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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Exploration and Statistical Analysis25%- 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
- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Correlation and covariance analysis
  • 3. Frequency distributions and percentiles
Topic 2: Data Acquisition and Preprocessing30%- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data formats and storage systems
  • 3. Data collection methods and sources
- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Indexing, filtering, sorting and grouping
Topic 3: Data Visualization and Communication15%- Visualization with Matplotlib and Seaborn
  • 1. Line, bar, scatter, histogram, box plots
  • 2. Customization and styling
  • 3. Heatmaps, pair plots and correlation matrices
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Color, layout and clarity
  • 3. Audience-focused presentation
Topic 4: 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. Error handling and best practices
  • 3. Connecting to SQLite, MySQL or PostgreSQL
Topic 5: 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. Model training, testing and evaluation
  • 2. Regression: linear, multiple, polynomial
  • 3. Classification: logistic regression, k-NN, decision trees

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Python Institute PCAD-31-02 questions and answers

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q49-Q54):

NEW QUESTION # 49
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

Answer: C


NEW QUESTION # 50
Which of the following best describes the use of relational databases in analytics?

Answer: C


NEW QUESTION # 51
Which of the following would best validate the uniqueness of a primary key in a Pandas DataFrame?

Answer: A


NEW QUESTION # 52
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

Answer: D


NEW QUESTION # 53
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

Answer: C


NEW QUESTION # 54
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