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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q102-Q107):

NEW QUESTION # 102
Why is it important to manage object identity correctly in complex data analysis pipelines?

Answer: A


NEW QUESTION # 103
Which of the following improves the readability of a spreadsheet used for collaborative data analysis?

Answer: A


NEW QUESTION # 104
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

Answer: B,C


NEW QUESTION # 105
Which file formats are commonly used for hierarchical or nested data structures in data acquisition?
(Choose two)

Answer: C,D


NEW QUESTION # 106
Which technique would be most appropriate to handle missing numerical values in a dataset intended for machine learning?

Answer: B


NEW QUESTION # 107
......

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