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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

1. Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

A) cursor.execute("SELECT * FROM users WHERE id = ?", user_id)
B) cursor.execute("SELECT * FROM users WHERE id = ?", (user_id,))
C) cursor.execute("SELECT * FROM users WHERE id = " + user_id)
D) cursor.execute("SELECT * FROM users WHERE id = ':id'", {'id': user_id})


2. What is a major challenge in aggregating data from multiple sources?
Response:

A) Duplicate records and format inconsistencies
B) Increased algorithmic complexity
C) Excessive disk space usage
D) Overfitting in predictive models


3. Which function is used to reshape a NumPy array without modifying the original data values?

A) resize()
B) reshape()
C) transpose()
D) flatten()


4. What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

A) Parquet offers row-based compression
B) Parquet stores data in plain text, making it human-readable
C) Parquet supports schema evolution and columnar storage
D) Parquet files can only be used with Excel


5. What is the main reason to remove duplicate records before performing analysis?

A) It helps increase sample size
B) It avoids converting data to JSON format
C) It ensures training data is intentionally overfitted
D) It reduces redundancy that can skew statistical metrics


Solutions:

Question # 1
Answer: B
Question # 2
Answer: A
Question # 3
Answer: B
Question # 4
Answer: C
Question # 5
Answer: D

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