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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q133-Q138):

NEW QUESTION # 133
What is the purpose of using df.loc[2:5, 'column1'] in a Pandas DataFrame?

Answer: C


NEW QUESTION # 134
Which methods are typically used to assess relationships between variables in exploratory data analysis?
(Choose two)

Answer: A,D


NEW QUESTION # 135
You have a list of product prices, and you need to remove duplicate values while maintaining the fastest performance. Which core data structure should you use?

Answer: B


NEW QUESTION # 136
What is a best practice when organizing data in a spreadsheet for analysis?

Answer: A


NEW QUESTION # 137
What is a major challenge in aggregating data from multiple sources?
Response:

Answer: A


NEW QUESTION # 138
......

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