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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

NEW QUESTION # 34
Which refinements are typically used to enhance clarity and presentation quality in visualizations?
(Choose two)

Answer: C,D


NEW QUESTION # 35
Which two benefits are most closely associated with using object-oriented programming (OOP) in data analysis pipelines?
(Choose two)

Answer: A,B


NEW QUESTION # 36
What is a key advantage of using the Parquet file format over CSV in large-scale data pipelines?

Answer: B


NEW QUESTION # 37
How would you extract the last three rows of a DataFrame df using position-based indexing?

Answer: A


NEW QUESTION # 38
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

Answer: D


NEW QUESTION # 39
......

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