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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Customization and styling
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Data reshaping and aggregation
- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
SQL and Database Integration10%- 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
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Hypothesis testing and confidence intervals
  • 3. Statistical significance and interpretation
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Hyperparameter tuning basics
  • 3. Overfitting, underfitting and generalization
- 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 (Q141-Q146):

NEW QUESTION # 141
What result will the following Pandas expression return: df['Age'].notnull().all()?

Answer: A


NEW QUESTION # 142
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

Answer: B


NEW QUESTION # 143
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

Answer: A,C


NEW QUESTION # 144
Which characteristics are typically associated with unstructured data formats?
(Choose two)

Answer: A,D


NEW QUESTION # 145
Which data collection method is most appropriate when gathering opinions from a target audience at scale?

Answer: C


NEW QUESTION # 146
......

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