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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Data reshaping and aggregation
  • 2. Indexing, filtering, sorting and grouping
  • 3. Data structures: Series, DataFrame, ndarray
- Data collection, integration and storage
  • 1. Data formats and storage systems
  • 2. Data collection methods and sources
  • 3. Data integration and merging
- Data cleaning and validation
  • 1. Handling missing, duplicate and invalid values
  • 2. Quality assurance and validation techniques
  • 3. Data standardization and transformation
Topic 2: Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Feature selection and dimensionality reduction basics
  • 2. Identifying patterns, trends and outliers
- Inferential statistics
  • 1. Hypothesis testing and confidence intervals
  • 2. Probability concepts and distributions
  • 3. Statistical significance and interpretation
- Descriptive statistics
  • 1. Frequency distributions and percentiles
  • 2. Correlation and covariance analysis
  • 3. Measures of central tendency and dispersion
Topic 3: 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. Line, bar, scatter, histogram, box plots
  • 2. Heatmaps, pair plots and correlation matrices
  • 3. Customization and styling
- Data storytelling and reporting
  • 1. Written and verbal presentation techniques
  • 2. Structuring insights and conclusions
Topic 4: SQL and Database Integration10%- SQL querying
  • 1. Subqueries and filtering
  • 2. SELECT, WHERE, JOIN, GROUP BY, aggregate functions
- Relational database concepts
  • 1. Tables, keys, relationships and normalization
- Python-database connectivity
  • 1. Error handling and best practices
  • 2. Connecting to SQLite, MySQL or PostgreSQL
  • 3. Executing queries and retrieving results
Topic 5: 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. Regression: linear, multiple, polynomial
  • 3. Model training, testing and evaluation

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q43-Q48):

NEW QUESTION # 43
In a scatter plot showing the relationship between advertising spend and sales revenue, the points form a tight upward-sloping pattern. What conclusion can be drawn?

Answer: D


NEW QUESTION # 44
What is the recommended Python library to connect and extract tabular data directly from an Excel file?

Answer: B


NEW QUESTION # 45
Which characteristics are typically associated with unstructured data formats?
(Choose two)

Answer: A,D


NEW QUESTION # 46
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: B,C


NEW QUESTION # 47
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

Answer: C


NEW QUESTION # 48
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