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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute PCAD-31-02 Exam Questions - Choice Of Certified Professionals [2026]

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q37-Q42):

NEW QUESTION # 37
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

Answer: D


NEW QUESTION # 38
Which Python constructs are useful for comparing and managing object identity in a data pipeline?
(Choose all that apply)

Answer: A,C


NEW QUESTION # 39
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)

Answer: B,D


NEW QUESTION # 40
Which of the following correctly demonstrates the use of parameterized queries using Python's sqlite3 module?

Answer: A


NEW QUESTION # 41
Which metric is most appropriate for evaluating the accuracy of a classification model?

Answer: D


NEW QUESTION # 42
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