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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q41-Q46):

NEW QUESTION # 41
Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

Answer: C


NEW QUESTION # 42
What is the function of the melt() operation in Pandas when reshaping wide-format data?

Answer: D


NEW QUESTION # 43
What is the primary reason for using parameterized queries instead of directly formatting SQL strings in Python?

Answer: B


NEW QUESTION # 44
Which Python library is most commonly used to establish a connection to a SQLite database and perform SQL operations?

Answer: B


NEW QUESTION # 45
What is the main advantage of using Seaborn over Matplotlib when creating complex statistical visualizations in Python?

Answer: A


NEW QUESTION # 46
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