary
Answer: A
NEW QUESTION # 72
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?
- A. math
- B. re
- C. sqlalchemy
- D. pandas.read_html()
Answer: D
NEW QUESTION # 73
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