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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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Python Institute PCAD-31-02 Exam Questions With PDF File Format

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Python Institute Certified Associate Data Analyst with Python (PCAD-31-02) Sample Questions (Q47-Q52):

NEW QUESTION # 47
When merging two Pandas DataFrames using a common key, which parameters are essential to ensure accurate and conflict-free results?
(Choose two)

Answer: A,C


NEW QUESTION # 48
Which method is best suited to transform inconsistent entries such as "N/A", "missing", and empty strings into a standard missing value representation in a Pandas DataFrame?

Answer: A


NEW QUESTION # 49
What is the primary role of the pandas library in the Python data science ecosystem?

Answer: C


NEW QUESTION # 50
Which of the following practices improves clarity when combining visualizations and textual explanations in a report?

Answer: A


NEW QUESTION # 51
Which SQL commands are typically used to retrieve and manipulate data in a data analysis context?
(Choose all that apply)
Response:

Answer: A,B


NEW QUESTION # 52
......

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