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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data reshaping and aggregation
  • 3. Data structures: Series, DataFrame, ndarray
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Probability concepts and distributions
  • 3. Hypothesis testing and confidence intervals

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q22-Q27):

NEW QUESTION # 22
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

Answer: C


NEW QUESTION # 23
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: A,D


NEW QUESTION # 24
Which Python feature allows the reuse of logic in different data processing steps, reducing duplication and improving maintainability?

Answer: D


NEW QUESTION # 25
Which operation would most efficiently apply element-wise multiplication to two NumPy arrays of equal size?

Answer: D


NEW QUESTION # 26
Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

Answer: D


NEW QUESTION # 27
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