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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
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. Customization and styling
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Heatmaps, pair plots and correlation matrices
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 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
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data integration and merging
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data reshaping and aggregation
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. Regression: linear, multiple, polynomial
  • 2. Model training, testing and evaluation
  • 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 (Q85-Q90):

NEW QUESTION # 85
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: A,C


NEW QUESTION # 86
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

Answer: C,D


NEW QUESTION # 87
Which function is used to reshape a NumPy array without modifying the original data values?

Answer: C


NEW QUESTION # 88
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

Answer: C


NEW QUESTION # 89
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

Answer: C


NEW QUESTION # 90
......

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