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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Color, layout and clarity
  • 2. Audience-focused presentation
  • 3. Choosing appropriate chart types
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Line, bar, scatter, histogram, box plots
  • 3. Customization and styling
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
Data Exploration and Statistical Analysis25%- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Hypothesis testing and confidence intervals
  • 3. Statistical significance and interpretation
- 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. Correlation and covariance analysis
  • 3. Frequency distributions and percentiles
Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Handling missing, duplicate and invalid values
  • 3. Quality assurance and validation techniques
- 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 reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Model training, testing and evaluation
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Regression: linear, multiple, polynomial
- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Hyperparameter tuning basics
  • 3. Overfitting, underfitting and generalization

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q43-Q48):

NEW QUESTION # 43
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

Answer: D


NEW QUESTION # 44
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)

Answer: A,C


NEW QUESTION # 45
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

Answer: C,D


NEW QUESTION # 46
Which Python tool or library is best suited for retrieving data from HTML tables on web pages?

Answer: B


NEW QUESTION # 47
Which Python construct allows you to iterate over a list of dictionary values and apply conditional logic to accumulate numerical values that meet a certain threshold?

Answer: C


NEW QUESTION # 48
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