PCAD-31-02 Study Materials & PCAD-31-02 Real Question

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
- Model performance and optimization
  • 1. Overfitting, underfitting and generalization
  • 2. Hyperparameter tuning basics
  • 3. Accuracy, precision, recall, F1-score
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. 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
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Frequency distributions and percentiles
  • 2. Measures of central tendency and dispersion
  • 3. Correlation and covariance analysis
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Statistical significance and interpretation
  • 3. Hypothesis testing and confidence intervals
SQL and Database Integration10%- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data integration and merging
  • 3. Data collection methods and sources
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Data standardization and transformation
  • 3. Handling missing, duplicate and invalid values
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray

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PCAD-31-02 Study Materials - 100% Pass Realistic Python Institute Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Real Question

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

NEW QUESTION # 132
Which element is essential to justify a conclusion drawn from a dataset?

Answer: D


NEW QUESTION # 133
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

Answer: A,C


NEW QUESTION # 134
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

Answer: B,D


NEW QUESTION # 135
Which techniques can be used to select a subset of rows and columns from a DataFrame using labels?
(Choose two)

Answer: B,C


NEW QUESTION # 136
Which Pandas methods are commonly used to extract central tendency and dispersion insights from a DataFrame?
(Choose two)

Answer: A,D


NEW QUESTION # 137
......

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