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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q61-Q66):

NEW QUESTION # 61
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

Answer: C


NEW QUESTION # 62
What is the primary role of the pandas library in the Python data science ecosystem?

Answer: B


NEW QUESTION # 63
Why might using a pie chart to represent category proportions be less effective than a bar chart?

Answer: A


NEW QUESTION # 64
Which SQL commands can be used to modify the contents of a table's data directly?
(Choose all that apply)

Answer: B,C


NEW QUESTION # 65
What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

Answer: D


NEW QUESTION # 66
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