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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q51-Q56):

NEW QUESTION # 51
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

Answer: B


NEW QUESTION # 52
What does a Pearson correlation coefficient value of -0.9 imply about the relationship between two numerical variables?

Answer: B


NEW QUESTION # 53
Which file format is most suitable for exchanging large tabular datasets with consistent column data types across systems?

Answer: C


NEW QUESTION # 54
Which practices help ensure secure and effective execution of SQL queries in Python scripts?
(Choose all that apply)

Answer: B,C


NEW QUESTION # 55
Which practices are typically part of the data integration process?
(Choose two)

Answer: A,D


NEW QUESTION # 56
......

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