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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: SQL and Database Integration10%- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Topic 2: Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
- 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
Topic 3: Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray
- 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. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
Topic 4: Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Choosing appropriate chart types
  • 3. Color, layout and clarity
Topic 5: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q120-Q125):

NEW QUESTION # 120
What is the primary role of the pandas library in the Python data science ecosystem?

Answer: D


NEW QUESTION # 121
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

Answer: A


NEW QUESTION # 122
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

Answer: C


NEW QUESTION # 123
What is the primary purpose of using annotations in a data visualization?

Answer: A


NEW QUESTION # 124
What is the benefit of using the __name__ == "__main__" construct in Python scripts?

Answer: D


NEW QUESTION # 125
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