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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Working with Data Using Python Libraries30%- Pandas Library
  • 1. Series and DataFrame structures
  • 2. DataFrame operations (merge, join, concat)
  • 3. Handling missing data
  • 4. GroupBy operations
  • 5. Data selection and filtering
- NumPy Fundamentals
  • 1. NumPy arrays and operations
  • 2. Vectorized operations
  • 3. Array indexing and slicing
  • 4. Basic statistical functions
- Data Visualization
  • 1. Matplotlib basics
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Seaborn introduction
  • 4. Customizing plots
Applied Data Analysis Projects20%- Exploratory Data Analysis (EDA)
  • 1. Data distribution analysis
  • 2. Correlation analysis
  • 3. Descriptive statistics computation
  • 4. Pattern identification
- Data Analysis Workflow
  • 1. Data exploration and cleaning
  • 2. Analysis and modeling
  • 3. Results interpretation and presentation
  • 4. Problem definition
Data Analysis Fundamentals20%- 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. Data analysis concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
Python Programming for Data Analysis30%- File Operations
  • 1. Context managers (with statement)
  • 2. Reading from files (text, CSV)
  • 3. Writing to files
- Python Data Types and Structures
  • 1. Data type conversions
  • 2. Numbers, strings, booleans
  • 3. Lists, tuples, dictionaries, sets
- Control Flow and Functions
  • 1. Loops (for, while)
  • 2. Return values and scope
  • 3. Conditional statements (if, elif, else)
  • 4. Function definitions and parameters

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q34-Q39):

NEW QUESTION # 34
When joining two tables in a relational database using SQL, which clause is used to specify the relationship between their columns?

Answer: A


NEW QUESTION # 35
Which actions are considered best practices when managing datasets in Python using pandas?
(Choose two)

Answer: A,C


NEW QUESTION # 36
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

Answer: B


NEW QUESTION # 37
Why is it important to manage object identity correctly in complex data analysis pipelines?

Answer: A


NEW QUESTION # 38
Which chart types are commonly used to represent relationships between two numerical variables in Python-based visualizations?
(choose two)
Response:

Answer: B,D


NEW QUESTION # 39
......

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