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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Data Acquisition and Preprocessing30%- 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
- Data cleaning and validation
  • 1. Quality assurance and validation techniques
  • 2. Handling missing, duplicate and invalid values
  • 3. Data standardization and transformation
Data Modeling and Machine Learning Basics20%- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation
- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Overfitting, underfitting and generalization
  • 3. Hyperparameter tuning basics
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. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Executing queries and retrieving results
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
Data Visualization and Communication15%- Visualization principles and best practices
  • 1. Choosing appropriate chart types
  • 2. Audience-focused presentation
  • 3. Color, layout and clarity
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
- Visualization with Matplotlib and Seaborn
  • 1. Heatmaps, pair plots and correlation matrices
  • 2. Customization and styling
  • 3. Line, bar, scatter, histogram, box plots
Data Exploration and Statistical Analysis25%- Descriptive statistics
  • 1. Measures of central tendency and dispersion
  • 2. Correlation and covariance analysis
  • 3. Frequency distributions and percentiles
- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Hypothesis testing and confidence intervals
  • 3. Probability concepts and distributions

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q134-Q139):

NEW QUESTION # 134
What is a key assumption of linear regression that distinguishes it from logistic regression?

Answer: A


NEW QUESTION # 135
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: C


NEW QUESTION # 136
Which method is commonly used to handle missing numerical data in a dataset?

Answer: C


NEW QUESTION # 137
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: B


NEW QUESTION # 138
Which SQL clause is most appropriate when you need to filter records that meet a specific condition during data retrieval in an analytics pipeline?

Answer: B


NEW QUESTION # 139
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