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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
Data Modeling and Machine Learning Basics20%- 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
Data Visualization and Communication15%- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Line, bar, scatter, histogram, box plots
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Error handling and best practices
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. Data standardization and transformation
  • 2. Handling missing, duplicate and invalid values
  • 3. Quality assurance and validation techniques
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data collection methods and sources
  • 3. Data integration and merging

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q95-Q100):

NEW QUESTION # 95
Which metric is most appropriate for evaluating the accuracy of a classification model?

Answer: D


NEW QUESTION # 96
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Answer: A


NEW QUESTION # 97
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: A,D


NEW QUESTION # 98
Why is it important to adjust data presentations based on the audience's background?

Answer: C


NEW QUESTION # 99
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

Answer: C


NEW QUESTION # 100
......

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