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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q73-Q78):

NEW QUESTION # 73
What is the purpose of using the groupby() function in a Pandas DataFrame when analyzing a dataset with multiple categories and numerical values?

Answer: A


NEW QUESTION # 74
Which transformation method adjusts the mean of the dataset to 0 and the standard deviation to 1?

Answer: A


NEW QUESTION # 75
Which of the following is the most suitable data structure in Python for representing a tabular row with column names?

Answer: B


NEW QUESTION # 76
What is the primary difference between the == operator and the is operator in Python when used in a data pipeline?

Answer: D


NEW QUESTION # 77
Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

Answer: C,D


NEW QUESTION # 78
......

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