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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Python Programming for Data Analysis | 30% | - File Operations
- 1. Writing to files
- 2. Context managers (with statement)
- 3. Reading from files (text, CSV)
- Control Flow and Functions
- 1. Loops (for, while)
- 2. Conditional statements (if, elif, else)
- 3. Return values and scope
- 4. Function definitions and parameters
- Python Data Types and Structures
- 1. Numbers, strings, booleans
- 2. Lists, tuples, dictionaries, sets
- 3. Data type conversions
|
| Topic 2: Data Analysis Fundamentals | 20% | - Data Collection and Preparation
- 1. Data cleaning and preprocessing basics
- 2. Data import/export operations
- 3. Data sources and acquisition methods
- Introduction to Data Analysis
- 1. Types of data (structured, unstructured, semi-structured)
- 2. Data analysis process lifecycle
- 3. Data analysis concepts and terminology
|
| Topic 3: Working with Data Using Python Libraries | 30% | - Data Visualization
- 1. Customizing plots
- 2. Creating basic charts (line, bar, scatter, histogram)
- 3. Matplotlib basics
- 4. Seaborn introduction
- NumPy Fundamentals
- 1. Vectorized operations
- 2. Basic statistical functions
- 3. NumPy arrays and operations
- 4. Array indexing and slicing
- Pandas Library
- 1. Data selection and filtering
- 2. DataFrame operations (merge, join, concat)
- 3. GroupBy operations
- 4. Handling missing data
- 5. Series and DataFrame structures
|
| Topic 4: Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
- 1. Pattern identification
- 2. Data distribution analysis
- 3. Descriptive statistics computation
- 4. Correlation analysis
- Data Analysis Workflow
- 1. Data exploration and cleaning
- 2. Problem definition
- 3. Results interpretation and presentation
- 4. Analysis and modeling
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
1. Which best practices help enhance database security when building Python data analysis pipelines?
(Choose two)
A) Sanitize input by replacing dangerous characters with asterisks
B) Store credentials securely using environment variables
C) Limit database user permissions to only necessary operations
D) Construct queries using f-strings for readability
2. What is the recommended Python library to connect and extract tabular data directly from an Excel file?
A) csv
B) matplotlib
C) sqlite3
D) openpyxl
3. Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)
A) Customizing tick labels
B) Disabling grid lines in all cases
C) Avoiding color entirely
D) Adding descriptive axis labels
4. Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?
A) Imputation using mean or median
B) Dropping all columns
C) Replacing with NULL
D) Filling with random values
5. What is the main purpose of defining a class when working with complex data structures in a Python-based analysis project?
A) To avoid using loops and conditional logic
B) To ensure all values are stored as plain strings
C) To encapsulate related data and behavior for better organization
D) To define global variables that persist across files
Solutions:
Question # 1 Answer: B,C | Question # 2 Answer: D | Question # 3 Answer: A,D | Question # 4 Answer: A | Question # 5 Answer: C |