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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
- Inferential statistics
  • 1. Probability concepts and distributions
  • 2. Hypothesis testing and confidence intervals
  • 3. Statistical significance and interpretation
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
Topic 2: SQL and Database Integration10%- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Error handling and best practices
Topic 3: Data Visualization and Communication15%- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Line, bar, scatter, histogram, box plots
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Color, layout and clarity
  • 3. Choosing appropriate chart types
Topic 4: Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Data reshaping and aggregation
  • 3. Indexing, filtering, sorting and grouping
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data collection methods and sources
  • 3. Data formats and storage systems
Topic 5: 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. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees

>> Exam Python Institute PCAD-31-02 Prep <<

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q145-Q150):

NEW QUESTION # 145
What is the primary reason for converting all categorical labels to lowercase during the data cleaning process?

Answer: D


NEW QUESTION # 146
Why is it important to define and apply validation rules consistently across all stages of a data pipeline?

Answer: C


NEW QUESTION # 147
Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

Answer: B,D


NEW QUESTION # 148
What is the main purpose of using the bootstrapping technique in inferential statistics?

Answer: D


NEW QUESTION # 149
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Answer: B


NEW QUESTION # 150
......

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