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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

NEW QUESTION # 49
Which techniques are most effective when designing reusable and testable functions in Python?
(Choose two)

Answer: A,C


NEW QUESTION # 50
Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

Answer: A,B


NEW QUESTION # 51
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

Answer: C


NEW QUESTION # 52
What result will the following Pandas expression return: df['Age'].notnull().all()?

Answer: A


NEW QUESTION # 53
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: B


NEW QUESTION # 54
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