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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees
- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data collection methods and sources
  • 3. Data formats and storage systems
- 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. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
SQL and Database Integration10%- 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. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
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. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- 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. Customization and styling
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Line, bar, scatter, histogram, box plots

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

NEW QUESTION # 117
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

Answer: B,D


NEW QUESTION # 118
When analyzing a dataset, which of the following functions would best help detect skewness or asymmetry in the data distribution?

Answer: A


NEW QUESTION # 119
What does the describe() method in Pandas provide when applied to a numerical DataFrame column?

Answer: D


NEW QUESTION # 120
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

Answer: D


NEW QUESTION # 121
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

Answer: C,D


NEW QUESTION # 122
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