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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Visualization and Communication15%- 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. 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
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Model training, testing and evaluation
  • 3. Classification: logistic regression, k-NN, decision trees
- Model performance and optimization
  • 1. Overfitting, underfitting and generalization
  • 2. Accuracy, precision, recall, F1-score
  • 3. Hyperparameter tuning basics
Data Exploration and Statistical Analysis25%- 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
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Frequency distributions and percentiles
  • 3. Measures of central tendency and dispersion
SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Executing queries and retrieving results
  • 3. Error handling and best practices
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which of the following statistical measures are considered measures of central tendency?
(Choose two)

Answer: B,C


NEW QUESTION # 40
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

Answer: D


NEW QUESTION # 41
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: B,D


NEW QUESTION # 42
How would you extract the last three rows of a DataFrame df using position-based indexing?

Answer: C


NEW QUESTION # 43
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

Answer: B,D


NEW QUESTION # 44
......

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