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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Latest Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) dumps pdf, PCAD-31-02 valid torrent

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q29-Q34):

NEW QUESTION # 29
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 # 30
Which method allows you to detect and remove rows with duplicate values across all columns in a Pandas DataFrame?

Answer: C


NEW QUESTION # 31
When analyzing a dataset of customer ages, you calculate the mean, median, and mode. What does it indicate if all three values are approximately equal?

Answer: A


NEW QUESTION # 32
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: D


NEW QUESTION # 33
What object-oriented principle allows a child class to reuse and extend the behavior of a parent class in a Python data modeling scenario?

Answer: A


NEW QUESTION # 34
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