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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q62-Q67):

NEW QUESTION # 62
Which practices are considered part of professional Python scripting standards?
(Choose two)

Answer: A,B


NEW QUESTION # 63
Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

Answer: B


NEW QUESTION # 64
Which method in a Python class is responsible for initializing the object's attributes at the time of creation?

Answer: C


NEW QUESTION # 65
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

Answer: D


NEW QUESTION # 66
Which metric is most appropriate for evaluating the accuracy of a classification model?

Answer: C


NEW QUESTION # 67
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