Passing PCAD-31-02 Score | PCAD-31-02 Latest Exam Format

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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

>> Passing PCAD-31-02 Score <<

PCAD-31-02 Latest Exam Format | PCAD-31-02 Simulations Pdf

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q21-Q26):

NEW QUESTION # 21
When fetching DATETIME values from a SQL database into Python, which Python data type is most appropriate for storing and manipulating these values?

Answer: D


NEW QUESTION # 22
Why might using a pie chart to represent category proportions be less effective than a bar chart?

Answer: D


NEW QUESTION # 23
What is the primary advantage of using web scraping as a data collection method in business analytics?

Answer: B


NEW QUESTION # 24
Which operations are valid and commonly used on Python lists during data transformation tasks?
(Choose two)

Answer: A,B


NEW QUESTION # 25
Which practices contribute to better exception handling in data pipelines involving file I/O and numerical computations?
(Choose two)

Answer: B,D


NEW QUESTION # 26
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