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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Hyperparameter tuning basics
  • 2. Accuracy, precision, recall, F1-score
  • 3. Overfitting, underfitting and generalization
- Supervised learning fundamentals
  • 1. Classification: logistic regression, k-NN, decision trees
  • 2. Model training, testing and evaluation
  • 3. Regression: linear, multiple, polynomial
Topic 2: Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Audience-focused presentation
  • 2. Choosing appropriate chart types
  • 3. Color, layout and clarity
- 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. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
Topic 3: Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
Topic 4: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Quality assurance and validation techniques
  • 3. Handling missing, duplicate and invalid values
- Data collection, integration and storage
  • 1. Data collection methods and sources
  • 2. Data integration and merging
  • 3. Data formats and storage systems
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
Topic 5: SQL and Database Integration10%- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- SQL querying
  • 1. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
  • 2. Subqueries and filtering
- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Error handling and best practices

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q91-Q96):

NEW QUESTION # 91
Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?

Answer: D


NEW QUESTION # 92
What does the standard deviation of a dataset represent in the context of statistical analysis?

Answer: B


NEW QUESTION # 93
What is the main purpose of using the bootstrapping technique in inferential statistics?

Answer: A


NEW QUESTION # 94
Why is it important to adjust data presentations based on the audience's background?

Answer: C


NEW QUESTION # 95
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

Answer: D


NEW QUESTION # 96
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