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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
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. Color, layout and clarity
  • 3. Audience-focused presentation
- Data storytelling and reporting
  • 1. Structuring insights and conclusions
  • 2. Written and verbal presentation techniques
Data Modeling and Machine Learning Basics20%- Model performance and optimization
  • 1. Accuracy, precision, recall, F1-score
  • 2. Overfitting, underfitting and generalization
  • 3. Hyperparameter tuning basics
- Supervised learning fundamentals
  • 1. Regression: linear, multiple, polynomial
  • 2. Classification: logistic regression, k-NN, decision trees
  • 3. Model training, testing and evaluation
Data Acquisition and Preprocessing30%- Data preparation with Pandas and NumPy
  • 1. Indexing, filtering, sorting and grouping
  • 2. Data structures: Series, DataFrame, ndarray
  • 3. Data reshaping and aggregation
- Data collection, integration and storage
  • 1. Data integration and merging
  • 2. Data collection methods and sources
  • 3. Data formats and storage systems
- Data cleaning and validation
  • 1. Data standardization and transformation
  • 2. Quality assurance and validation techniques
  • 3. Handling missing, duplicate and invalid values
SQL and Database Integration10%- Python-database connectivity
  • 1. Executing queries and retrieving results
  • 2. Error handling and best practices
  • 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
Data Exploration and Statistical Analysis25%- Exploratory data analysis
  • 1. Identifying patterns, trends and outliers
  • 2. Feature selection and dimensionality reduction basics
- Inferential statistics
  • 1. Statistical significance and interpretation
  • 2. Probability concepts and distributions
  • 3. Hypothesis testing and confidence intervals
- Descriptive statistics
  • 1. Correlation and covariance analysis
  • 2. Measures of central tendency and dispersion
  • 3. Frequency distributions and percentiles

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

NEW QUESTION # 134
What will the following function return?
def average(*args):
return sum(args) / len(args)
average(4, 5, 6)

Answer: C


NEW QUESTION # 135
Which of the following statements correctly describe best practices when managing Python modules and packages in data analytics workflows?
(Choose two)

Answer: B,C


NEW QUESTION # 136
Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

Answer: C


NEW QUESTION # 137
What is a major challenge in aggregating data from multiple sources?
Response:

Answer: C


NEW QUESTION # 138
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

Answer: D


NEW QUESTION # 139
......

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