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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which actions are valid techniques for handling erroneous categorical values in a dataset?
(Choose two)

Answer: B,D


NEW QUESTION # 86
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: D


NEW QUESTION # 87
When analyzing a sales dataset using Pandas, which of the following techniques help extract key insights by grouping and summarizing the data?
(Choose two)

Answer: B,C


NEW QUESTION # 88
What is the main reason for using try-except blocks in a data analysis script that reads multiple files?

Answer: D


NEW QUESTION # 89
Which command should a data analyst use to install the pandas library in a Python environment using the Python Package Index?

Answer: A


NEW QUESTION # 90
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