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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
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. Regression: linear, multiple, polynomial
  • 2. Model training, testing and evaluation
  • 3. Classification: logistic regression, k-NN, decision trees
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. 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
SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Executing queries and retrieving results
  • 3. Error handling and best practices
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Choosing appropriate chart types
  • 3. Audience-focused presentation
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Heatmaps, pair plots and correlation matrices

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q67-Q72):

NEW QUESTION # 67
Which Python control structures are commonly used for data filtering and iterative operations in real-world analytics tasks?
(Choose two)

Answer: A,C


NEW QUESTION # 68
Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

Answer: A,D


NEW QUESTION # 69
How would you extract the last three rows of a DataFrame df using position-based indexing?

Answer: B


NEW QUESTION # 70
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

Answer: C


NEW QUESTION # 71
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

Answer: C,D


NEW QUESTION # 72
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