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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Question #1

Which practices are typically part of the data integration process?
(Choose two)

  • A. Data encryption
  • B. Schema alignment
  • C. Format standardization
  • D. Neural network training
Reveal Solution  Discussion  0

Correct Answer: B,C  🗳️

Question #2

What is the function of the melt() operation in Pandas when reshaping wide-format data?

  • A. Converts column values into rows
  • B. Transposes the DataFrame
  • C. Sorts the DataFrame based on a column
  • D. Combines multiple DataFrames
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #3

What is the primary advantage of using web scraping as a data collection method in business analytics?

  • A. It guarantees real-time data accuracy
  • B. It eliminates the need for data cleaning
  • C. It ensures compliance with privacy laws
  • D. It allows automation of data collection from public sources
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #4

Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

  • A. Use raw code outputs for clarity
  • B. Add context through narrative or titles
  • C. Use jargon-rich annotations
  • D. Focus on trend and summary metrics
Reveal Solution  Discussion  0

Correct Answer: A,D  🗳️

Question #5

Why is it important to use a test dataset separate from the training dataset when evaluating a machine learning model?

  • A. To reduce the number of iterations during training
  • B. To optimize the model's training accuracy
  • C. To evaluate the model's performance on unseen data
  • D. To ensure the loss function is minimized
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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