New PCAD-31-02 Test Experience | PCAD-31-02 Pdf Format

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q132-Q137):

NEW QUESTION # 132
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

Answer: D


NEW QUESTION # 133
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

Answer: A


NEW QUESTION # 134
Which of the following would most likely be classified as unstructured data?

Answer: C


NEW QUESTION # 135
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

Answer: C


NEW QUESTION # 136
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)

Answer: A,D


NEW QUESTION # 137
......

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