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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Data reshaping and aggregation
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data integration and merging
  • 3. Data collection methods and sources
SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- 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%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
Data Visualization and Communication15%- 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. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Choosing appropriate chart types
  • 3. Audience-focused presentation
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which method in the pandas library allows efficient loading of large datasets in chunks to conserve memory?

Answer: A


NEW QUESTION # 72
Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?

Answer: C


NEW QUESTION # 73
Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)

Answer: A,D


NEW QUESTION # 74
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

Answer: D


NEW QUESTION # 75
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

Answer: A,D


NEW QUESTION # 76
......

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