Reliable Python Institute PCAD-31-02 Exam Pattern & Pdf PCAD-31-02 Free

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Python Institute PCAD-31-02 Exam Syllabus Topics:

SectionWeightObjectives
Python Programming for Data Analysis30%- Python Data Types and Structures
  • 1. Lists, tuples, dictionaries, sets
  • 2. Data type conversions
  • 3. Numbers, strings, booleans
- File Operations
  • 1. Context managers (with statement)
  • 2. Reading from files (text, CSV)
  • 3. Writing to files
- Control Flow and Functions
  • 1. Return values and scope
  • 2. Loops (for, while)
  • 3. Function definitions and parameters
  • 4. Conditional statements (if, elif, else)
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. Problem definition
  • 3. Results interpretation and presentation
  • 4. Analysis and modeling
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 concepts and terminology
  • 2. Types of data (structured, unstructured, semi-structured)
  • 3. Data analysis process lifecycle
Working with Data Using Python Libraries30%- Pandas Library
  • 1. DataFrame operations (merge, join, concat)
  • 2. GroupBy operations
  • 3. Handling missing data
  • 4. Series and DataFrame structures
  • 5. Data selection and filtering
- Data Visualization
  • 1. Seaborn introduction
  • 2. Creating basic charts (line, bar, scatter, histogram)
  • 3. Matplotlib basics
  • 4. Customizing plots
- 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 (Q30-Q35):

NEW QUESTION # 30
Which type of regression is most appropriate when the response variable is categorical, such as predicting customer churn (Yes/No)?

Answer: B


NEW QUESTION # 31
Which techniques are commonly used to manage type conversion between SQL and Python when importing database values?
(Choose two)

Answer: A,D


NEW QUESTION # 32
Which operations are recommended when organizing messy tabular data in Pandas for further transformation and statistical modeling?
(choose two)

Answer: B,D


NEW QUESTION # 33
What is the correct sequence to connect to a database and execute a query using the sqlite3 module in Python?

Answer: A


NEW QUESTION # 34
Which element is essential to justify a conclusion drawn from a dataset?

Answer: C


NEW QUESTION # 35
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