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Python Institute PCAD-31-02 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Topic 1: Applied Data Analysis Projects | 20% | - Exploratory Data Analysis (EDA)
- 1. Pattern identification
- 2. Correlation analysis
- 3. Data distribution analysis
- 4. Descriptive statistics computation
- Data Analysis Workflow
- 1. Problem definition
- 2. Analysis and modeling
- 3. Results interpretation and presentation
- 4. Data exploration and cleaning
|
| 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 concepts and terminology
- 3. Data analysis process lifecycle
|
| Topic 3: Working with Data Using Python Libraries | 30% | - Pandas Library
- 1. Data selection and filtering
- 2. Handling missing data
- 3. GroupBy operations
- 4. DataFrame operations (merge, join, concat)
- 5. Series and DataFrame structures
- Data Visualization
- 1. Customizing plots
- 2. Seaborn introduction
- 3. Creating basic charts (line, bar, scatter, histogram)
- 4. Matplotlib basics
- NumPy Fundamentals
- 1. Vectorized operations
- 2. Basic statistical functions
- 3. Array indexing and slicing
- 4. NumPy arrays and operations
|
| Topic 4: Python Programming for Data Analysis | 30% | - File Operations
- 1. Writing to files
- 2. Reading from files (text, CSV)
- 3. Context managers (with statement)
- Control Flow and Functions
- 1. Conditional statements (if, elif, else)
- 2. Return values and scope
- 3. Function definitions and parameters
- 4. Loops (for, while)
- Python Data Types and Structures
- 1. Numbers, strings, booleans
- 2. Lists, tuples, dictionaries, sets
- 3. Data type conversions
|
Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions:
Question 1
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)
A. Avoiding color entirely
B. Disabling grid lines in all cases
C. Adding descriptive axis labels
D. Customizing tick labels
Question 2
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?
A. It overlays multiple plots in the same figure without axes
B. It links Matplotlib with Seaborn's grid styling
C. It configures multiple plots in a grid layout within a single figure
D. It creates a 3D surface plot using pandas
Question 3
Which of the following are true characteristics of bootstrapping in statistics?
(Choose two)
A. It assumes a normal distribution
B. It requires a large population sample
C. It uses random sampling with replacement
D. It enables confidence interval estimation
Question 4
What is the primary purpose of using a requirements.txt file in Python-based data projects?
A. To list the packages and versions required for consistent environment setup
B. To store the current working directory of Python modules
C. To track script runtime dependencies during execution
D. To automatically compress source code for deployment
Question 5
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?
A. Decision tree regression
B. Logistic regression
C. Polynomial regression
D. Linear regression
Solutions:
Question 1 Answer: C,D | Question 2 Answer: C | Question 3 Answer: C,D | Question 4 Answer: A | Question 5 Answer: B |