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Python Institute PCAD-31-02 Exam Syllabus Topics:

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

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

Which approaches are commonly used to extract structured data from APIs in Python?
(Choose two)

  • A. Load CSV directly with re.compile()
  • B. Open Excel files using matplotlib
  • C. Access data using requests.get() and parse it with .json()
  • D. Use the requests module to retrieve JSON responses
Reveal Solution  Discussion  0

Correct Answer: C,D  🗳️

Which of the following libraries is specifically optimized for high-performance numerical computation with multi-dimensional arrays in Python?

  • A. pandas
  • B. matplotlib
  • C. NumPy
  • D. seaborn
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Which of the following exceptions would most likely be raised if a string is directly converted to an integer in Python?

  • A. RuntimeError
  • B. IOError
  • C. ValueError
  • D. TypeError
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Which method is commonly used to handle missing numerical data in a dataset?

  • A. Replacing with text labels
  • B. Multiplying all missing values by zero
  • C. Filling with the mean or median
  • D. Dropping all columns
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Which method is appropriate for combining two DataFrames based on a shared key column where non-matching rows should be excluded from the result?

  • A. concat()
  • B. join(..., how='outer')
  • C. append()
  • D. merge(..., how='inner')
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

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