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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
Topic 2: Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data collection methods and sources
  • 3. Data formats and storage systems
- 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. Handling missing, duplicate and invalid values
  • 2. Data standardization and transformation
  • 3. Quality assurance and validation techniques
Topic 3: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Overfitting, underfitting and generalization
  • 2. Accuracy, precision, recall, F1-score
  • 3. Hyperparameter tuning basics
- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
Topic 4: 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. Probability concepts and distributions
  • 3. Statistical significance and interpretation
Topic 5: 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. Connecting to SQLite, MySQL or PostgreSQL
  • 2. Error handling and best practices
  • 3. Executing queries and retrieving results

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PCAD-31-02 Training Materials: Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) & PCAD-31-02 Cram PDF & PCAD-31-02 Exam Guide

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q90-Q95):

NEW QUESTION # 90
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: D


NEW QUESTION # 91
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

Answer: D


NEW QUESTION # 92
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: C


NEW QUESTION # 93
Which of the following statements best describes the structural relationship between Pandas Series and DataFrames?

Answer: B


NEW QUESTION # 94
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

Answer: C


NEW QUESTION # 95
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