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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q98-Q103):

NEW QUESTION # 98
Which of the following best describes the purpose of the plt.subplot() function in Matplotlib?

Answer: C


NEW QUESTION # 99
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

Answer: B


NEW QUESTION # 100
Which actions are considered part of basic data validation tasks in Python using Pandas?
(Choose two)

Answer: A,B


NEW QUESTION # 101
What is the key difference between the .iloc[] and .loc[] accessors when working with Series or DataFrames?

Answer: B


NEW QUESTION # 102
Which approaches help tailor data visualizations for non-technical stakeholders?
(Choose two)

Answer: B,C


NEW QUESTION # 103
......

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