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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
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. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
SQL and Database Integration10%- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
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. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q93-Q98):

NEW QUESTION # 93
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

Answer: B


NEW QUESTION # 94
Which practices enhance both readability and professionalism in spreadsheet formatting?
(Choose two)

Answer: B,C


NEW QUESTION # 95
When creating a line chart with Matplotlib, which function is most appropriate to add a text label at a specific data point?

Answer: C


NEW QUESTION # 96
Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

Answer: C


NEW QUESTION # 97
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

Answer: C


NEW QUESTION # 98
......

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