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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q61-Q66):

NEW QUESTION # 61
What is the outcome of the following code?
data = [5, 10, 15]
result = [x**2 for x in data if x > 5]
print(result)

Answer: A


NEW QUESTION # 62
What is the best approach when comparing SQL NULL values with Python data during conditional checks?

Answer: B


NEW QUESTION # 63
What is the function of the melt() operation in Pandas when reshaping wide-format data?

Answer: C


NEW QUESTION # 64
Which term best describes the process of combining customer data from multiple systems into a single unified dataset?

Answer: B


NEW QUESTION # 65
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

Answer: A,B


NEW QUESTION # 66
......

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