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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Data Visualization and Communication | 15% | - Visualization principles and best practices
- 1. Color, layout and clarity
- 2. Audience-focused presentation
- 3. Choosing appropriate chart types
- Data storytelling and reporting
- 1. Structuring insights and conclusions
- 2. Written and verbal presentation techniques
- 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% | - Descriptive statistics
- 1. Frequency distributions and percentiles
- 2. Correlation and covariance analysis
- 3. Measures of central tendency and dispersion
- Exploratory data analysis
- 1. Identifying patterns, trends and outliers
- 2. Feature selection and dimensionality reduction basics
- Inferential statistics
- 1. Hypothesis testing and confidence intervals
- 2. Statistical significance and interpretation
- 3. Probability concepts and distributions
|
| Data Modeling and Machine Learning Basics | 20% | - Supervised learning fundamentals
- 1. Classification: logistic regression, k-NN, decision trees
- 2. Regression: linear, multiple, polynomial
- 3. Model training, testing and evaluation
- Model performance and optimization
- 1. Overfitting, underfitting and generalization
- 2. Accuracy, precision, recall, F1-score
- 3. Hyperparameter tuning basics
|
| SQL and Database Integration | 10% | - Relational database concepts
- 1. Tables, keys, relationships and normalization
- SQL querying
- 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- 2. Subqueries and filtering
- Python-database connectivity
- 1. Executing queries and retrieving results
- 2. Error handling and best practices
- 3. Connecting to SQLite, MySQL or PostgreSQL
|
| Data Acquisition and Preprocessing | 30% | - Data cleaning and validation
- 1. Data standardization and transformation
- 2. Quality assurance and validation techniques
- 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
- 1. Data collection methods and sources
- 2. Data formats and storage systems
- 3. Data integration and merging
- Data preparation with Pandas and NumPy
- 1. Data reshaping and aggregation
- 2. Data structures: Series, DataFrame, ndarray
- 3. Indexing, filtering, sorting and grouping
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
1. Which method in a Python class is responsible for initializing the object's attributes at the time of creation?
A) __init__()
B) __getattr__()
C) __str__()
D) __main__()
2. What is the primary role of the pandas library in the Python data science ecosystem?
A) Visualizing large datasets
B) Reducing execution time of compiled code
C) Manipulating labeled data structures
D) Performing statistical tests
3. Which practices are typically part of the data integration process?
(Choose two)
A) Data encryption
B) Neural network training
C) Format standardization
D) Schema alignment
4. Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)
A) It requires a large population sample
B) It uses random sampling with replacement
C) It enables confidence interval estimation
D) It assumes a normal distribution
5. Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?
A) numpy.outer()
B) numpy.matrix()
C) array1 * array2
D) array1.dot(array2)
Solutions:
Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C,D | Question # 4 Answer: B,C | Question # 5 Answer: C |