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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Visualization and Communication15%- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Visualization with Matplotlib and Seaborn
  • 1. Customization and styling
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Line, bar, scatter, histogram, box plots
Topic 2: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Overfitting, underfitting and generalization
  • 3. Accuracy, precision, recall, F1-score
- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation
Topic 3: SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Executing queries and retrieving results
  • 3. Connecting to SQLite, MySQL or PostgreSQL
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
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. Measures of central tendency and dispersion
  • 2. Frequency distributions and percentiles
  • 3. Correlation and covariance analysis
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Statistical significance and interpretation
  • 3. Probability concepts and distributions
Topic 5: Data Acquisition and Preprocessing30%- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Handling missing, duplicate and invalid values
  • 3. Quality assurance and validation techniques
- Data preparation with Pandas and NumPy
  • 1. Data structures: Series, DataFrame, ndarray
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data reshaping and aggregation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q84-Q89):

NEW QUESTION # 84
What is the most efficient way to handle missing values in a Pandas DataFrame when preparing a dataset for analysis that must maintain original index structure and avoid row deletions?

Answer: A


NEW QUESTION # 85
When working with relational databases in Python, what are key components required to establish and manage database connections?
(Choose all that apply)

Answer: C,D


NEW QUESTION # 86
What may occur if a model is evaluated using the same data it was trained on?

Answer: C


NEW QUESTION # 87
When analyzing a dataset of customer ages, you calculate the mean, median, and mode. What does it indicate if all three values are approximately equal?

Answer: A


NEW QUESTION # 88
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

Answer: C


NEW QUESTION # 89
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