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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. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
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
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
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. Accuracy, precision, recall, F1-score
  • 2. Overfitting, underfitting and generalization
  • 3. Hyperparameter tuning basics
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Line, bar, scatter, histogram, box plots
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
- 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 formats and storage systems
  • 3. Data collection methods and sources

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q117-Q122):

NEW QUESTION # 117
Which conditions typically necessitate data normalization or scaling before analysis?
(choose two)

Answer: A,C


NEW QUESTION # 118
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

Answer: A


NEW QUESTION # 119
Why might using a pie chart to represent category proportions be less effective than a bar chart?

Answer: A


NEW QUESTION # 120
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

Answer: A,B


NEW QUESTION # 121
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

Answer: A,C


NEW QUESTION # 122
......

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