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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which of the following practices contributes most to making Python scripts modular and maintainable?

Answer: A


NEW QUESTION # 83
Which element is essential to justify a conclusion drawn from a dataset?

Answer: D


NEW QUESTION # 84
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

Answer: C


NEW QUESTION # 85
Which method is commonly used to handle missing numerical data in a dataset?

Answer: A


NEW QUESTION # 86
Which technique is considered a primary defense against SQL injection when executing database queries from Python?

Answer: C


NEW QUESTION # 87
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